☆ 4.6 Review

Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers

DIAGNOSTICS (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Tumor glycolytic heterogeneity improves detection of regional nodal metastasis in patients with lung adenocarcinoma

Kun-Han Lue et al.

Summary: In this study, the diagnostic value of F-18-FDG PET-derived features for predicting pathological lymph node metastasis in lung adenocarcinoma patients was evaluated. The primary tumor entropy and visual interpretation of regional nodes via F-18-FDG PET were found to independently predict pathological regional lymph node metastasis.

ANNALS OF NUCLEAR MEDICINE (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Prediction of HER2 expression in breast cancer by combining PET/CT radiomic analysis and machine learning

Yiwen Chen et al.

Summary: This study evaluated the role of radiomics and machine learning based on PET/CT images in predicting HER2 status in breast cancer patients. The XGBoost model outperformed other machine learning models, with the use of PET/CT fusion features showing significant improvement in predicting HER2 status. The machine learning classifier based on PET/CT radiomic features has potential for predicting HER2 status in breast cancer.

ANNALS OF NUCLEAR MEDICINE (2022)

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Article Oncology

Overall Survival Prognostic Modelling of Non-small Cell Lung Cancer Patients Using Positron Emission Tomography/Computed Tomography Harmonised Radiomics Features: The Quest for the Optimal Machine Learning Algorithm

Mehdi Amini et al.

Summary: Radiomics prognostic models have shown promising results in various clinical applications. However, there are still challenges to be addressed, such as information limitation and selecting the optimal machine learning and feature selection methods. In this study, different feature selection and machine learning methods were applied to PET, CT, and PET/CT fusion to identify the best combinations for predicting overall survival in non-small cell lung cancer patients.

CLINICAL ONCOLOGY (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Quantitative classification and radiomics of [18F]FDG-PET/CT in indeterminate thyroid nodules

Elizabeth J. de Koster et al.

Summary: The study aimed to evaluate whether quantitative [F-18]FDG-PET/CT assessment and radiomic analysis of [F-18]FDG-positive thyroid nodules could improve the preoperative differentiation of indeterminate thyroid nodules. The results showed that applying distinct SUV cut-offs for non-Hurthle cell and Hurthle cell nodules can optimize the ability to rule out malignancy, while radiomic analysis did not provide additional differentiation for [F-18]FDG-positive nodules.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2022)

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Article Oncology

Prognostic Value of Metabolic, Volumetric and Textural Parameters of Baseline [F-18]FDG PET/CT in Early Triple-Negative Breast Cancer

Clement Bouron et al.

Summary: The aim of this study was to evaluate PET/CT parameters for determining different prognostic groups in triple-negative breast cancer (TNBC). The study found that MTV, TLG, and entropy could be of interest in predicting the prognostic outcome of TNBC patients. Textural features associated with metabolic and volumetric parameters of baseline [F-18]FDG PET/CT have a prognostic value for identifying high-relapse-risk groups in early TNBC patients.

CANCERS (2022)

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Article Oncology

Imbalanced Data Correction Based PET/CT Radiomics Model for Predicting Lymph Node Metastasis in Clinical Stage T1 Lung Adenocarcinoma

Jieqin Lv et al.

Summary: The study aimed to develop and validate an imbalanced data correction method based on the PET/CT radiomics model for predicting lymph node metastasis in clinical stage T1 lung adenocarcinoma. By selecting features and resampling data, a combined model was constructed, which demonstrated strong prediction performance.

FRONTIERS IN ONCOLOGY (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

The Role of Histogram-Based Textural Analysis of 18F-FDG PET/CT in Evaluating Tumor Heterogeneity and Predicting the Prognosis of Invasive Lung Adenocarcinoma

Hasan Onner et al.

Summary: This study investigated the role of histogram-based textural features (HBTFs) extracted from F-18-FDG PET/CT in tumoral heterogeneity (TH) evaluation and prognosis prediction of invasive lung adenocarcinoma (ILA). The results showed that HBTFs were associated with clinicopathological factors causing TH and some F-18-FDG PET/CT parameters were associated with lymph node involvement. Therefore, TH measurement with HBTFs may contribute to precision medicine for ILA.

MOLECULAR IMAGING AND RADIONUCLIDE THERAPY (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Can Radiomics Analyses in 18F-FDG PET/CT Images of Primary Breast Carcinoma Predict Hormone Receptor Status?

Mine Araz et al.

Summary: This study found that preoperative F-18-FDG PET/CT radiomics features cannot predict hormone receptor positivity in primary breast cancer. Only SUV metrics and TLG were statistically significant in predicting hormone receptor status.

MOLECULAR IMAGING AND RADIONUCLIDE THERAPY (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis

V. Romeo et al.

Summary: The study aimed to evaluate whether a radiomics and machine learning model utilizing quantitative parameters and radiomics features extracted from simultaneous multiparametric F-18-FDG PET/MRI images can effectively distinguish between benign and malignant breast lesions.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

18F-FDOPA PET for the Noninvasive Prediction of Glioma Molecular Parameters: A Radiomics Study

Timothee Zaragori et al.

Summary: The study shows that F-18-FDOPA PET imaging can effectively predict IDH mutations and 1p/19q codeletion in gliomas by combining static and dynamic features. Dynamic features are found to be more important for predicting IDH mutations, while other radiomics features are more useful for predicting 1p/19q codeletion.

JOURNAL OF NUCLEAR MEDICINE (2022)

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Article Endocrinology & Metabolism

Radiomics analysis improves 18FDG PET/CT-based risk stratification of cytologically indeterminate thyroid nodules

Luca Giovanella et al.

Summary: The study evaluated the ability of PET metrics and radiomics features to predict final diagnosis of (18)FDG-avid cytologically indeterminate thyroid nodules. Results showed that two radiomics features (shape_Sphericity and glcm_Autocorrelation) can differentiate malignant from benign lesions. A predictive model integrating radiomics score and cytology classes effectively stratified the risk of malignancy.

ENDOCRINE (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Radiogenomic Models Using Machine Learning Techniques to Predict EGFR Mutations in Non-Small Cell Lung Cancer

Jay Kumar Raghavan Nair et al.

Summary: The study established radiogenomic models using texture features from FDG PET-CT and CT images to identify NSCLC tumors with EGFR mutations. The models showed promising results in predicting EGFR mutant status, demonstrating the potential value of imaging signatures in pretreatment assessment and prognosis for precision therapy.

CANADIAN ASSOCIATION OF RADIOLOGISTS JOURNAL-JOURNAL DE L ASSOCIATION CANADIENNE DES RADIOLOGISTES (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Additional value of metabolic parameters to PET/CT-based radiomics nomogram in predicting lymphovascular invasion and outcome in lung adenocarcinoma

Pei Nie et al.

Summary: This study demonstrates that the integration of radiomics techniques and clinical parameters can predict LVI in LAC effectively, and SUVmax is closely associated with the prognosis of LAC.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Value of18F-FDG PET/CT radiomic features to distinguish solitary lung adenocarcinoma from tuberculosis

Yujing Hu et al.

Summary: A predictive model was developed using (18)F-FDG PET/CT radiomic features to distinguish solitary lung adenocarcinoma from tuberculosis. The model showed significant value in differentiating between the two diseases with high performance in both training and validation cohorts.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Histologic subtype classification of non-small cell lung cancer using PET/CT images

Yong Han et al.

Summary: This study evaluated the capability of PET/CT images for differentiating histologic subtypes of NSCLC and identified the optimal model from radiomics-based machine learning/deep learning algorithms. Linear discriminant analysis and support vector machine coupled with feature selection method achieved optimal performance for the classification of ADC and SCC. The VGG16 deep learning algorithm outperformed conventional machine learning methods in combination with radiomics for the differential diagnosis of NSCLC subtypes.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Machine learning-based FDG PET-CT radiomics for outcome prediction in larynx and hypopharynx squamous cell carcinoma

J. Zhong et al.

Summary: Machine learning-based radiomic feature analysis of baseline PET-CT data might predict early disease progression in patients with locally advanced larynx and hypopharynx SCC.

CLINICAL RADIOLOGY (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Machine learning based on clinico-biological features integrated18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung

Caiyue Ren et al.

Summary: This study developed a nomogram via machine learning to noninvasively differentiate between adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer, assisting in clinical decision making for precision treatment.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2021)

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Article Endocrinology & Metabolism

Radiomics Analysis of [18F]-Fluorodeoxyglucose-Avid Thyroid Incidentalomas Improves Risk Stratification and Selection for Clinical Assessment

Luca Ceriani et al.

Summary: This study evaluated the ability of PET metrics and radiomics features to predict final diagnosis of [F-18]FDG thyroid incidentalomas (TIs). TLG, SUVmax, and shape_Sphericity were found to be the best predictors, with multiple parameters together showing stronger predictive abilities. The findings provide useful information for the management of TIs.

THYROID (2021)

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Article Biochemical Research Methods

Potentials and caveats of AI in hybrid imaging

Lalith Kumar Shiyam Sundar et al.

Summary: State-of-the-art patient management requires investigating both the anatomy and physiology of patients, with hybrid imaging techniques providing both structural and functional information. Artificial intelligence algorithms show promise in facilitating analysis of multi-parametric data in medical imaging, addressing challenges in extracting clinical information from large sets of multi-dimensional imaging data.

METHODS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Integration of PET/CT Radiomics and Semantic Features for Differentiation between Active Pulmonary Tuberculosis and Lung Cancer

Dongyang Du et al.

Summary: This study successfully differentiated between active pulmonary tuberculosis and lung cancer by combining PET/CT radiomics features with semantic features, showing improvement in diagnostic accuracy.

MOLECULAR IMAGING AND BIOLOGY (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Is FDG-PET texture analysis related to intratumor biological heterogeneity in lung cancer?

Manuel Pineiro-Fiel et al.

Summary: The study revealed strong correlations between tumor volume and F-18-FDG-PET texture indices in lung cancer, suggesting that texture indices might not provide independent information apart from being driven by their correlation with tumor volume, and may not be suitable as intratumor heterogeneity markers.

EUROPEAN RADIOLOGY (2021)

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Article Dentistry, Oral Surgery & Medicine

Texture Analysis Using Preoperative Positron Emission Tomography Images May Predict the Prognosis of Patients With Resectable Oral Squamous Cell Carcinoma

Masashi Kimura et al.

Summary: This study aimed to assess the potential prognostic values of textural features extracted from preoperative F-18-fluorodeoxyglucose positron emission tomography images in patients with resectable oral squamous cell carcinoma. The results showed that entropy is a statistically significant prognostic factor of both OS and DFS. Texture analysis using preoperative positron emission tomography images may contribute to risk stratification in patients with oral squamous cell carcinoma.

JOURNAL OF ORAL AND MAXILLOFACIAL SURGERY (2021)

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Article Oncology

Prediction of post-radiotherapy locoregional progression in HPV-associated oropharyngeal squamous cell carcinoma using machine-learning analysis of baseline PET/CT radiomics

Stefan P. Haider et al.

Summary: Novel radiomic models based on PET/CT imaging features can effectively predict locoregional progression in HPV-associated OPSCC patients, enabling risk stratification for personalized therapy planning.

TRANSLATIONAL ONCOLOGY (2021)

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Article Oncology

A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer

Bong-Il Song

Summary: The study developed and validated a machine learning-based radiomics model for predicting ALN metastasis in IDC patients, showing good sensitivity in predicting ALN metastasis and assisting in preoperatively predicting ALN status in patients.

BREAST CANCER (2021)

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Article Oncology

Stage-Specific PET Radiomic Prediction Model for the Histological Subtype Classification of Non-Small-Cell Lung Cancer

Yanlei Ji et al.

Summary: This study investigated the impact of staging on differences in glucose metabolic heterogeneity between lung adenocarcinoma and squamous cell carcinoma through PET textural analysis and developed a stage-specific PET radiomic prediction model to distinguish these two types of cancer. The results showed that the radiomic signature had good performance in distinguishing lung ADC and SCC, and the radiomic-clinical nomogram integrating radiomic features with clinical predictors exhibited even better discriminative performance.

CANCER MANAGEMENT AND RESEARCH (2021)

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Article Oncology

Hormonal Receptor Immunochemistry Heterogeneity and 18F-FDG Metabolic Heterogeneity: Preliminary Results of Their Relationship and Prognostic Value in Luminal Non-Metastatic Breast Cancers

Nicolas Aide et al.

Summary: In luminal non-metastatic breast tumors, the heterogeneity of ER and PR expressions was found to be correlated with F-18-FDG PET histogram parameters and gray-level co-occurrence matrix parameters. Random forest classification identified age, clinical stage, SUVmax, skewness (ER), kurtosis (ER), entropy (HISTO), and uniformity (HISTO) as important variables for predicting 8-year EFS. Skewness (ER) was identified as a predictor of 8-year EFS.

FRONTIERS IN ONCOLOGY (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

18F-FDG PET-based radiomics model for predicting occult lymph node metastasis in clinical N0 solid lung adenocarcinoma

Lili Wang et al.

Summary: This study developed a radiomics model based on PET images for predicting lymph node metastasis in solid lung adenocarcinoma patients. The model demonstrated good predictive accuracy and clinical utility in preoperative prediction of occult lymph node metastasis.

QUANTITATIVE IMAGING IN MEDICINE AND SURGERY (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Prognostic value of tumor metabolic imaging phenotype by FDG PET radiomics in HNSCC

Hyukjin Yoon et al.

Summary: The study evaluated the prognostic value of radiomics PET parameters in HNSCC patients, finding that the metabolic heterogeneity parameter GLNU(GLZLM) may assist clinicians in patient risk assessment as a feasible prognostic factor.

ANNALS OF NUCLEAR MEDICINE (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Prediction of local recurrence and distant metastasis using radiomics analysis of pretreatment nasopharyngeal [18F]FDG PET/CT images

Lihong Peng et al.

Summary: This study aimed to predict locoregional recurrence and distant metastasis in locally advanced nasopharyngeal carcinoma patients using radiomics analysis of pretreatment [18F]FDG PET/CT images. A radiomics signature consisting of 5 features was derived with a high AUC value of 0.8290 for predicting treatment outcomes.

ANNALS OF NUCLEAR MEDICINE (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach

Yi Zhou et al.

Summary: This study demonstrated the effectiveness of combining F-18-FDG PET/CT radiomics features with machine learning methods to differentiate between primary and metastatic lung lesions and classify histological subtypes. Gradient boosting decision tree (GBDT) and random forest (RF) were identified as the optimal classification methods for PET and CT datasets, respectively, with GBDT also considered the optimal feature selection method.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

A clinically practical radiomics-clinical combined model based on PET/CT data and nomogram predicts EGFR mutation in lung adenocarcinoma

Cheng Chang et al.

Summary: This study developed a predictive model for EGFR mutation in lung adenocarcinoma patients using radiomics signatures from PET/CT and clinical risk factors. The combined PET/CT radiomics-clinical model showed superior performance in predicting EGFR mutation compared to individual PET/CT radiomics or clinical models.

EUROPEAN RADIOLOGY (2021)

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Article Oncology

Multiblock Discriminant Analysis of Integrative 18F-FDG-PET/CT Radiomics for Predicting Circulating Tumor Cells in Early-Stage Non-small Cell Lung Cancer Treated With Stereotactic Body Radiation Therapy

Sang Ho Lee et al.

Summary: The study aimed to predict circulating tumor cells (CTCs) in early-stage non-small cell lung cancer (ES-NSCLC) patients using F-18-FDG-PET/CT radiomics and multiblock discriminant analysis. DIABLO achieved the best performance in predicting pre- and post-SBRT CTCs, outperforming single-block sPLS-DA models.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2021)

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Article Oncology

Integrating Multiomics Information in Deep Learning Architectures for Joint Actuarial Outcome Prediction in Non-Small Cell Lung Cancer Patients After Radiation Therapy

Sunan Cui et al.

Summary: The study introduced novel actuarial deep learning neural network architectures for joint prediction in stage III non-small cell lung cancer patients, integrating complex multiomics information and time-to-event data, outperforming traditional models in predicting radiation therapy outcomes like radiation pneumonitis and local control. The proposed models showed promising performance in model evaluation, testing, and external validation, highlighting the effectiveness of incorporating multiomics information in deep learning frameworks.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2021)

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Article Oncology

FET-PET radiomics in recurrent glioblastoma: prognostic value for outcome after re-irradiation?

Montserrat Carles et al.

Summary: The study evaluated the prognostic value of FET-PET radiomics in rGBM patients and found that radiomics features can effectively distinguish tumor and non-tumor tissues, with predictive value for time-to-progression, overall survival, and recurrence location.

RADIATION ONCOLOGY (2021)

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Article Oncology

FDG-PET Radiomics for Response Monitoring in Non-Small-Cell Lung Cancer Treated with Radiation Therapy

Montserrat Carles et al.

Summary: This study aims to identify clinically relevant image feature (IF) changes during chemoradiation in NSCLC patients. By utilizing IF variability observed across 4D PET scans as a patient-specific normalization factor, statistically relevant IF changes during treatment are emphasized. Changes in deltaAUCCSH within the primary tumor during treatment can discriminate patients with and without LR, and this could be recommended for monitoring treatment response in NSCLC patients.

CANCERS (2021)

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Article Oncology

Breast Tumor Characterization Using [18F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics

Denis Krajnc et al.

Summary: In this study, machine learning techniques were used to establish predictive models for breast cancer detection and characterization using [F-18]FDG-PET/CT images. The results showed high performance in differentiating between malignant and benign tumors, as well as in identifying the triple negative tumor subtype. The study also highlighted the importance of radiomic features and advanced data pre-processing methods in improving the accuracy of breast cancer diagnosis.

CANCERS (2021)

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Article Oncology

A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma

Cheng Chang et al.

Summary: This study developed a machine learning model that combines PET/CT radiomic features and clinical characteristics to predict the ALK rearrangement status in lung adenocarcinomas non-invasively. The model showed a significant advantage in predicting the ALK mutation status compared to the clinical model alone.

FRONTIERS IN ONCOLOGY (2021)

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Review Medicine, General & Internal

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Manuel Pineiro-Fiel et al.

Summary: PET radiomics shows promising prospects, but the insufficient number of patients and lack of in-depth validations in most studies highlight the need for standardization initiatives in the upcoming years.

DIAGNOSTICS (2021)

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Article Medicine, General & Internal

Preoperative Texture Analysis Using 11C-Methionine Positron Emission Tomography Predicts Survival after Surgery for Glioma

Osamu Manabe et al.

Summary: This study found that low gray-level run emphasis (LGRE) and correlation extracted from texture analysis of MET uptake were associated with prognosis in patients with glioma, suggesting their potential prognostic value.

DIAGNOSTICS (2021)

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Article Oncology

Radiomics predicts risk of cachexia in advanced NSCLC patients treated with immune checkpoint inhibitors

Wei Mu et al.

Summary: The study used radiomics analysis of PET/CT images to predict cachexia risk in NSCLC patients treated with ICI, and found that the radiomics signature could also help predict durable clinical benefit, progression-free survival, and overall survival following ICI treatment.

BRITISH JOURNAL OF CANCER (2021)

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Article Oncology

Prognostic Value of Computed Tomography and/or 18F-Fluorodeoxyglucose Positron Emission Tomography Radiomics Features in Locally Advanced Non-small Cell Lung Cancer

Angel Moran et al.

Summary: In patients with locally advanced non-small cell lung cancer, adding PET radiomics features to conventional factors significantly improves prognostic value, while adding CT features does not.

CLINICAL LUNG CANCER (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Prediction of the treatment outcome using machine learning with FDG-PET image-based multiparametric approach in patients with oral cavity squamous cell carcinoma

N. Fujima et al.

Summary: The study aimed to investigate the utility of machine learning-based multiparametric analysis on FDG-PET images for predicting treatment outcomes in patients with OCSCC. The results suggest that this approach has the potential to predict local control or failure in OCSCC patients compared to traditional FDG-PET parameters and staging methods.

CLINICAL RADIOLOGY (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer

Margarita Kirienko et al.

Summary: The radiogenomic data showed associations between PET and CT radiomic features, genetic variants, and gene expression with NSCLC histology. Machine learning successfully predicted histology and demonstrated moderate ability to predict patient relapse.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2021)

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Article Medicine, General & Internal

Role of 18F-FDG PET/CT Radiomics Features in the Differential Diagnosis of Solitary Pulmonary Nodules: Diagnostic Accuracy and Comparison between Two Different PET/CT Scanners

Domenico Albano et al.

Summary: This retrospective study investigated the predictive abilities of F-18-FDG-PET/CT metrics and RFs in determining the final diagnosis of solitary pulmonary nodules. Results showed that RFs played a significant role in predicting the diagnosis, with accuracies directly related to the scanner used.

JOURNAL OF CLINICAL MEDICINE (2021)

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Article Oncology

A Novel Approach Using FDG-PET/CT-Based Radiomics to Assess Tumor Immune Phenotypes in Patients With Non-Small Cell Lung Cancer

Jianyuan Zhou et al.

Summary: In this study, FDG-PET/CT-based radiomic features were utilized to predict TMIT-I tumors in NSCLC patients, showing good performance in discriminating between TMITs and providing a promising approach for immunotherapy selection in a clinical setting.

FRONTIERS IN ONCOLOGY (2021)

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Article Oncology

Identification of Stage IIIC/IV EGFR-Mutated Non-Small Cell Lung Cancer Populations Sensitive to Targeted Therapy Based on a PET/CT Radiomics Risk Model

Dan Shao et al.

Summary: The study aimed to construct an individualized PET/CT prognostic biomarker for EGFR-mutated NSCLC patients to quantify the progression risk and identify treatment-sensitive populations after EGFR-TKI therapy. Results showed higher Rad-scores were associated with worse PFS in training, internal and external validation cohorts, effectively identifying high-risk patients for rapid progression. The Rad-score can provide precise treatment options for NSCLC patients.

FRONTIERS IN ONCOLOGY (2021)

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Article Oncology

Habitat Imaging-Based 18F-FDG PET/CT Radiomics for the Preoperative Discrimination of Non-small Cell Lung Cancer and Benign Inflammatory Diseases

Ling Chen et al.

Summary: This study aimed to use habitat imaging-based F-18-FDG PET/CT radiomics to predict non-small cell lung cancer and benign inflammatory diseases. After feature selection, the habitat imaging-based radiomics models showed good discrimination performance, with significant improvement compared to conventional methods.

FRONTIERS IN ONCOLOGY (2021)

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Article Oncology

Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics

Jihui Li et al.

Summary: This study aimed to evaluate the expression of PD-L1 in NSCLC patients using radiomic features from F-18-FDG PET/CT images and clinicopathological characteristics. A prediction model combining radiomic signature and clinicopathologic features showed good predictive value in predicting PD-L1 expression levels above 1% and 50%.

FRONTIERS IN ONCOLOGY (2021)

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Article Oncology

Integrated CT Radiomics Features Could Enhance the Efficacy of 18F-FET PET for Non-Invasive Isocitrate Dehydrogenase Genotype Prediction in Adult Untreated Gliomas: A Retrospective Cohort Study

Weiyan Zhou et al.

Summary: The study investigated predictive models based on F-18-FET PET/CT radiomics features for identifying the IDH genotype in adult gliomas. Combining PET and CT radiomics features improved non-invasive prediction of IDH genotype in untreated gliomas, which is crucial for personalized treatment decision-making in clinical practice.

FRONTIERS IN ONCOLOGY (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers

Kyle J. Lafata et al.

Summary: This study demonstrates that intra-treatment radiomics data during radiation therapy for oropharyngeal cancer can serve as a prognostic factor, potentially guiding treatment strategies. The radiomic expression during treatment shows better prognostic advantages compared to baseline metabolic imaging characteristics and clinical features.

MEDICAL PHYSICS (2021)

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Article Biochemistry & Molecular Biology

Relevance of Dynamic 18F-DOPA PET Radiomics for Differentiation of High-Grade Glioma Progression from Treatment-Related Changes

Shamimeh Ahrari et al.

Summary: This study evaluated the relevance of F-18-DOPA PET static and dynamic radiomics for differentiation of high-grade glioma progression from treatment-related changes. The best radiomics models showed slightly better performances than the reference model based on TBR values.

BIOMEDICINES (2021)

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Article Health Care Sciences & Services

Clinical Significance of Peritumoral Adipose Tissue PET/CT Imaging Features for Predicting Axillary Lymph Node Metastasis in Patients with Breast Cancer

Jeong Won Lee et al.

Summary: This study found that textural parameters of peritumoral breast AT based on FDG PET/CT can predict axillary lymph node metastasis in breast cancer patients, with GLCM entropy showing the highest predictive value.

JOURNAL OF PERSONALIZED MEDICINE (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization

Panagiotis Papadimitroulas et al.

Summary: Radiomics and Deep Neural Networks (DNNs) have shown promising applications in medical image analysis for clinical practices in terms of classification and prediction. However, most current research is limited by dataset availability and generalizability. The study stresses the importance of multicenter recruitment of large datasets to enhance biomarker variability, establish the potential clinical value of radiomics, and develop robust explainable AI models.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic [18F]FET PET radiomics

Zhicong Li et al.

Summary: The study aimed to evaluate radiomic features extracted from dynamic [F-18]FET PET images for predicting TERTp-mutation status in patients with IDH-wildtype high-grade glioma, with the time-to-peak model showing the highest predictability.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2021)

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Article Multidisciplinary Sciences

Differentiation between non-small cell lung cancer and radiation pneumonitis after carbon-ion radiotherapy by 18F-FDG PET/CT texture analysis

Makito Suga et al.

Summary: The study aimed to evaluate the ability of F-18-FDG PET/CT metabolic parameters and its textural image features to differentiate NSCLC from RP, and confirmed that texture parameters can improve diagnostic accuracy.

SCIENTIFIC REPORTS (2021)

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Article Oncology

Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images

Wei Mu et al.

Summary: The study suggests that quantitative imaging can serve as an alternative to immunohistochemistry for predicting PD-L1 expression status in NSCLC patients treated with immune checkpoint inhibitors. The developed deeply learned score was able to accurately predict durable clinical benefit, progression-free survival, and overall survival in retrospective and prospective cohorts of patients. The findings support the use of this score as a surrogate for PD-L1 measurement in guiding individual treatment decisions.

JOURNAL FOR IMMUNOTHERAPY OF CANCER (2021)

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Article Oncology

Multiparametric Integrated 18F-FDG PET/MRI-Based Radiomics for Breast Cancer Phenotyping and Tumor Decoding

Lale Umutlu et al.

Summary: This study utilized simultaneous F-18-FDG PET/MRI as a platform for comprehensive radiomics analysis for breast cancer subtype, hormone receptor status, proliferation rate, and metastatic spread, demonstrating high accuracy in predicting these factors. The combination of multiparametric MRI and PET data provided the best results for differentiating between different breast cancer subtypes and predicting hormone receptor status, proliferation rate, and metastatic spread.

CANCERS (2021)

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Article Oncology

18F-FMISO-PET Hypoxia Monitoring for Head-and-Neck Cancer Patients: Radiomics Analyses Predict the Outcome of Chemo-Radiotherapy

Montserrat Carles et al.

Summary: This study monitored FMISO-hypoxia in head-and-neck cancer patients undergoing chemo-radiotherapy and aimed to develop a radiomics model for predicting treatment outcome. Tumor hypoxia is associated with radiation resistance and can be longitudinally monitored by F-18-fluoromisonidazole (F-18-FMISO)-PET/CT. The radiomics analysis of F-18-FMISO-derived hypoxia dynamics was able to predict outcome of HNSCC patients after CRT.

CANCERS (2021)

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Article Oncology

Development and Validation of a 18F-FDG PET-Based Radiomic Model for Evaluating Hypermetabolic Mediastinal-Hilar Lymph Nodes in Non-Small-Cell Lung Cancer

Ming-li Ouyang et al.

Summary: This study developed a PET radiomic model in combination with CT imaging features to differentiate LN metastasis in NSCLC patients, demonstrating good diagnostic efficacy.

FRONTIERS IN ONCOLOGY (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

A subregion-based positron emission tomography/computed tomography (PET/CT) radiomics model for the classification of non-small cell lung cancer histopathological subtypes

Hui Shen et al.

Summary: This study successfully classified lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC) using subregion radiomics features and SVM-RBF classifier. The optimized features were the interquartile range feature from subregion 2 of CT and the gender feature from clinical reports, leading to improved diagnostic performance.

QUANTITATIVE IMAGING IN MEDICINE AND SURGERY (2021)

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Article Medicine, General & Internal

Pre-Operative Prediction of Mediastinal Node Metastasis Using Radiomics Model Based on 18F-FDG PET/CT of the Primary Tumor in Non-Small Cell Lung Cancer Patients

Kai Zheng et al.

Summary: The radiomics model based on F-18-FDG PET/CT shows promise in predicting pathological mediastinal lymph node staging in patients with non-small cell lung cancer, suggesting its potential utility in guiding therapeutic planning.

FRONTIERS IN MEDICINE (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Prognostication Based on Texture Analysis of Baseline 18F Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Nonsmall-Cell Lung Carcinoma Patients Who Underwent Platinum-Based Chemotherapy as First-Line Treatment

Akshima Sharma et al.

Summary: The study demonstrated the potential for texture analysis using F-18 FDG PET/CT as an independent marker for predicting 1-year overall survival in NSCLC patients undergoing platinum-based chemotherapy. This method can be used to stratify patients who may not benefit from platinum-based chemotherapy and require alternative therapy options.

INDIAN JOURNAL OF NUCLEAR MEDICINE (2021)

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Article Oncology

Feasibility on the Use of Radiomics Features of 11[C]-MET PET/CT in Central Nervous System Tumours: Preliminary Results on Potential Grading Discrimination Using a Machine Learning Model

Giorgio Russo et al.

Summary: This study aims to evaluate the feasibility of using machine learning on 11[C]-MET PET/CT scan images to create a predictive model capable of discriminating between low-grade and high-grade CNS tumours. The proposed machine learning model, based on innovative segmentation and feature selection processes, showed good performance in predicting tumour grade. Further studies are needed to improve radiomics algorithms and support medical decision-making.

CURRENT ONCOLOGY (2021)

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Article Biochemistry & Molecular Biology

Molecular subtype classification of breast cancer using established radiomic signature models based on F-18-FDG PET/CT images

Jianjing Liu et al.

Summary: In molecular subtype classification of breast cancer, PET/CT derived radiomic signature models outperformed individual significant PET parameters.

FRONTIERS IN BIOSCIENCE-LANDMARK (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Pleomorphic carcinoma of the lung: Prognostic models of semantic, radiomics and combined features from CT and PET/CT in 85 patients

Chohee Kim et al.

Summary: This study investigated the prognostic value of semantic, radiomics, and combined risk models for pulmonary pleomorphic carcinomas (PCs). Results indicated that the combined risk model incorporating both semantic and radiomics features outperformed models using either feature type alone. The high SUVmax and histogram feature of energy were identified as significant predictors of poor prognosis in lung PCs.

EUROPEAN JOURNAL OF RADIOLOGY OPEN (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement

Ji Eun Park et al.

EUROPEAN RADIOLOGY (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Machine Learning Methods for Optimal Radiomics-Based Differentiation Between Recurrence and Inflammation: Application to Nasopharyngeal Carcinoma Post-therapy PET/CT Images

Dongyang Du et al.

MOLECULAR IMAGING AND BIOLOGY (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Biological correlates of tumor perfusion and its heterogeneity in newly diagnosed breast cancer using dynamic first-pass 18F-FDG PET/CT

Neree Payan et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

11 C-Methionine Integrated PET/MRI- Based Texture Analysis Features May Have a Potential Ability to Distinguish Oligodendroglioma (IDH-Mutant and 1p/19q-Codeleted) From Varied Gliomas

Kai Zhao et al.

ACADEMIC RADIOLOGY (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Usefulness of gradient tree boosting for predicting histological subtype and EGFR mutation status of non-small cell lung cancer on 18F FDG-PET/CT

Sho Koyasu et al.

ANNALS OF NUCLEAR MEDICINE (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Individualized discrimination of tumor recurrence from radiation necrosis in glioma patients using an integrated radiomics-based model

Kai Wang et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer

Jianyuan Zhang et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Pretreatment 18F-FDG PET/CT Radiomics Predict Local Recurrence in Patients Treated with Stereotactic Body Radiotherapy for Early-Stage Non-Small Cell Lung Cancer: A Multicentric Study

Gurvan Dissaux et al.

JOURNAL OF NUCLEAR MEDICINE (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Subregional Radiomics Analysis of PET/CT Imaging with Intratumor Partitioning: Application to Prognosis for Nasopharyngeal Carcinoma

Hui Xu et al.

MOLECULAR IMAGING AND BIOLOGY (2020)

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Article Oncology

Radiomics based on 18F-FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine-learning approach: A preliminary study

Xuejin Ou et al.

CANCER MEDICINE (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Radiomics of 18F-FDG PET/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy

Wei Mu et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Non-invasive tumor decoding and phenotyping of cerebral gliomas utilizing multiparametric 18F-FET PET-MRI and MR Fingerprinting

Johannes Haubold et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

18F-FDG PET/CT radiomic predictors of pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer patients

Panli Li et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Homological radiomics analysis for prognostic prediction in lung cancer patients

Kenta Ninomiya et al.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2020)

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Article Multidisciplinary Sciences

Robust, independent and relevant prognostic 18F-fluorodeoxyglucose positron emission tomography radiomics features in non-small cell lung cancer: Are there any?

Tom Konert et al.

PLOS ONE (2020)

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Article Oncology

Heterogeneity in tumours: Validating the use of radiomic features on 18F-FDG PET/CT scans of lung cancer patients as a prognostic tool

Marie Manon Krebs Krarup et al.

RADIOTHERAPY AND ONCOLOGY (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

PET/CT radiomics signature of human papilloma virus association in oropharyngeal squamous cell carcinoma

Stefan P. Haider et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Alex Zwanenburg et al.

RADIOLOGY (2020)

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Article Multidisciplinary Sciences

Transcriptomics in cancer revealed by Positron Emission Tomography radiomics

Florent Tixier et al.

SCIENTIFIC REPORTS (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Next-Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Algorithms

Isaac Shiri et al.

MOLECULAR IMAGING AND BIOLOGY (2020)

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Article Oncology

18F-FDG Pet Parameters and Radiomics Features Analysis in Advanced Nsclc Treated with Immunotherapy as Predictors of Therapy Response and Survival

Giulia Polverari et al.

CANCERS (2020)

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Article Oncology

Pretreatment 18F-FDG PET/CT texture parameters provide complementary information to Epstein-Barr virus DNA titers in patients with metastatic nasopharyngeal carcinoma

Huan-Chun Lin et al.

ORAL ONCOLOGY (2020)

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Article Cell Biology

Radiomics-based prediction of survival in patients with head and neck squamous cell carcinoma based on pre- and post-treatment 18F-PET/CT

Zheran Liu et al.

Aging-US (2020)

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Article Computer Science, Information Systems

Multi-Level Multi-Modality Fusion Radiomics: Application to PET and CT Imaging for Prognostication of Head and Neck Cancer

Wenbing Lv et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Development and validation of a prognostic model incorporating [18F]FDG PET/CT radiomics for patients with minor salivary gland carcinoma

Nai-Ming Cheng et al.

EJNMMI RESEARCH (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Value of 18F-FDG PET/CT-based radiomics model to distinguish the growth patterns of early invasive lung adenocarcinoma manifesting as ground-glass opacity nodules

Xiaonan Shao et al.

EJNMMI RESEARCH (2020)

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Article Oncology

Prediction of MGMT Status for Glioblastoma Patients Using Radiomics Feature Extraction From 18F-DOPA-PET Imaging

Jing Qian et al.

INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS (2020)

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Article Oncology

Potential Added Value of PET/CT Radiomics for Survival Prognostication beyond AJCC 8th Edition Staging in Oropharyngeal Squamous Cell Carcinoma

Stefan P. Haider et al.

CANCERS (2020)

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Article Oncology

Development and Validation of a Radiomics Nomogram Based on18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography and Clinicopathological Factors to Predict the Survival Outcomes of Patients With Non-Small Cell Lung Cancer

Bin Yang et al.

FRONTIERS IN ONCOLOGY (2020)

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Article Oncology

F-18-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma

Bin Yang et al.

TRANSLATIONAL LUNG CANCER RESEARCH (2020)

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Article Oncology

Predicting EGFR mutation subtypes in lung adenocarcinoma using F-18-FDG PET/CT radiomic features

Qiufang Liu et al.

TRANSLATIONAL LUNG CANCER RESEARCH (2020)

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Review Radiology, Nuclear Medicine & Medical Imaging

Radiomics in medical imaging-how-to guide and critical reflection

Janita E. van Timmeren et al.

INSIGHTS INTO IMAGING (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

A multi-objective radiomics model for the prediction of locoregional recurrence in head and neck squamous cell cancer

Kai Wang et al.

MEDICAL PHYSICS (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Diagnostic classification of solitary pulmonary nodules using support vector machine model based on 2-[18F]fluoro-2-deoxy-D-glucose PET/computed tomography texture features

Jianping Zhang et al.

NUCLEAR MEDICINE COMMUNICATIONS (2020)

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Article Computer Science, Interdisciplinary Applications

Prediction of survival outcome based on clinical features and pretreatment 18FDG-PET/CT for HNSCC patients

Sayantani Ghosh et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2020)

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Review Oncology

Radiomics in radiation oncology-basics, methods, and limitations

Philipp Lohmann et al.

STRAHLENTHERAPIE UND ONKOLOGIE (2020)

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Article Multidisciplinary Sciences

DRTOP: deep learning-based radiomics for the time-to-event outcome prediction in lung cancer

Parnian Afshar et al.

SCIENTIFIC REPORTS (2020)

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Article Multidisciplinary Sciences

Non-invasive decision support for NSCLC treatment using PET/CT radiomics

Wei Mu et al.

NATURE COMMUNICATIONS (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Predictive value of quantitative 18F-FDG-PET radiomics analysis in patients with head and neck squamous cell carcinoma

Roland M. Martens et al.

EJNMMI RESEARCH (2020)

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Article Oncology

Radiomics Analysis and Correlation With Metabolic Parameters in Nasopharyngeal Carcinoma Based on PET/MR Imaging

Qi Feng et al.

FRONTIERS IN ONCOLOGY (2020)

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Article Oncology

Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET

Mengmeng Yan et al.

FRONTIERS IN ONCOLOGY (2020)

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Article Oncology

Performance of 18F-FDG PET/CT Radiomics for Predicting EGFR Mutation Status in Patients With Non-Small Cell Lung Cancer

Min Zhang et al.

FRONTIERS IN ONCOLOGY (2020)

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Article Medicine, General & Internal

Value of Shape and Texture Features from 18F-FDG PET/CT to Discriminate between Benign and Malignant Solitary Pulmonary Nodules: An Experimental Evaluation

Barbara Palumbo et al.

DIAGNOSTICS (2020)

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Article Multidisciplinary Sciences

Incorporating radiomic feature of pretreatment 18F-FDG PET improves survival stratification in patients with EGFR-mutated lung adenocarcinoma

Yu-Hung Chen et al.

PLOS ONE (2020)

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Article Oncology

FET PET Radiomics for Differentiating Pseudoprogression from Early Tumor Progression in Glioma Patients Post-Chemoradiation

Philipp Lohmann et al.

CANCERS (2020)

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Review Radiology, Nuclear Medicine & Medical Imaging

Radiomics in PET/CT: Current Status and Future AI-Based Evolutions

Mathieu Hatt et al.

SEMINARS IN NUCLEAR MEDICINE (2020)

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Article Oncology

Value of 18F-FDG PET/CT-Based Radiomics Nomogram to Predict Survival Outcomes and Guide Personalized Targeted Therapy in Lung Adenocarcinoma With EGFR Mutations

Bin Yang et al.

FRONTIERS IN ONCOLOGY (2020)

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Article Medicine, Research & Experimental

Early response evaluation using primary tumor and nodal imaging features to predict progression-free survival of locally advanced non-small cell lung cancer

Nasha Zhang et al.

THERANOSTICS (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Relationship of EGFR Mutation to Glucose Metabolic Activity and Asphericity of Metabolic Tumor Volume in Lung Adenocarcinoma

Wonseok Whi et al.

NUCLEAR MEDICINE AND MOLECULAR IMAGING (2020)

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Article Oncology

[18F]FDG PET immunotherapy radiomics signature (iRADIOMICS) predicts response of non-small-cell lung cancer patients treated with pembrolizumab

Damijan Valentinuzzi et al.

RADIOLOGY AND ONCOLOGY (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Evaluating Focal18F-FDG Uptake in Thyroid Gland with Radiomics

Aysegul Aksu et al.

NUCLEAR MEDICINE AND MOLECULAR IMAGING (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Assessment of the Prognostic Value of Radiomic Features in 18F-FMISO PET Imaging of Hypoxia in Postsurgery Brain Cancer Patients: Secondary Analysis of Imaging Data from a Single-Center Study and the Multicenter ACRIN 6684 Trial

Mark Muzi et al.

TOMOGRAPHY (2020)

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Article Computer Science, Artificial Intelligence

Radiomics of F-18 Fluorodeoxyglucose PET/CT Images Predicts Severe Immune-related Adverse Events in Patients with NSCLC

Wei Mu et al.

RADIOLOGY-ARTIFICIAL INTELLIGENCE (2020)

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Article Oncology

Predicting neo-adjuvant chemotherapy response and progression-free survival of locally advanced breast cancer using textural features of intratumoral heterogeneity on F-18 FDG PET/CT and diffusion-weighted MR imaging

Hai-Jeon Yoon et al.

BREAST JOURNAL (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Correlations between metabolic texture features, genetic heterogeneity, and mutation burden in patients with lung cancer

Seung Hwan Moon et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Discovery of pre-therapy 2-deoxy-2-18F-fluoro-D-glucose positron emission tomography-based radiomics classifiers of survival outcome in non-small-cell lung cancer patients

Mubarik A. Arshad et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Pre-treatment 18F-FDG PET-based radiomics predict survival in resected non-small cell lung cancer

H. K. Ahn et al.

CLINICAL RADIOLOGY (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Ability of 18F-FDG PET/CT Radiomic Features to Distinguish Breast Carcinoma from Breast Lymphoma

Xuejin Ou et al.

CONTRAST MEDIA & MOLECULAR IMAGING (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy

Lidija Antunovic et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2019)

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Article Medicine, General & Internal

Intratumoral heterogeneity as measured using the tumor-stroma ratio and PET texture analyses in females with lung adenocarcinomas differs from that of males with lung adenocarcinomas or squamous cell carcinomas

Young Wha Koh et al.

MEDICINE (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Radiomics Analysis of PET and CT Components of PET/CT Imaging Integrated with Clinical Parameters: Application to Prognosis for Nasopharyngeal Carcinoma

Wenbing Lv et al.

MOLECULAR IMAGING AND BIOLOGY (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Texture Analysis on [18F]FDG PET/CT in Non-Small-Cell Lung Cancer: Correlations Between PET Features, CT Features, and Histological Types

Francesco Bianconi et al.

MOLECULAR IMAGING AND BIOLOGY (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Comparison of the volumetric and radiomics findings of 18F-FDG PET/CT images with immunohistochemical prognostic factors in local/locally advanced breast cancer

Emine Acar et al.

NUCLEAR MEDICINE COMMUNICATIONS (2019)

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Article Oncology

Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma

Hao Peng et al.

CLINICAL CANCER RESEARCH (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Prognostic Value of Tumor Heterogeneity and SUVmax of Pretreatment 18F-FDG PET/CT for Salivary Gland Carcinoma With High-Risk Histology

Nai-Ming Cheng et al.

CLINICAL NUCLEAR MEDICINE (2019)

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Article Medicine, General & Internal

Radiomics features analysis of PET images in oropharyngeal and hypopharyngeal cancer

Ken Ying-Kai Liao et al.

MEDICINE (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Histogram analysis of C-11-methionine integrated PET/MRI may facilitate to determine the O-6-methylguanylmethyltransferase methylation status in gliomas

Peng Yu et al.

NUCLEAR MEDICINE COMMUNICATIONS (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method

Mehdi Astaraki et al.

PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2019)

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Article Respiratory System

Quantitative FDG PET/CT may help risk-stratify early-stage non-small cell lung cancer patients at risk for recurrence following anatomic resection

Stephanie Harmon et al.

JOURNAL OF THORACIC DISEASE (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Radiomics signature based on FDG-PET predicts proliferative activity in primary glioma

Z. Kong et al.

CLINICAL RADIOLOGY (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Integrating manual diagnosis into radiomics for reducing the false positive rate of 18F-FDG PET/CT diagnosis in patients with suspected lung cancer

Fei Kang et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2019)

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Article Multidisciplinary Sciences

Prognostic Significance of Metabolic Parameters and Textural Features on 18F-FDG PET/CT in Invasive Ductal Carcinoma of Breast

Chin-Chuan Chang et al.

SCIENTIFIC REPORTS (2019)

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Article Neuroimaging

F-18-FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma

Ziren Kong et al.

NEUROIMAGE-CLINICAL (2019)

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Article Medicine, General & Internal

Textural features on 18F-FDG PET/CT and dynamic contrast-enhanced MR imaging for predicting treatment response and survival of patients with hypopharyngeal carcinoma

Chih-Kai Wong et al.

MEDICINE (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Assessing EGFR gene mutation status in non-small cell lung cancer with imaging features from PET/CT

Mengmeng Jiang et al.

NUCLEAR MEDICINE COMMUNICATIONS (2019)

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Article Multidisciplinary Sciences

Radiomics features of the primary tumor fail to improve prediction of overall survival in large cohorts of CT- and PET-imaged head and neck cancer patients

Rachel B. Ger et al.

PLOS ONE (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Bone Marrow and Tumor Radiomics at 18F-FDG PET/CT: Impact on Outcome Prediction in Non-Small Cell Lung Cancer

Sarah A. Mattonen et al.

RADIOLOGY (2019)

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Article Oncology

Using neighborhood gray tone difference matrix texture features on dual time point PET/CT images to differentiate malignant from benign FDG-avid solitary pulmonary nodules

Song Chen et al.

CANCER IMAGING (2019)

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Article Oncology

18F-FDG-PET-based Radiomics signature predicts MGMT promoter methylation status in primary diffuse glioma

Ziren Kong et al.

CANCER IMAGING (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Texture analysis of pretreatment [18F]FDG PET/CT for the prognostic prediction of locally advanced salivary gland carcinoma treated with interstitial brachytherapy

Wen-Jie Wu et al.

EJNMMI RESEARCH (2019)

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Article Oncology

Identifying pathological subtypes of non-small-cell lung cancer by using the radiomic features of 18F-fluorodeoxyglucose positron emission computed tomography

Xue Sha et al.

TRANSLATIONAL CANCER RESEARCH (2019)

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Article Oncology

Hybrid 18F-FDG-PET/MRI Measurement of Standardized Uptake Value Coupled with Yin Yang 1 Signature in Metastatic Breast Cancer. A Preliminary Study

Concetta Schiano et al.

CANCERS (2019)

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Article Multidisciplinary Sciences

11C-methionine-PET for differentiating recurrent brain tumor from radiation necrosis: radiomics approach with random forest classifier

Masatoshi Hotta et al.

SCIENTIFIC REPORTS (2019)

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Article Oncology

Predictive Power of a Radiomic Signature Based on 18F-FDG PET/CT Images for EGFR Mutational Status in NSCLC

Xiaofeng Li et al.

FRONTIERS IN ONCOLOGY (2019)

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Article Oncology

A Non-invasive Radiomic Method Using 18F-FDG PET Predicts Isocitrate Dehydrogenase Genotype and Prognosis in Patients With Glioma

Longfei Li et al.

FRONTIERS IN ONCOLOGY (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

18F-FDG-PET/CT features of primary tumours for predicting the risk of recurrence in thyroid cancer after total thyroidectomy: potential usefulness of combination of the SUV-related, volumetric, and heterogeneous texture parameters

Masatoyo Nakajo et al.

BRITISH JOURNAL OF RADIOLOGY (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

Predicting Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer Combined Statistical Modeling Using Clinicopathological Factors and FDG PET/CT Texture Parameters

Hyunjong Lee et al.

CLINICAL NUCLEAR MEDICINE (2019)

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Article Otorhinolaryngology

Prognostic value of textural indices extracted from pretherapeutic 18-F FDG-PET/CT in head and neck squamous cell carcinoma

Catherine Guezennec et al.

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Article Radiology, Nuclear Medicine & Medical Imaging

Potential feature exploration and model development based on 18F-FDG PET/CT images for differentiating benign and malignant lung lesions

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EUROPEAN JOURNAL OF RADIOLOGY (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

A Pilot Study of Texture Analysis of Primary Tumor [18F]FDG Uptake to Predict Recurrence in Surgically Treated Patients with Non-small Cell Lung Cancer

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Article Radiology, Nuclear Medicine & Medical Imaging

Combining Structural and Textural Assessments of Volumetric FDG-PET Uptake in NSCLC

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IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES (2019)

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Article Engineering, Biomedical

Combining many-objective radiomics and 3D convolutional neural network through evidential reasoning to predict lymph node metastasis in head and neck cancer

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Article Radiology, Nuclear Medicine & Medical Imaging

FLT PET Radiomics for Response Prediction to Chemoradiation Therapy in Head and Neck Squamous Cell Cancer

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TOMOGRAPHY (2019)

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Article Radiology, Nuclear Medicine & Medical Imaging

[18F] FDG Positron Emission Tomography (PET) Tumor and Penumbra Imaging Features Predict Recurrence in Non-Small Cell Lung Cancer

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Article Radiology, Nuclear Medicine & Medical Imaging

Texture analysis of high-resolution dedicated breast 18 F-FDG PET images correlates with immunohistochemical factors and subtype of breast cancer

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Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery

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Can Laws Be a Potential PET Image Texture Analysis Approach for Evaluation of Tumor Heterogeneity and Histopathological Characteristics in NSCLC?

Seyhan Karacavus et al.

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Article Radiology, Nuclear Medicine & Medical Imaging

Glioma Survival Prediction with Combined Analysis of In Vivo 11C-MET PET Features, Ex Vivo Features, and Patient Features by Supervised Machine Learning

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Article Radiology, Nuclear Medicine & Medical Imaging

Intratumoral heterogeneity in 18F-FDG PET/CT by textural analysis in breast cancer as a predictive and prognostic subrogate

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ANNALS OF NUCLEAR MEDICINE (2018)

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Associations Between PET Textural Features and GLUT1 Expression, and the Prognostic Significance of Textural Features in Lung Adenocarcinoma

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ANTICANCER RESEARCH (2018)

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Article Radiology, Nuclear Medicine & Medical Imaging

Texture analysis of 18F-FDG PET/CT for grading thymic epithelial tumours: usefulness of combining SUV and texture parameters

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BRITISH JOURNAL OF RADIOLOGY (2018)

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Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions

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EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2018)

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Robustness versus disease differentiation when varying parameter settings in radiomics features: application to nasopharyngeal PET/CT

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EUROPEAN RADIOLOGY (2018)

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Article Radiology, Nuclear Medicine & Medical Imaging

A multiobjective Bayesian networks approach for joint prediction of tumor local control and radiation pneumonitis in nonsmall-cell lung cancer (NSCLC) for response-adapted radiotherapy

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18F-fluorodeoxyglucose positron-emission tomography (FDG-PET)-Radiomics of metastatic lymph nodes and primary tumor in non-small cell lung cancer (NSCLC) - A prospective externally validated study

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Article Multidisciplinary Sciences

Intratumoral heterogeneity characterized by pretreatment PET in non-small cell lung cancer patients predicts progression-free survival on EGFR tyrosine kinase inhibitor

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18F-FDG PET image biomarkers improve prediction of late radiation-induced xerostomia

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Predicting hypoxia status using a combination of contrast-enhanced computed tomography and [18F]-Fluorodeoxyglucose positron emission tomography radiomics features

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Article Oncology

Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy

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RADIOTHERAPY AND ONCOLOGY (2018)

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Prognostic value of combining a quantitative image feature from positron emission tomography with clinical factors in oligometastatic non-small cell lung cancer

Garrett L. Jensen et al.

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Influence of age on radiomic features in 18F-FDG PET in normal breast tissue and in breast cancer tumors

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Oncotarget (2018)

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Associations of Tumor PD-1 Ligands, Immunohistochemical Studies, and Textural Features in 18F-FDG PET in Squamous Cell Carcinoma of the Head and Neck

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SCIENTIFIC REPORTS (2018)

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Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy

Anastasia Oikonomou et al.

SCIENTIFIC REPORTS (2018)

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Article Radiology, Nuclear Medicine & Medical Imaging

Integrating quantitative morphological and intratumoural textural characteristics in FDG-PET for the prediction of prognosis in pharynx squamous cell carcinoma patients

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CLINICAL RADIOLOGY (2018)

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Article Radiology, Nuclear Medicine & Medical Imaging

Radiomic Profiling of Head and Neck Cancer: 18F-FDG PET Texture Analysis as Predictor of Patient Survival

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CONTRAST MEDIA & MOLECULAR IMAGING (2018)

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A pilot study using kernelled support tensor machine for distant failure prediction in lung SBRT

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Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans

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PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS (2018)

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Integrated texture parameter of 18F-FDG PET may be a stratification factor for the survival of nonoperative patients with locally advanced non-small-cell lung cancer

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NUCLEAR MEDICINE COMMUNICATIONS (2018)

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Textural features of cervical cancers on FDG-PET/CT associate with survival and local relapse in patients treated with definitive chemoradiotherapy

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SCIENTIFIC REPORTS (2018)

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Predicting IDH genotype in gliomas using FET PET radiomics

Philipp Lohmann et al.

SCIENTIFIC REPORTS (2018)

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Combined FET PET/MRI radiomics differentiates radiation injury from recurrent brain metastasis

Philipp Lohmann et al.

NEUROIMAGE-CLINICAL (2018)

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Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis

Shih-ying Huang et al.

NPJ BREAST CANCER (2018)

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Intratumoral heterogeneity of 18F-FLT uptake predicts proliferation and survival in patients with newly diagnosed gliomas

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ANNALS OF NUCLEAR MEDICINE (2017)

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Article Radiology, Nuclear Medicine & Medical Imaging

Correlation of pretreatment 18F-FDG PET tumor textural features with gene expression in pharyngeal cancer and implications for radiotherapy-based treatment outcomes

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Radiation injury vs. recurrent brain metastasis: combining textural feature radiomics analysis and standard parameters may increase 18F-FET PET accuracy without dynamic scans

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EUROPEAN RADIOLOGY (2017)

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Article Radiology, Nuclear Medicine & Medical Imaging

Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non-Small Cell Lung Cancer

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Tumor Heterogeneity Measured on F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Combined With Plasma Epstein-Barr Virus Load Predicts Prognosis in Patients With Primary Nasopharyngeal Carcinoma

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[18F] FDG PET/CT features for the molecular characterization of primary breast tumors

Lidija Antunovic et al.

EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2017)

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Correlation between tumour characteristics, SUV measurements, metabolic tumour volume, TLG and textural features assessed with 18F-FDG PET in a large cohort of oestrogen receptor-positive breast cancer patients

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Radiomics: the bridge between medical imaging and personalized medicine

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NATURE REVIEWS CLINICAL ONCOLOGY (2017)

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Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics

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Post-radiochemotherapy PET radiomics in head and neck cancer - The influence of radiomics implementation on the reproducibility of local control tumor models

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Article Multidisciplinary Sciences

Diagnostic classification of solitary pulmonary nodules using dual time 18F-FDG PET/CT image texture features in granuloma-endemic regions

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Metabolic Radiomics for Pretreatment 18F-FDG PET/CT to Characterize Locally Advanced Breast Cancer: Histopathologic Characteristics, Response to Neoadjuvant Chemotherapy, and Prognosis

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SCIENTIFIC REPORTS (2017)

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Article Radiology, Nuclear Medicine & Medical Imaging

The effects of segmentation algorithms on the measurement of 18F-FDG PET texture parameters in non-small cell lung cancer

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EJNMMI RESEARCH (2017)

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Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

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SCIENTIFIC REPORTS (2017)

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Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma

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ACTA ONCOLOGICA (2017)

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[18F]FDG-PET/CT texture analysis in thyroid incidentalomas: preliminary results

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EUROPEAN JOURNAL OF HYBRID IMAGING (2017)

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Textural features of 18F-FDG PET after two cycles of neoadjuvant chemotherapy can predict pCR in patients with locally advanced breast cancer

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ANNALS OF NUCLEAR MEDICINE (2017)

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Article Radiology, Nuclear Medicine & Medical Imaging

Intratumoural heterogeneity measured using FDG PET and MRI is associated with tumour-stroma ratio and clinical outcome in head and neck squamous cell carcinoma

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CLINICAL RADIOLOGY (2017)

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Article Oncology

18FDG-PET/CT for predicting the outcome in ER+/HER2-breast cancer patients: comparison of clinicopathological parameters and PET image-derived indices including tumor texture analysis

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BREAST CANCER RESEARCH (2017)

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Article Radiology, Nuclear Medicine & Medical Imaging

Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas

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EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2016)

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Differentiating the grades of thymic epithelial tumor malignancy using textural features of intratumoral heterogeneity via 18F-FDG PET/CT

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Article Radiology, Nuclear Medicine & Medical Imaging

Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I-III

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FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy

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Pretreatment 18F-FDG PET Textural Features in Locally Advanced Non Small Cell Lung Cancer: Secondary Analysis of ACRIN 6668/RTOG 0235

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Article Radiology, Nuclear Medicine & Medical Imaging

Early-Stage Non-Small Cell Lung Cancer: Quantitative Imaging Characteristics of 18F Fluorodeoxyglucose PET/CT Allow Prediction of Distant Metastasis

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Radiomics: Images Are More than Pictures, They Are Data

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RADIOLOGY (2016)

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Early Change in Metabolic Tumor Heterogeneity during Chemoradiotherapy and Its Prognostic Value for Patients with Locally Advanced Non-Small Cell Lung Cancer

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Article Radiology, Nuclear Medicine & Medical Imaging

Do clinical, histological or immunohistochemical primary tumour characteristics translate into different 18F-FDG PET/CT volumetric and heterogeneity features in stage II/III breast cancer?

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Intratumoral metabolic heterogeneity predicts invasive components in breast ductal carcinoma in situ

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Article Oncology

Textural features in pre-treatment [F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy

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Article Radiology, Nuclear Medicine & Medical Imaging

Non-Small Cell Lung Cancer Treated with Erlotinib: Heterogeneity of 18F-FDG Uptake at PET-Association with Treatment Response and Prognosis

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Article Radiology, Nuclear Medicine & Medical Imaging

Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer

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EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING (2015)

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18F-FDG PET-Derived Textural Indices Reflect Tissue-Specific Uptake Pattern in Non-Small Cell Lung Cancer

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Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement

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FDG uptake heterogeneity evaluated by fractal analysis improves the differential diagnosis of pulmonary nodules

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Visual Versus Quantitative Assessment of Intratumor 18F-FDG PET Uptake Heterogeneity: Prognostic Value in Non-Small Cell Lung Cancer

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Relationship between Tumor Heterogeneity Measured on FDG-PET/CT and Pathological Prognostic Factors in Invasive Breast Cancer

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Heterogeneity in [18F] Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography of Non-Small Cell Lung Carcinoma and Its Relationship to Metabolic Parameters and Pathologic Staging

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Autoclustering of Non-small Cell Lung Carcinoma Subtypes on 18F-FDG PET Using Texture Analysis: A Preliminary Result

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Are Pretreatment 18F-FDG PET Tumor Textural Features in Non-Small Cell Lung Cancer Associated with Response and Survival After Chemoradiotherapy?

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Article Radiology, Nuclear Medicine & Medical Imaging

Textural Features of Pretreatment 18F-FDG PET/CT Images: Prognostic Significance in Patients with Advanced T-Stage Oropharyngeal Squamous Cell Carcinoma

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