☆ 4.6 Review

Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications

CANCERS (2022)

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Article Computer Science, Artificial Intelligence

Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond

Guang Yang et al.

Summary: XAI is an emerging research field in machine learning that aims to explain the decision-making process of AI systems. In healthcare, XAI is becoming increasingly important for improving the transparency and explainability of deep learning applications, although the lack of explainability in most AI systems may be a major barrier to successful implementation of AI tools in clinical practice.

INFORMATION FUSION (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

How does the pancreatic solid pseudopapillary neoplasm confuse us: Analyzing from the point view of MRI-based radiomics?

Hongxian Gu et al.

Summary: MRI-based radiomics analysis can help distinguish SPNs from radiologically misdiagnosed adenocarcinoma, neuroendocrine tumor, and cystadenoma of pancreas. The efficacy of single sequence logistic model was similar, while combining four sequences and clinical information with radiomics showed better performance in distinction.

MAGNETIC RESONANCE IMAGING (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

CT classification model of pancreatic serous cystic neoplasms and mucinous cystic neoplasms based on a deep neural network

Rong Yang et al.

Summary: This study utilized a deep neural network model to differentiate between pancreatic SCNs and MCNs, finding that the use of the multi-channel ResNet model was superior to the manual ResNet model in classification performance.

ABDOMINAL RADIOLOGY (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Radiomics Analysis for Predicting Malignant Potential of Intraductal Papillary Mucinous Neoplasms of the Pancreas: Comparison of CT and MRI

Shenhao Cheng et al.

Summary: This study compared the performance of CT and MRI radiomics in predicting the malignant potential of intraductal papillary mucinous neoplasms (IPMNs) of the pancreas and compared them to the revised 2017 international consensus Fukuoka guidelines. The study found that MRI radiomics models performed better than CT radiomics models in predicting the malignant potential of IPMNs and had superior performance compared to the clinical and imaging model based on Fukuoka guidelines.

ACADEMIC RADIOLOGY (2022)

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Article Radiology, Nuclear Medicine & Medical Imaging

Preoperative Prediction of G1 and G2/3 Grades in Patients With Nonfunctional Pancreatic Neuroendocrine Tumors Using Multimodality Imaging

Cong Liu et al.

Summary: The study developed and validated a multimodality radiomics model for predicting the grade of nonfunctional pancreatic neuroendocrine tumors before surgery. The model combined radiomic features from multidetector computed tomography and MRI to effectively differentiate different tumor grades.

ACADEMIC RADIOLOGY (2022)

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Article Gastroenterology & Hepatology

Computed Tomography-Based Tumor Heterogeneity Analysis Reveals Differences in a Cohort with Advanced Pancreatic Carcinoma under Palliative Chemotherapy

Jochen Paul Steinacker et al.

Summary: The study suggests that tumor heterogeneity analysis can provide valuable information for therapy response evaluation in pancreatic cancer patients, aiding in treatment adjustment. Data shows certain image features are significantly correlated with the time of tumor progression.

VISCERAL MEDICINE (2021)

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Article Gastroenterology & Hepatology

Deep learning analysis for the detection of pancreatic cancer on endosonographic images: a pilot study

Ryosuke Tonozuka et al.

Summary: The study developed an EUS-CAD system for detecting pancreatic ductal carcinoma with good detection ability. Analysis found that tumor overdiagnosis was associated with mass formation.

JOURNAL OF HEPATO-BILIARY-PANCREATIC SCIENCES (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Magnetic resonance imaging radiomic analysis can preoperatively predict G1 and G2/3 grades in patients with NF-pNETs

Yun Bian et al.

Summary: This study aimed to explore the relationship between MRI rad-score and NF-pNET grades and evaluate the potential of MRI rad-score to differentiate between grade 1 and grade 2/3 NF-pNETs. The results showed that MRI rad-score was significantly associated with NF-pNET grades and may serve as a valuable non-invasive tool for grading NF-pNETs.

ABDOMINAL RADIOLOGY (2021)

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Article Surgery

Pure and Hybrid Deep Learning Models can Predict Pathologic Tumor Response to Neoadjuvant Therapy in Pancreatic Adenocarcinoma: A Pilot Study

Michael D. Watson et al.

Summary: A deep learning model can predict tumor response to neoadjuvant therapy for pancreatic adenocarcinoma patients, and incorporating decreases in serum CA19-9 levels can improve the model's accuracy. Further development is required before clinical application.

AMERICAN SURGEON (2021)

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Article Gastroenterology & Hepatology

Magnetic resonance (MR) for mural nodule detection studying Intraductal papillary mucinous neoplasms (IPMN) of pancreas: Imaging-pathologic correlation

Mirko D'Onofrio et al.

Summary: This study aimed to evaluate the diagnostic accuracy of dynamic MR with DWI in identifying mural nodules of pancreatic IPMN. The results showed significant correlation between MR and pathological specimen in detecting >=5mm solid nodules of IPMN, indicating a potential for accurate diagnosis and correlation with lesion malignancy using these imaging techniques.

PANCREATOLOGY (2021)

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Article Multidisciplinary Sciences

Improving prognostic performance in resectable pancreatic ductal adenocarcinoma using radiomics and deep learning features fusion in CT images

Yucheng Zhang et al.

Summary: The study found weak linear relationships between deep transfer learning features and radiomics features, indicating a potential complementary relationship. By combining radiomics and transfer learning features, the prognosis performance for predicting overall survival in PDAC patients can be significantly improved.

SCIENTIFIC REPORTS (2021)

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Article Medicine, General & Internal

Delta Radiomics Analysis for Local Control Prediction in Pancreatic Cancer Patients Treated Using Magnetic Resonance Guided Radiotherapy

Davide Cusumano et al.

Summary: This study investigated the role of Delta Radiomics analysis in predicting one-year local control in patients with locally advanced pancreatic cancer treated with MRgRT. The most significant feature in predicting 1yLC was the variation of cluster shade calculated at BED = 40 Gy. Further studies with external validation datasets and larger patient cohorts are recommended to confirm the validity of these findings.

DIAGNOSTICS (2021)

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Article Computer Science, Artificial Intelligence

DeepPrognosis: Preoperative prediction of pancreatic cancer survival and surgical margin via comprehensive understanding of dynamic contrast-enhanced CT imaging and tumor-vascular contact parsing

Jiawen Yao et al.

Summary: PDAC is a lethal cancer with surgery being the best treatment option, but outcomes vary significantly among patients. A new deep neural network model, CE-ConvLSTM, is proposed for survival prediction of PDAC patients, along with a self-learning method and multi-task CNN to improve survival prediction performance.

MEDICAL IMAGE ANALYSIS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Radiomics model of dual-time 2-[18F]FDG PET/CT imaging to distinguish between pancreatic ductal adenocarcinoma and autoimmune pancreatitis

Zhaobang Liu et al.

Summary: The study aimed to create a radiomics-based prediction model using dual-time PET/CT imaging for the classification of PDAC and AIP lesions. The model showed promising performance for discriminating between benign AIP and malignant PDAC lesions, indicating its potential as a diagnostic tool for clinical decision-making.

EUROPEAN RADIOLOGY (2021)

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Article Gastroenterology & Hepatology

Survival prediction after upfront surgery in patients with pancreatic ductal adenocarcinoma: Radiomic, clinic-pathologic and body composition analysis

Hongyuan Shi et al.

Summary: The study found that integrating radiomic features with clinicpathologic features and body composition measures can effectively predict survival in PDAC patients after surgery. The radiomics-based model performed better than the clinical model without radiomics signature and the AJCC TNM staging system, showing significant differences in predicting patient survival.

PANCREATOLOGY (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

CT-derived radiomic features to discriminate histologic characteristics of pancreatic neuroendocrine tumors

Giulia Benedetti et al.

Summary: The study aimed to assess the ability of radiomic features extracted from contrast-enhanced and non-contrast-enhanced CT images in discriminating histopathologic characteristics of pancreatic neuroendocrine tumors. The results showed that radiomic features can effectively discriminate the histopathological features of panNET, suggesting a potential role as a non-invasive tool for tumor characterization.

RADIOLOGIA MEDICA (2021)

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Article Oncology

Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study

Sijia Cui et al.

Summary: The study developed a nomogram model combining clinical characteristics and radiomic features to predict high-grade BD-IPMN patients preoperatively. This model showed potential clinical value and treatment guidance significance.

CANCER IMAGING (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

MRI-based radiomics approach for differentiation of hypovascular non-functional pancreatic neuroendocrine tumors and solid pseudopapillary neoplasms of the pancreas

Tao Song et al.

Summary: CE-MRI-based radiomics approach shows great potential in differentiating hypo-NF-pNETs and SPNs. Age is a significant independent factor for this differentiation. The radiomics signature of the arterial phase performs well in discriminating these two types of pancreatic tumors.

BMC MEDICAL IMAGING (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Preoperative prediction of pathological grade in pancreatic ductal adenocarcinoma based on 18F-FDG PET/CT radiomics

Haiqun Xing et al.

Summary: A machine learning model based on radiomic features from PET/CT images was developed to predict the pathological grade of PDAC preoperatively with high accuracy, showing great potential for clinical application in predicting PDAC differentiation.

EJNMMI RESEARCH (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Preoperative differentiation of serous cystic neoplasms from mucin-producing pancreatic cystic neoplasms using a CT-based radiomics nomogram

Shuai Chen et al.

Summary: A CT-based radiomics nomogram was developed and validated for preoperative differential diagnosis of SCNs from mucin-producing PCNs. The nomogram, incorporating clinical features and fusion radiomics signature, showed good diagnostic efficacy in both training and validation cohorts. This comprehensive approach can effectively differentiate SCNs from mucin-producing PCNs.

ABDOMINAL RADIOLOGY (2021)

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Article Oncology

Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient's Pathological Grades

Tao Zhang et al.

Summary: The study demonstrates the value of combining CT radiomics features with various machine learning methods to accurately identify the pathological grades of pancreatic neuroendocrine tumors. DC + AdaBoost, DC + GBDT, and Xgboost+RF show strong abilities in distinguishing between different grades of PNET, with promising application prospects.

FRONTIERS IN ONCOLOGY (2021)

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Article Oncology

Predicting Metastasis Risk in Pancreatic Neuroendocrine Tumors Using Deep Learning Image Analysis

Sergey Klimov et al.

Summary: This study presents a machine learning approach to predict the risk of metastasis in PanNET patients using morphological information. By analyzing tissue slides from PanNET patients, a model was developed to identify different tissues and predict the risk of metastasis. The results show that the model can effectively distinguish between high-risk and low-risk patients, with significant predictive performance for grade I/II patients.

FRONTIERS IN ONCOLOGY (2021)

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Article Medicine, General & Internal

Dual Tracer 68Ga-DOTATOC and 18F-FDG PET Improve Preoperative Evaluation of Aggressiveness in Resectable Pancreatic Neuroendocrine Neoplasms

Paola Mapelli et al.

Summary: This study aimed to define an imaging risk profile for Pancreatic neuroendocrine neoplasms (PanNENs) candidates to surgery by assessing the predictive role of 68Ga-DOTATOC and 18F-FDG PET/CT and PET/MR derived parameters in risk stratification. The results showed that specific PET parameters such as 68Ga-DOTATOC SUVmax, 18F-FDG MTV, and TLG were able to predict tumour stage, metastases, and aggressiveness, providing valuable information for diagnostic preoperative work-up.

DIAGNOSTICS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Whole-tumour evaluation with MRI and radiomics features to predict the efficacy of S-1 for adjuvant chemotherapy in postoperative pancreatic cancer patients: a pilot study

Liang Liang et al.

Summary: This study identified T1WI_NGTDM_Strength radiomics feature of the whole tumor and tumor location as potential predictors of the efficacy of S-1 for PDAC, supporting the precision selection of S-1 as an adjuvant chemotherapy regimen.

BMC MEDICAL IMAGING (2021)

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Article Oncology

CT Simplified Radiomic Approach to Assess the Metastatic Ductal Adenocarcinoma of the Pancreas

Mirko D'Onofrio et al.

Summary: In the diagnostic and therapeutic path of pancreatic ductal adenocarcinoma, early detection and correct staging are crucial. Analyzing CT characteristics showed substantial qualitative and quantitative differences between metastatic and non-metastatic disease.

CANCERS (2021)

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Article Oncology

XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8+ T-Cells in Patients With Pancreatic Ductal Adenocarcinoma

Jing Li et al.

Summary: A machine learning model was developed and validated to predict CD8(+) tumor-infiltrating lymphocyte expression levels in PDAC patients using CT radiomic features. The model showed good discrimination and clinical usefulness, indicating its potential in identifying patients for immunotherapies.

FRONTIERS IN ONCOLOGY (2021)

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Article Oncology

Differentiating TP53 Mutation Status in Pancreatic Ductal Adenocarcinoma Using Multiparametric MRI-Derived Radiomics

Jing Gao et al.

Summary: This study demonstrated that a non-invasive and quantitative mpMRI-based radiomics model can accurately predict TP53 mutation status in pancreatic cancer patients, leading to precision treatment. The 3D ADC-ap-DWI-T2WI model showed the best performance for differentiating TP53 status with high accuracy.

FRONTIERS IN ONCOLOGY (2021)

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Article Oncology

Development of CT-Based Imaging Signature for Preoperative Prediction of Invasive Behavior in Pancreatic Solid Pseudopapillary Neoplasm

Wen-peng Huang et al.

Summary: The study successfully predicted the invasiveness of pancreatic solid pseudopapillary neoplasm by establishing a CT radiomics signature model. Among models in different dimensions and phases, the 3D-arterial model performed the best.

FRONTIERS IN ONCOLOGY (2021)

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Article Medicine, General & Internal

Automated Detection of Pancreatic Cystic Lesions on CT Using Deep Learning

Lorraine Abel et al.

Summary: An algorithm based on a two-step nnU-Net architecture was developed for automated detection of pancreatic cystic lesions on CT scans, showing comparable performance to human readers. The algorithm had high sensitivity for large lesions and those located in the distal pancreas.

DIAGNOSTICS (2021)

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Article Medicine, General & Internal

A [68Ga]Ga-DOTANOC PET/CT Radiomic Model for Non-Invasive Prediction of Tumour Grade in Pancreatic Neuroendocrine Tumours

Alessandro Bevilacqua et al.

Summary: Predicting grade 1 and 2 primary pancreatic neuroendocrine tumours using radiomic features extracted from pre-surgical imaging can help foresee clinical behavior, with surgical histology training achieving the best prediction performance. Hybrid population training showed better results compared to biopsy-only models.

DIAGNOSTICS (2021)

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Article Multidisciplinary Sciences

Effects of interobserver and interdisciplinary segmentation variabilities on CT-based radiomics for pancreatic cancer

Jeffrey Wong et al.

Summary: This study conducted the first interobserver segmentation variability study on CT-based radiomics for pancreatic cancer, finding a higher average Dice similarity coefficient for radiation oncology observers than radiology observers. An interdisciplinary difference was also discovered, with larger variations in intraclass correlation coefficients for radiomic features observed for the radiation oncology group compared to the radiology group.

SCIENTIFIC REPORTS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Machine learning for MRI radiomics: a study predicting tumor-infiltrating lymphocytes in patients with pancreatic ductal adenocarcinoma

Yun Bian et al.

Summary: In this study, a machine learning classifier based on MRI was developed and validated for predicting TILs in PDAC patients. The XGBoost model showed good discrimination and clinical usefulness in predicting PDAC TILs, which could aid in clinical decision making for immune therapy.

ABDOMINAL RADIOLOGY (2021)

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Article Oncology

CT-Based Radiomics Analysis for Preoperative Diagnosis of Pancreatic Mucinous Cystic Neoplasm and Atypical Serous Cystadenomas

Tiansong Xie et al.

Summary: This study demonstrated that CT-based radiomics analysis has the potential to preoperatively distinguish between pancreatic MCN and ASCN, serving as a novel tool for guiding clinical decision-making. The Rad-score showed robust and reliable performance, while the radiological model slightly less well in classification.

FRONTIERS IN ONCOLOGY (2021)

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Article Oncology

Pancreatic Ductal Adenocarcinoma at CT: A Combined Nomogram Model to Preoperatively Predict Cancer Stage and Survival Outcome

Chunyuan Cen et al.

Summary: A combined model integrating clinical and radiomics features was proposed for preoperative discrimination of different stages of PDAC and prediction of overall survival, showing excellent performance in the study.

FRONTIERS IN ONCOLOGY (2021)

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Article Medicine, General & Internal

Deep Learning-Based Differentiation between Mucinous Cystic Neoplasm and Serous Cystic Neoplasm in the Pancreas Using Endoscopic Ultrasonography

Leang Sim Nguon et al.

Summary: This study successfully differentiated between MCN and SCN using a deep learning network model, achieving high accuracy and AUC score. The research confirms the feasibility of diagnosing pancreatic cystic neoplasms using deep learning network models, with potential for further improvement through the use of more datasets.

DIAGNOSTICS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

Validation of Prognostic Radiomic Features From Resectable Pancreatic Ductal Adenocarcinoma in Patients With Advanced Disease Undergoing Chemotherapy

Emmanuel Salinas-Miranda et al.

Summary: This study evaluated the performance of prognostic radiomic features previously published in resectable PDAC patients in a test set of unresectable PDAC patients undergoing chemotherapy. The Sqrt Cluster Tendency radiomic feature remained a significant prognostic factor for overall survival and time to progression in unresectable PDAC patients, suggesting its potential value in predicting outcomes in this population. Further investigation is warranted to understand the biological correlates and clinical applicability of this radiomic feature in PDAC patients.

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

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Article Oncology

Radiomic Features at CT Can Distinguish Pancreatic Cancer from Noncancerous Pancreas

Po-Ting Chen et al.

Summary: The study successfully identified distinguishing CT radiomic features of pancreatic ductal adenocarcinoma (PDAC) and demonstrated that radiomic analysis with machine learning can accurately distinguish between PDAC patients and healthy controls. The developed generalized model showed high sensitivity, specificity, and accuracy when tested on both Taiwanese and U.S. data sets, indicating its potential clinical utility.

RADIOLOGY-IMAGING CANCER (2021)

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Article Oncology

Predicting the recurrence risk of pancreatic neuroendocrine neoplasms after radical resection using deep learning radiomics with preoperative computed tomography images

Chenyu Song et al.

Summary: By using radiomics and deep learning radiomics, a preoperative recurrence prediction model for pancreatic neuroendocrine neoplasms patients after radical surgery was successfully established, allowing for optimized risk evaluation of pNEN recurrence.

ANNALS OF TRANSLATIONAL MEDICINE (2021)

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Article Surgery

Use of Artificial Intelligence Deep Learning to Determine the Malignant Potential of Pancreatic Cystic Neoplasms With Preoperative Computed Tomography Imaging

Michael D. Watson et al.

Summary: This study aimed to predict malignancy of PCNs using CT-guided deep learning techniques, and the results showed that the deep learning model's predictions were more accurate than traditional guidelines.

AMERICAN SURGEON (2021)

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Role of CT texture features for predicting outcome of pancreatic cancer patients with liver metastases

Junjie Hang et al.

Summary: This study evaluated the prognostic value of CT texture features in pancreatic cancer with liver metastases, revealing some significant correlations between texture features and OS. A predictive nomogram incorporating independent prognostic factors accurately predicted OS, and RS and NTP stratified patients into two risk groups.

JOURNAL OF CANCER (2021)

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Review Radiology, Nuclear Medicine & Medical Imaging

The Role of Imaging in Current Treatment Strategies for Pancreatic Adenocarcinoma

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Summary: Imaging plays a crucial role in surveillance, diagnosis, resectability evaluation, and treatment response evaluation of pancreatic cancer. Different imaging modalities have their own advantages and are essential in guiding treatment decisions and assessing therapeutic efficacy.

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Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation

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Management of patients with increased risk for familial pancreatic cancer: updated recommendations from the International Cancer of the Pancreas Screening (CAPS) Consortium

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Preoperative Prediction of Pancreatic Neuroendocrine Neoplasms Grading Based on Enhanced Computed Tomography Imaging: Validation of Deep Learning with a Convolutional Neural Network

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CT-Radiomic Approach to Predict G1/2 Nonfunctional Pancreatic Neuroendocrine Tumor

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Article Radiology, Nuclear Medicine & Medical Imaging

Assessment of Response to Neoadjuvant Therapy Using CT Texture Analysis in Patients With Resectable and Borderline Resectable Pancreatic Ductal Adenocarcinoma

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Imaging phenotype using 18F-fluorodeoxyglucose positron emission tomography-based radiomics and genetic alterations of pancreatic ductal adenocarcinoma

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Article Radiology, Nuclear Medicine & Medical Imaging

Preoperative differentiation of pancreatic mucinous cystic neoplasm from macrocystic serous cystic adenoma using radiomics: Preliminary findings and comparison with radiological model

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Article Radiology, Nuclear Medicine & Medical Imaging

Pancreatic ductal adenocarcinoma: a radiomics nomogram outperforms clinical model and TNM staging for survival estimation after curative resection

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EUROPEAN RADIOLOGY (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Development of a Novel Multiparametric MRI Radiomic Nomogram for Preoperative Evaluation of Early Recurrence in Resectable Pancreatic Cancer

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Contrast-enhanced CT radiomics for predicting lymph node metastasis in pancreatic ductal adenocarcinoma: a pilot study

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CANCER IMAGING (2020)

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Performance of CT-based radiomics in diagnosis of superior mesenteric vein resection margin in patients with pancreatic head cancer

Yun Bian et al.

ABDOMINAL RADIOLOGY (2020)

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Article Radiology, Nuclear Medicine & Medical Imaging

Complementary role of computed tomography texture analysis for differentiation of pancreatic ductal adenocarcinoma from pancreatic neuroendocrine tumors in the portal-venous enhancement phase

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Improving Treatment Response Prediction for Chemoradiation Therapy of Pancreatic Cancer Using a Combination of Delta-Radiomics and the Clinical Biomarker CA19-9

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A Contrast-Enhanced Computed Tomography Based Radiomics Approach for Preoperative Differentiation of Pancreatic Cystic Neoplasm Subtypes: A Feasibility Study

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Auto-segmentation of pancreatic tumor in multi-parametric MRI using deep convolutional neural networks

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Noncontrast Radiomics Approach for Predicting Grades of Nonfunctional Pancreatic Neuroendocrine Tumors

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Multiphase computed tomography radiomics of pancreatic intraductal papillary mucinous neoplasms to predict malignancy

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Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer

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42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20 (2020)

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Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma

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Article Gastroenterology & Hepatology

Treatment outcomes, 30-day readmission and healthcare resource utilization after pancreatoduodenectomy for pancreatic malignancies

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Computer-Aided Diagnosis of Pancreas Serous Cystic Neoplasms: A Radiomics Method on Preoperative MDCT Images

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Editorial Material Biochemistry & Molecular Biology

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Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative imaging biomarkers for predicting outcomes of patients treated with chemotherapy

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Usefulness of Deep Learning Analysis for the Diagnosis of Malignancy in Intraductal Papillary Mucinous Neoplasms of the Pancreas

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Prognostic Value of CT Radiomic Features in Resectable Pancreatic Ductal Adenocarcinoma

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SCIENTIFIC REPORTS (2019)

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Diagnostic ability of artificial intelligence using deep learning analysis of cyst fluid in differentiating malignant from benign pancreatic cystic lesions

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CT radiomics associations with genotype and stromal content in pancreatic ductal adenocarcinoma

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Article Oncology

Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning

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Deep learning for World Health Organization grades of pancreatic neuroendocrine tumors on contrast-enhanced magnetic resonance images: a preliminary study

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY (2019)

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Application of Deep Learning to Pancreatic Cancer Detection: Lessons Learned From Our Initial Experience

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JOURNAL OF THE AMERICAN COLLEGE OF RADIOLOGY (2019)

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Article Oncology

Radiomics Facilitates Candidate Selection for Irradiation Stents Among Patients With Unresectable Pancreatic Cancer

Hai-Feng Zhou et al.

FRONTIERS IN ONCOLOGY (2019)

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Article Multidisciplinary Sciences

Preoperative CT texture features predict prognosis after curative resection in pancreatic cancer

Hyung Sun Kim et al.

SCIENTIFIC REPORTS (2019)

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Computed tomography based radiomic signature as predictive of survival and local control after stereotactic body radiation therapy in pancreatic carcinoma

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PLOS ONE (2019)

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A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging

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CT Radiogenomic Characterization of the Alternative Lengthening of Telomeres Phenotype in Pancreatic Neuroendocrine Tumors

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CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas

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Article Oncology

Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: Using whole-tumor CT texture analysis as quantitative biomarkers

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CANCER MEDICINE (2018)

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A radiomics-based formula for the preoperative prediction of postoperative pancreatic fistula in patients with pancreaticoduodenectomy

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Resectable pancreatic adenocarcinoma: Role of CT quantitative imaging biomarkers for predicting pathology and patient outcomes

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EUROPEAN JOURNAL OF RADIOLOGY (2017)

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CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma - a quantitative analysis

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BMC MEDICAL IMAGING (2017)

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Assessment of treatment response during chemoradiation therapy for pancreatic cancer based on quantitative radiomic analysis of daily CTs: An exploratory study

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Combining radiomic features with a miRNA classifier may improve prediction of malignant pathology for pancreatic intraductal papillary mucinous neoplasms

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ONCOTARGET (2016)

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Quantitative imaging to evaluate malignant potential of IPMNs

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ONCOTARGET (2016)

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Age-based computer-aided diagnosis approach for pancreatic cancer on endoscopic ultrasound images

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Review Gastroenterology & Hepatology

Early detection and prevention of pancreatic cancer: Is it really possible today?

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WORLD JOURNAL OF GASTROENTEROLOGY (2014)

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Differentiation of Pancreatic Cancer and Chronic Pancreatitis Using Computer-Aided Diagnosis of Endoscopic Ultrasound (EUS) Images: A Diagnostic Test

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Prognostic factors in metastatic pancreatic cancer: Older patients are associated with reduced overall survival

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Efficacy of an Artificial Neural Network- Based Approach to Endoscopic Ultrasound Elastography in Diagnosis of Focal Pancreatic Masses

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Neural network analysis of dynamic sequences of EUS elastography used for the differential diagnosis of chronic pancreatitis and pancreatic cancer

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