4.6 Article

Development and verification of radiomics framework for computed tomography image segmentation

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Radiology, Nuclear Medicine & Medical Imaging

Lung cancer diagnosis using deep attention-based multiple instance learning and radiomics

Junhua Chen et al.

Summary: In this article, a computer-aided diagnosis method based on multiple instance learning and deep attention mechanism is proposed, which better reflects the clinical diagnosis process and provides higher interpretability. With radiomics features and attention mechanism, the model achieves good performance on a small imbalanced dataset and provides accurate indication of the importance of each nodule in determining the diagnosis.

MEDICAL PHYSICS (2022)

Article Radiology, Nuclear Medicine & Medical Imaging

Intratumoral analysis of digital breast tomosynthesis for predicting the Ki-67 level in breast cancer: A multi-center radiomics study

Tao Jiang et al.

Summary: This study aimed to evaluate the Ki-67 level in digital breast tomosynthesis (DBT) images of breast cancer patients using a subregional radiomics approach. Results showed that radiomics features extracted from the inner subregion had higher accuracy compared to the whole tumor region or marginal subregion.

MEDICAL PHYSICS (2022)

Article Radiology, Nuclear Medicine & Medical Imaging

A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest x-ray images

Zongsheng Hu et al.

Summary: The study demonstrated that incorporating radiomic analysis into deep learning modeling enhanced the performance and robustness of COVID-19/non-COVID-19 pneumonia/healthy individual classification, showing promising potential for clinical applications in the COVID-19 pandemic.

MEDICAL PHYSICS (2022)

Article Computer Science, Artificial Intelligence

Recent advances and clinical applications of deep learning in medical image analysis

Xuxin Chen et al.

Summary: This paper reviews the recent studies on applying deep learning methods in medical image analysis, emphasizing the latest progress and contributions of state-of-the-art unsupervised and semi-supervised deep learning in this field. It also discusses major technical challenges and suggests possible solutions for future research efforts.

MEDICAL IMAGE ANALYSIS (2022)

Article Oncology

Gross Tumor Volume Segmentation for Stage III NSCLC Radiotherapy Using 3D ResSE-Unet

Xinhao Yu et al.

Summary: This paper proposes a 3D CNN model called 3D ResSE-Unet for gross tumor volume segmentation in stage III NSCLC radiotherapy. The model combines residual connection and channel attention mechanisms and uses three-dimensional convolution operation and encoding-decoding structure to extract spatial information of tumors from computed tomography data. The performance of the model is evaluated using a testing set and compared with other models. The results show that 3D ResSE-Unet achieves superior segmentation results with a short time cost.

TECHNOLOGY IN CANCER RESEARCH & TREATMENT (2022)

Article Radiology, Nuclear Medicine & Medical Imaging

Automatic segmentation of lung tumors on CT images based on a 2D & 3D hybrid convolutional neural network

Wutian Gan et al.

Summary: A hybrid convolution neural network was implemented for automatic lung tumor segmentation on CT images, showing significant improvement in evaluation metrics compared to individual 3D CNN and 2D CNN. Larger tumor volumes may lead to higher Dice metric values, but instability in tumor boundary delineation was observed.

BRITISH JOURNAL OF RADIOLOGY (2021)

Article Engineering, Biomedical

Multi-parametric MRI based radiomics with tumor subregion partitioning for differentiating benign and malignant soft-tissue tumors

Shengjie Shang et al.

Summary: This study utilized MRI-based radiomics approaches to distinguish malignant from benign soft-tissue tumors with handcrafted and deep learning-based features. The findings showed that the fusion radiomics nomogram had the best diagnostic performance, indicating the potential clinical value of tumoral and intratumoral radiomics in soft-tissue tumor diagnosis.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2021)

Article Oncology

Simultaneous Identification of EGFR, KRAS, ERBB2, and TP53 Mutations in Patients with Non-Small Cell Lung Cancer by Machine Learning-Derived Three-Dimensional Radiomics

Tiening Zhang et al.

Summary: A machine learning-derived radiomics approach was developed to simultaneously discriminate EGFR, KRAS, ERBB2, and TP53 mutations in NSCLC patients. This noninvasive and low-cost method could be helpful in screening patients before invasive sampling and NGS testing.

CANCERS (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy

Michele Avanzo et al.

Summary: The study aimed to improve ML models in predicting NSCLC response to SBRT by integrating pre-treatment CT image features with BED distribution features. Results showed that models using BED variables performed better in predicting tumor response, achieving higher AUC and accuracy than those using CT features alone.

MEDICAL PHYSICS (2021)

Article Oncology

The Impact of Artificial Intelligence CNN Based Denoising on FDG PET Radiomics

Cyril Jaudet et al.

Summary: This study compares the impact of AI denoising and standard gaussian postfilter (EARL1) on FDG PET images. The results show that AI denoising preserves most of the lesion's texture information, while EARL1 has a lower concordant ratio.

FRONTIERS IN ONCOLOGY (2021)

Article Radiology, Nuclear Medicine & Medical Imaging

Machine and deep learning methods for radiomics

Michele Avanzo et al.

MEDICAL PHYSICS (2020)

Review Radiology, Nuclear Medicine & Medical Imaging

Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Perspectives

Ji Eun Park et al.

KOREAN JOURNAL OF RADIOLOGY (2019)

Review Oncology

Data Analysis Strategies in Medical Imaging

Chintan Parmar et al.

CLINICAL CANCER RESEARCH (2018)

Article Radiology, Nuclear Medicine & Medical Imaging

Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels

Muhammad Shafiq-ul-Hassan et al.

MEDICAL PHYSICS (2017)

Review Oncology

Radiomics: the bridge between medical imaging and personalized medicine

Philippe Lambin et al.

NATURE REVIEWS CLINICAL ONCOLOGY (2017)

Article Oncology

Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology

Weimiao Wu et al.

FRONTIERS IN ONCOLOGY (2016)

Article Radiology, Nuclear Medicine & Medical Imaging

Measuring Computed Tomography Scanner Variability of Radiomics Features

Dennis Mackin et al.

INVESTIGATIVE RADIOLOGY (2015)

Article Multidisciplinary Sciences

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Hugo J. W. L. Aerts et al.

NATURE COMMUNICATIONS (2014)

Review Radiology, Nuclear Medicine & Medical Imaging

Quantitative Imaging in Cancer Evolution and Ecology

Robert A. Gatenby et al.

RADIOLOGY (2013)

Article Radiology, Nuclear Medicine & Medical Imaging

Reproducibility of Four-dimensional Computed Tomography-based Lung Ventilation Imaging

Tokihiro Yamamoto et al.

ACADEMIC RADIOLOGY (2012)

Article Computer Science, Information Systems

Isolation-Based Anomaly Detection

Fei Tony Liu et al.

ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA (2012)

Article Radiology, Nuclear Medicine & Medical Imaging

Improving Apparent Diffusion Coefficient Estimates and Elucidating Tumor Heterogeneity Using Bayesian Adaptive Smoothing

Simon Waker-Samuel et al.

MAGNETIC RESONANCE IN MEDICINE (2011)

Article Radiology, Nuclear Medicine & Medical Imaging

Investigation of the support vector machine algorithm to predict lung radiation-induced pneumonitis

Shifeng Chen et al.

MEDICAL PHYSICS (2007)

Article Radiology, Nuclear Medicine & Medical Imaging

Influence of MRI acquisition protocols and image intensity normalization methods on texture classification

G Collewet et al.

MAGNETIC RESONANCE IMAGING (2004)