Current imaging methods for diagnosing BC are associated with limited sensitivity and specificity and modest positive predictive power. The recent progress in image analysis using artificial intelligence (AI) has created great promise to improve breast cancer (BC) diagnosis and subtype differentiation. In this case, novel quantitative computational methods, such as radiomics, have been developed to improve the sensitivity and specificity of early BC diagnosis and classification. The potential of radiomics in improving the diagnostic efficacy of imaging studies has been shown in several studies. In this review article, we discuss the radiomics workflow and current hand-crafted radiomics methods in the diagnosis and classification of BC based on most recent studies on different imaging modalities, e.g. MRI, mammography, contrast-enhanced spectral mammography (CESM), ultrasound imaging, and digital breast tumosynthesis (DBT). We also discuss current challenges and potential strategies to improve the specificity and sensitivity of radiomics in breast cancer to help achieve a higher level of BC classification and diagnosis in the clinical setting. The growing field of AI incorporation with imaging information has opened a great opportunity to provide a higher level of care for BC patients.
Breast MRI radiomics and machine learning radiomics-based predictions of response to neoadjuvant chemotherapy -- how are they affected by variations in tumour delineation?
Convolutional Restricted Boltzmann Machine Based-Radiomics for Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer
RADIFUSION: A multi-radiomics deep learning based breast cancer risk prediction model using sequential mammographic images with image attention and bilateral asymmetry refinement
Monash University Malaysia（莫纳什大学马来西亚分校）；The University of Oklahoma（俄克拉荷马大学）；Karolinska Institute（卡罗林斯卡学院）；Karolinska University Hospital（卡罗林斯卡大学医院）；University of Malaya（马来亚大学）；The University of Melbourne（墨尔本大学）
Radiomics and artificial Intelligence for thyroid cancer diagnosis: Concepts, challenges, and solutions
University of Tabriz（大不里士大学）；Maragheh University of Medical Sciences（马拉盖医科大学）；Tabriz University of Medical Sciences（大不里士医科大学）；TITU University（TITU大学）；Australian University (AU)（澳大利亚大学（AU））；AGH University of Science and Technology（AGH科技大学）；Cracow University of Technology（克拉科夫理工大学）；Polish Academy of Sciences（波兰科学院）；Queensland University of Technology（昆士兰科技大学）；Tabriz Islamic Art University（大不里士伊斯兰艺术大学）
Non-Redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer
LTCI, Télécom Paris, Institut Polytechnique de Paris（巴黎理工学院巴黎电信学院LTCI实验室）；Guerbet Research（盖尔贝特研究中心）；Department of Radiology, Hospital of Annecy-Genevois, Université de Paris（巴黎大学阿讷西-热内瓦医院放射科）；Sorbonne Université, CNRS, LIP6（索邦大学、法国国家科学研究中心、LIP6实验室）
Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT images
Southern Medical University（南方医科大学）；LaTIM（拉蒂姆实验室）；INSERM（法国国家健康与医学研究院）；Univ Brest（布雷斯特大学）；Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院）
头颈癌肿瘤和淋巴结的自动分割在优化治疗策略和预后分析中起着至关重要的作用。本研究旨在使用nnU-Net进行自动分割，并使用放射组学在多中心HNC队列中使用治疗前PET/CT图像进行无复发生存期（RFS）预测。HECKTOR 2022中提供了一个包含883例患者（524例患者用于培训，359例患者用于测试）的多中心HNC数据集。检索每例患者的扩展口咽区域的边界框，固定尺寸为224 x 224 x 224 $mm^{3}$。然后采用3D nnU-Net结构对原发肿瘤和淋巴结进行同步自动分割，在预测分割的基础上，提取每个患者的10个常规特征和346个标准化放射组学特征。构建了三个预后模型，仅包含常规和放射组学特征，以及它们通过多变量CoxPH建模的组合。探索了统计协调方法ComBat，以减少多中心变异。Dice评分和C指数分别用作分割和预后任务的评价指标。对于分割任务，我们通过3D nnU-Net实现了原发性肿瘤和淋巴结的平均骰子得分约为0.701。对于预后任务，常规和放射组学模型在测试集中分别获得了0.658和0.645的C指数，而组合模型没有改善预后性能，C指数为0.648。
Radiomic biomarker extracted from PI-RADS 3 patients support more eìcient and robust prostate cancer diagnosis: a multi-center study
Zhengzhou University（郑州大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Renmin Hospital of Wuhan University（武汉大学人民医院）；Guangdong Provincial People's Hospital（广东省人民医院）；Radboud University Medical Center（拉德堡德大学医学中心）；Wuhan University People's Hospital（武汉大学人民医院）
Utilizing Automated Breast Cancer Detection to Identify Spatial Distributions of Tumor Infiltrating Lymphocytes in Invasive Breast Cancer
Stony Brook University（石溪大学）；Stony Brook Medicine（石溪医学院）；Stony Brook University Hospital（石溪大学医院）；University of Michigan Medical School（密歇根大学医学院）；National Cancer Institute（美国国家癌症研究所）；National Institutes of Health（美国国立卫生研究院）；Emory University（埃默里大学）
Do High-Performance Image-to-Image Translation Networks Enable the Discovery of Radiomic Features? Application to MRI Synthesis from Ultrasound in Prostate Cancer
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
The Hong Kong University of Science and Technology（香港科技大学）；Stanford University School of Medicine（斯坦福大学医学院）；The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳-香港协同创新研究院）
Non-Invasive MGMT Status Prediction in GBM Cancer Using Magnetic Resonance Images (MRI) Radiomics Features: Univariate and Multivariate Machine Learning Radiogenomics Analysis
Kermanshah University of Medical Sciences（克尔曼沙阿医科大学）；Rajaie Cardiovascular Medical and Research Center（拉贾伊心血管医疗与研究中心）；Iran University of Medical Science（伊朗医科大学）；The University of British Columbia（不列颠哥伦比亚大学）；Kerman University（克尔曼大学）；University of British Columbia（不列颠哥伦比亚大学）
背景和目的：本研究旨在通过MRI放射组学特征以及单因素和多因素分析来预测O-6甲基鸟嘌呤-DNA甲基转移酶（MGMT）基因启动子的甲基化状态。材料和方法：82例MGMT甲基化状态的患者纳入本研究。在MR图像的四个区域中手动分割肿瘤，a）整个肿瘤，b）活动/增强区域，c）坏死区域和d）水肿区域（E）。为每个患者提取了大约七千个放射组学特征。通过不同的机器学习算法，使用特征选择和分类器来预测MGMT状态。使用受试者工作特征（ROC）曲线的曲线下面积（AUC）进行模型评价。结果如下：关于单变量分析，发现使用4.5 mm Sigma拉普拉斯高斯滤波器（AUC：0.71（p值：0.002））的整个肿瘤段中的灰度共生矩阵（GLCM）的逆方差特征是最佳预测因子。对于多变量分析，在水肿区域中具有从模型选择特征选择器和筛选过滤器的决策树分类器具有最高性能（AUC：0.78），其次是在水肿区域中具有从模型选择特征选择器和筛选过滤器的Ada Boost分类器（AUC：0.74）。结论：这项研究表明，使用机器学习算法的放射组学是预测GBM癌症患者MGMT甲基化状态的可行的非侵入性方法关键词：放射组学，放射基因组学，GBM，MRI，MGMT
ViT-DeiT: An Ensemble Model for Breast Cancer Histopathological Images Classification
Umm Al-Qura University（乌姆·库拉大学）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；University of Tabuk（塔布克大学）；King Faisal Specialist Hospital and Research Center（费萨尔国王专科医院和研究中心）；University of Nevada（内华达大学）
Robust breast cancer detection in mammography and digital breast tomosynthesis using annotation-efficient deep learning approach
DeepHealth, Inc.（深健公司）；Rhode Island Hospital（罗德岛医院）；Brown University（布朗大学）；Henan Provincial People’s Hospital（河南省人民医院）；Medford Radiology Group（梅德福放射学集团）；University of Massachusetts Medical School（马萨诸塞大学医学院）
Deep Learning-based Radiomic Features for Improving Neoadjuvant Chemoradiation Response Prediction in Locally Advanced Rectal Cancer
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Cancer Hospital Chinese Academy of Medical Sciences（中国医学科学院肿瘤医院）；Cedars Sinai Medical Center（西达赛奈医疗中心）
Towards Early Detection: AI-Based Five-Year Forecasting of Breast Cancer Risk Using Digital Breast Tomosynthesis Imaging
Athinoula A. Martinos Center for Biomedical Imaging, Mass General Brigham and Harvard Medical School（Athinoula A. Martinos 生物医学成像中心，马萨诸塞总医院与哈佛医学院）；École Polytechnique Fédérale de Lausanne (EPFL)（洛桑联邦理工学院）；Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin（柏林夏里特医学院，自由大学和洪堡大学的成员）；Department of Radiology, University of Wisconsin School of Medicine and Public Health（威斯康星大学医学与公共卫生学院放射科）
2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-center Study
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Guangzhou First People’s Hospital, School of Medicine, South China University of Technology（华南理工大学医学院附属广州市第一人民医院）；Guizhou Provincial People’s Hospital（贵州省人民医院）；Henan Cancer Hospital（河南省肿瘤医院）；Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences（广东省人民医院、广东省医学科学院）；Beihang University（北京航空航天大学）
