Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review.
Authors
Affiliations (19)
Affiliations (19)
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital, and Centre for Infection Immunity and Cancer (IIC) of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
- Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK.
- Department of Thoracic Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
- Department of Medical Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Department of Radiology, The Fourth Affiliated Hospital of School of Medicine, International School of Medicine, Zhejiang University, Yiwu, Zhejiang, 322000, China.
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China.
- Centre for Regenerative Medicine, Institute for Regeneration and Repair, The University of Edinburgh, Edinburgh, UK.
- Zhejiang University-University of Edinburgh Joint Institute, Zhejiang University, Haining, China.
- School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK.
- Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK.
- Usher Institute, University of Edinburgh, Edinburgh, UK.
- First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China. [email protected].
- Department of Respiratory Diseases, Haining People's Hospital, Haining, Zhejiang, China. [email protected].
- Department of Thoracic Cancer, Affiliated Hangzhou First People's Hospital, Westlake University School of Medicine, Hangzhou, Zhejiang, China. [email protected].
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital, and Centre for Infection Immunity and Cancer (IIC) of Zhejiang University-University of Edinburgh Institute (ZJU-UoE Institute), Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China. [email protected].
- Edinburgh Medical School, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK. [email protected].
- Biomedical and Health Translational Research Center of Zhejiang Province, Haining, China. [email protected].
- Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence, Haining, China. [email protected].
- Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Zhejiang University, Hangzhou, China. [email protected].
Abstract
Targeted therapy is central to precision oncology, but identifying patients who will benefit remains challenging. Conventional molecular testing, though the current standard, provides limited predictive value. With recent advances in artificial intelligence (AI) and the widespread availability of imaging data, radiology-based AI models have emerged as valuable non-invasive tools for treatment response assessment. We conducted a comprehensive review of 112 studies that developed radiology-based AI models for predicting responses to targeted therapy across various cancer types. The reviewed models were classified into direct prediction approaches, which use end-to-end imaging-based modeling to estimate therapeutic response, and indirect prediction approaches, which infer molecular biomarkers from imaging features to indirectly assess therapeutic sensitivity. Across the identified literature, computed tomography (CT) was the most frequently used imaging modality, followed by magnetic resonance imaging (MRI), positron emission tomography (PET), and ultrasound (US). Lung and breast cancers were the most commonly studied diseases, though work has also expanded into gastric, colorectal, liver, kidney, brain, and ovarian cancers. Both machine learning (ML) and deep learning (DL) frameworks have been applied, with ML remaining dominant but DL gaining increasing attention in recent years, likely because ML offers interpretability and suitability for smaller datasets, whereas DL excels in handling complex, high-dimensional data. Collectively, these studies demonstrate promising performance in predicting response to targeted therapy, while also highlighting the diversity of cancer contexts and methodological designs. Radiology-based AI offers a non-invasive approach to guide treatment selection and monitoring in targeted therapy. This review summarizes current progress, highlights strengths and limitations of direct and indirect prediction strategies, and discusses future directions. To support accessibility, we also provide a continuously updated interactive website of included resources.