Abstract
Background: 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.
Methods: 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.
Results: 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.
Conclusion: 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.
Keywords: Targeted Therapy, AI, Prognosis and Response Prediction, Pan-cancer, Radiology
Highlights:
• Categorizes AI-based approaches for targeted therapy into direct and indirect paradigms.
• Provides a comprehensive overview of machine learning and deep learning methodologies.
• Critically discusses the limitations of current studies and outlines future research directions.
• Develops an interactive website to visualize and continuously update the related resources.
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.
Methods: 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.
Results: 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.
Conclusion: 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.
Keywords: Targeted Therapy, AI, Prognosis and Response Prediction, Pan-cancer, Radiology
Highlights:
• Categorizes AI-based approaches for targeted therapy into direct and indirect paradigms.
• Provides a comprehensive overview of machine learning and deep learning methodologies.
• Critically discusses the limitations of current studies and outlines future research directions.
• Develops an interactive website to visualize and continuously update the related resources.
| Original language | English |
|---|---|
| Article number | 1337 |
| Journal | Journal of translational medicine |
| Volume | 23 |
| DOIs | |
| Publication status | Published - 24 Nov 2025 |
Keywords / Materials (for Non-textual outputs)
- AI
- Pan-cancer
- Prognosis and response prediction
- Radiology
- Targeted therapy