Radiology-based artificial intelligence for predicting targeted therapy response in pan-cancer: a comprehensive review
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".