The Actual Performance of ML/AI Models in Predicting Radiation-Induced Toxicity in Head and Neck Cancer: A Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
An increasing number of Artificial intelligence (AI) and machine learning (ML) models are being developed to predict radiation-induced toxicities (RITs) in patients with head and neck cancer (HNC). But their performance and reliability remain uncertain. This systematic review and meta-analysis evaluated the predictive accuracy and methodological quality of these models. We comprehensively searched PubMed, EMBASE, Web of Science, and the Cochrane Library to identify studies reporting on ML/AI models for predicting RITs in HNC patients. Eligible studies were assessed for bias risk using the PROBAST tool, and key performance metrics, including the area under the receiver operating curve (AUROC), were extracted. A hierarchical multilevel meta-analysis was performed to estimate pooled AUROC values, and subgroup analyses explored the influence of study characteristics on model performance. A total of 67 studies with a total of 568 models were included, showing moderate discriminatory power of ML/AI models, with a pooled AUROC = 0.76; 95 % CI: 0.73-0.78. Nonetheless, substantial heterogeneity was observed across studies. Incorporating imaging biomarkers significantly improved model performance. Prospective and internal validation showed comparable performance; external validation shows true generalizability. The predominance of retrospective designs and variability in predictor selection may have introduced bias, affecting model reliability and generalisability. ML/AI models hold promise for predicting RITs in HNC patients, but methodological constraints limit their applicability. Standardised and transparent reporting of model development and validation processes is vital for improving comparability among studies. Future research should explore hybrid modelling methods and the integration of clinical, dosimetric, radiomic, and genomic data to boost predictive accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.093 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.058 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".