Performance of machine learning (ML) and deep learning (DL) algorithms in prediction of breast cancer distant metastasis via imaging: a systematic review and meta‑analysis

Distant metastasis is a major cause of breast cancer mortality. Early prediction may support risk-adapted surveillance and treatment planning. We evaluated imaging-based machine learning (ML) and deep learning (DL) models for predicting future distant metastasis in patients without baseline metastatic disease. PubMed, Scopus, Web of Science, and supplementary Google Scholar searches were performed through June 2025. Eligible studies evaluated imaging-based AI prediction models and reported quantitative performance. The primary outcome was the area under the receiver operating characteristic curve (AUC). Risk of bias was assessed using PROBAST + AI. Random-effects meta-analysis, exploratory subgroup analyses, and univariable meta-regression were performed. Nine studies contributed 43 model evaluations. The pooled model-level AUC was 0.80 (95% CI: 0.77–0.84). A secondary analysis restricted to the best-performing model from each study yielded an AUC of 0.89 (95% CI: 0.83–0.95). CT-based models were associated with higher AUCs than MRI-based models (0.83 vs. 0.71), and models incorporating clinicopathological features had higher AUCs than imaging-only models (0.91 vs. 0.74). Exploratory meta-regression identified associations with imaging modality, follow-up duration, and inclusion of clinicopathological features. Heterogeneity was substantial, and five of nine studies had high risk of bias. Imaging-based ML and DL models show potential for predicting future distant metastasis. In the adjuvant setting, externally validated models may support risk stratification and individualized surveillance, but current evidence is insufficient to justify surveillance or treatment changes based solely on AI predictions. Prospective external validation and transparent reporting are needed before clinical implementation.