Survival prediction in metastatic breast cancer using artificial intelligence: a scoping review
Bibliographic record
Abstract
PURPOSE OF REVIEW: Accurately predicting survival in metastatic breast cancer (MBC) is essential to support personalized treatment decisions. This scoping review examines the current applications of artificial intelligence (AI) models for survival prediction in MBC and highlights their relevance in improving clinical outcomes. RECENT FINDINGS: Of 1787 records screened, 15 studies met inclusion criteria. These studies used supervised learning approaches, including random survival forests (13.3%), Naïve Bayes classifiers (13.3%), and logistic regression models (20.0%), to predict overall survival, progression-free survival, and treatment response. Input data varied widely, incorporating electronic health records, clinical data, imaging, and genomic profiles. Among included studies, 66.7% addressed all three major breast cancer subtypes, 20.0% focused on ER-positive HER2-negative cases, and 13.3% did not specify subtype. Model performance varied, with sensitivities ranging from 42% to 90%, specificities from 53% to 90%, and area under the curve values between 0.70 and 0.85. SUMMARY: AI models show promising potential for improving survival prediction in MBC, offering tools to support more individualized care. However, limitations remain, including inconsistent data quality, suboptimal model performance, and a lack of external validation. Future work should focus on refining models and ensuring clinical applicability through robust validation.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".