20 MACHINE LEARNING IDENTIFIES PROGNOSTICATORS OF INTRACRANIAL METASTATIC DISEASE IN PATIENTS WITH BREAST OR LUNG CANCER
Bibliographic record
Abstract
BACKGROUND: Intracranial metastatic disease is a severe complication of cancer that confers substantial morbidity and mortality. Patients with breast or lung cancer are at particularly elevated risk of IMD. Early identification of individuals at increased risk could enable targeted surveillance and timely intervention. METHODS: We developed interpretable machine-learning competing-risk models to estimate the risk of intracranial metastatic disease among patients with breast or lung cancer. For each cancer type, cause-specific Cox models were combined via the Aalen-Johansen estimator to produce absolute risk estimates at one, three, and five years. RESULTS: Here we show high test set discrimination for intracranial metastatic disease (Uno's C-index: breast 0.95; lung 0.88) and favorable time-dependent precision-recall performance (AUPRC(t) at 1/3/5 years: breast 0.17/0.53/0.63; lung 0.37/0.61/0.64). Decision-curve analysis across relevant thresholds demonstrates greater net clinical benefit than baseline strategies. Model interpretability analysis identifies cancer stage as the dominant determinant in both cancers; in breast cancer, triple-negative and HER2-positive subtypes contribute additional risk, whereas in lung cancer, histology and tumor size are prominent contributors. CONCLUSIONS: Machine-learning based competing-risk survival models offer greater insight into prognostication of intracranial metastatic disease than baseline strategies. These findings support the potential of such models to strengthen personalized risk stratification and guide targeted surveillance for Intracranial metastatic disease among patients with breast or lung cancer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".