Predicting Breast Cancer Relapse from Histopathological Images with Ensemble Machine Learning Models
Bibliographic record
Abstract
Relapse and metastasis occur in 30-40% of breast cancer patients, even after targeted treatments like trastuzumab for HER2-positive breast cancer. Accurate individual prognosis is essential for determining appropriate adjuvant treatment and early intervention. This study aims to enhance relapse and metastasis prediction using an innovative framework with machine learning (ML) and ensemble learning (EL) techniques. The developed framework is analyzed using The Cancer Genome Atlas (TCGA) data, which has 123 HER2-positive breast cancer patients. Our two-stage experimental approach first applied six basic ML models (support vector machine, logistic regression, decision tree, random forest, adaptive boosting, and extreme gradient boosting) and then ensembled these models using weighted averaging, soft voting, and hard voting techniques. The weighted averaging ensemble approach achieved enhanced performances of 88.46% accuracy, 89.74% precision, 94.59% sensitivity, 73.33% specificity, 92.11% F-Value, 71.07% Mathew's correlation coefficient, and an AUC of 0.903. This framework enables the accurate prediction of relapse and metastasis in HER2-positive breast cancer patients using H&E images and clinical data, thereby assisting in better treatment decision-making.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".