Machine learning-based prediction of luminal breast cancer subtypes using polarised light microscopy
Bibliographic record
Abstract
BACKGROUND: Routine histopathology cannot distinguish between clinically diverse luminal A and B breast cancer subtypes (LBCS), often requiring ancillary testing. Mueller matrix polarimetry (MMP) offers a promising approach by analysing polarised light interactions with complex breast tissues. This study explores the efficacy of using MMP for luminal subtype differentiation. METHODS: We analysed 30 polarimetric and 7 clinical parameters from 116 unstained breast core biopsies, LBCS classified using the BluePrint® molecular assay. These features were used to train various machine learning models: logistic regression, linear discriminant analysis, support vector machine, random forest, and XGBoost to distinguish luminal subtypes. Receiver operating characteristic curve (ROC) analysis was used to each to assess diagnostic performance using area under the curve, accuracy, sensitivity, and specificity. RESULTS: Using the top six most prognostic polarimetric (three) and clinical (three) biomarkers ranked by feature importance, the best-performing random forest model achieved an accuracy of 81% (area under ROC = 86%), with both sensitivity and specificity at 75% on an unseen test set, indicating moderately promising, clinically informative performance. CONCLUSIONS: MMP, particularly its selected Mueller matrix elements, combined with clinical biomarkers show promise in distinguishing LBCS as validated against BluePrint®. By detecting subtle differences in tissue morphology, this approach may enhance breast cancer prognosis and help guide treatment decisions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".