Machine learning-based prediction of luminal breast cancer subtypes using polarised light microscopy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it