Novel quantitative signature of tumor stromal architecture: polarized light imaging differentiates between myxoid and sclerotic human breast cancer stroma
Bibliographic record
Abstract
As a leading cause of death in women, breast cancer is a global health concern for which personalized therapy remains largely unrealized, resulting in over- or under-treatment. Recently, tumor stroma has been shown to carry important prognostic information, both in its relative abundance and morphology, but its current assessment methods are few and suboptimal. Herein, we present a novel stromal architecture signature (SAS) methodology based on polarized light imaging that quantifies patterns of tumor connective tissue. We demonstrate its ability to differentiate between myxoid and sclerotic stroma, two pathology-derived categories associated with significantly different patient outcomes. The results demonstrate a 97% sensitivity and 88% specificity for myxoid stroma identification in a pilot study of 102 regions of interest from human invasive ductal carcinoma breast cancer surgical specimens (20 patients). Additionally, the SAS numerical score is indicative of the wide range of stromal characteristics within these binary classes and highlights ambiguous mixed-morphology regions prone to misclassification. The enabling polarized light microscopy technique is inexpensive, fast, fully automatable, applicable to fresh or embedded tissue without the need for staining and thus potentially translatable into research and/or clinical settings. The SAS metric yields quantifiable and objective stromal characterization with promise for prognosis in many types of cancers beyond breast carcinoma, enabling researchers and clinicians to further investigate the emerging and important role of stromal architectural patterns in solid tumors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".