Toward a quantitative method for estimating tumour-stroma ratio in breast cancer using polarized light microscopy
Bibliographic record
Abstract
The tumour-stroma ratio (TSR) has been explored as a useful source of prognostic information in various cancers, including colorectal, breast, and gastric. Despite research showing potential prognostic utility, its uptake into the clinic has been limited, in part due to challenges associated with subjectivity, reproducibility, and quantification. We have recently proposed a simple, robust, and quantifiable high-contrast method of imaging intra- and peri-tumoural stroma based on polarized light microscopy. Here we report on its use to quantify TSR in human breast cancer using unstained slides from 40 patient samples of invasive ductal carcinoma (IDC). Polarimetric results based on a stromal abundance metric correlated well with pathology designations, showing a statistically significant difference between high- and low-stroma samples as scored by two clinical pathologists. The described polarized light imaging methodology shows promise for use as a quantitative, automatic, and standardizable tool for quantifying TSR, potentially addressing some of the challenges associated with its current estimation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".