Virtual tissue microarrays: a novel and viable approach to optimizing tissue microarrays for biomarker research applied to ductal carcinoma <i>in situ</i>
Bibliographic record
Abstract
AIMS: Tissue microarrays (TMAs) are effective tools for performing high-throughput standardization analyses of biomarkers, but evidence indicating the core number required to be representative of the whole tumour is lacking. Ductal carcinoma in situ (DCIS) is a non-obligate precursor of invasive breast cancer. The number and size of cores that can best represent a DCIS lesion are unknown. Rather than performing extensive experiments using several variants of physical TMAs, the aim of this study was to develop a 'virtual TMA' approach that is effective at optimizing biomarker discovery and validation. METHODS AND RESULTS: Whole DCIS sections from 95 patients were evaluated by immunohistochemistry for oestrogen receptor (ER), progesterone receptor (PgR), HER2, and Ki67. Histoscores were generated manually for ER, PgR, and HER2, as well as percentage positivity for Ki67. Slides were scanned using the FDA-approved Ariol SL50 Image Analysis system, and the virtual array (V-Array) module was used. Virtual cores created virtual TMAs, and our validated scoring classifiers were applied. Automated histoscores and percentage positivity were determined, and compared against increasing numbers of cores. The optimal number of cores was based on concordant results between virtual TMAs and corresponding whole sections. CONCLUSIONS: We have shown that virtual arrays constitute an important tool in digital pathology in both research and clinical settings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".