Whole‐specimen histopathology: a method to produce whole‐mount breast serial sections for 3‐D digital histopathology imaging
Bibliographic record
Abstract
AIMS: To develop a method for preparing diagnostic-quality, whole-mount serial sections of breast specimens while preserving 3-D conformation. This required supporting the fresh specimen prior to breadloafing and refining the conventional tissue processing method. The overall goal is to use digital images of whole-specimen histopathology to improve the estimation of extent of disease. METHODS AND RESULTS: To maintain a 3-D conformation, the specimen is suspended in 3.5% agar at 55 degrees C. The block is sliced at 5-mm intervals. Sectioning is performed after extended fixation in 4% formaldehyde from paraformaldehyde in 0.1 m Millonig's buffer, followed by paraffin processing using a non-routine schedule and extended paraffin infiltration. Whole-mount serial breast sections are produced with features of equal or superior quality to that which can be achieved using conventional methods. The method is compatible with some immunohistochemical stains but requires further optimization for others. CONCLUSIONS: The technique is currently suitable for research applications. With the reduction in processing time achievable with microwave-assisted processing, there is the potential for its use as a routine clinical method. This tool may improve the accuracy of margin estimates and identification of multifocality in breast cancer; further evaluation is necessary.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.011 | 0.010 |
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".