Stimulated Raman histology, a novel method to allow for rapid pathologic examination of unprocessed, fresh prostate biopsies
Bibliographic record
Abstract
INTRODUCTION: Delay between targeted prostate biopsy (PB) and pathologic diagnosis can lead to a concern of inadequate sampling and repeated biopsy. Stimulated Raman histology (SRH) is a novel microscopic technique allowing real-time, label-free, high-resolution microscopic images of unprocessed, unsectioned tissue. This technology holds potential to decrease the time for PB diagnosis from days to minutes. We evaluated the concordance of pathologist interpretation of PB SRH as compared with traditional hematoxylin and eosin (H&E) stained slides. METHODS: , to create SRH images. The cores were then processed as per normal pathologic protocols. Sixteen PB containing a mix of benign and malignant histology were used as an SRH training cohort for four genitourinary pathologists, who were then tested on a set of 32 PBs imaged by SRH and processed by traditional H&E. Sensitivity, specificity, accuracy, and concordance for prostate cancer (PCa) detection on SRH relative to H&E were assessed. RESULTS: The mean pathologist accuracy for the identification of any PCa on PB SRH was 95.7%. In identifying any PCa or ISUP grade group 2-5 PCa, a pathologist was independently able to achieve good and very good concordance (κ: 0.769 and 0.845, respectively; p < 0.001). After individual assessment was completed a pathology consensus conference was held for the interpretation of the PB SRH; after the consensus conference the pathologists' concordance in identifying any PCa was also very good (κ: 0.925, p < 0.001; sensitivity 95.6%; specificity 100%). CONCLUSION: SRH produces high-quality microscopic images that allow for accurate identification of PCa in real-time without need for sectioning or tissue processing. The pathologist performance improved through progressive training, showing that ultimately high accuracy can be obtained. Ongoing SRH evaluation in the diagnostic and treatment setting hold promise to reduce time to tissue diagnosis, while interpretation by convolutional neural network may further improve diagnostic characteristics and broaden use.
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.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".