Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
Bibliographic record
Abstract
Prostate cancer remains one of the most prevalent malignancies affecting men worldwide, making early detection and advancements in precision medicine crucial for effective intervention and treatment. A standardized protocol is presented for utilizing stimulated Raman histology (SRH) with integrated artificial intelligence (AI) in prostate cancer detection, offering significant advancements over conventional histopathological methods. SRH provides these advancements by enhancing efficiency through near-real-time, label-free imaging of fresh, unstained tissues, thereby eliminating the delays associated with traditional biopsy analysis. By using stimulated Raman scattering (SRS) microscopy to detect the specific vibrational frequencies of CH2 bonds associated with lipids and CH3 bonds linked to proteins and DNA, cancerous and benign tissues in prostate biopsies can be differentiated. The AI model further enhances diagnostic precision, achieving 98.6% accuracy in identifying prostate cancer. The protocol outlines essential steps for sample preparation, imaging, and data analysis, facilitating improved biobanking processes and enabling downstream applications, such as transcriptomics and xenograft studies. This approach accelerates the diagnostic workflow and shows promise for intraoperative applications, potentially aiding surgeons in identifying positive margins intraoperatively. Additionally, the ability to re-scan and adjust cancer-to-tissue ratios allows for a more tailored analysis of biopsy samples, enhancing tumor detection in unprocessed tissues. Further research and validation are necessary for the widespread adoption of SRH in clinical practice.
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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 | 0.000 |
| 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 | 0.000 | 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".