Stimulated Raman histology as a novel method for rapid pathologic examination of unprocessed, fresh testicular biopsies to determine seminoma
Bibliographic record
Abstract
BACKGROUND: Stimulated Raman histology (SRH) is a novel microscopic technique allowing real-time, label free, high-resolution microscopic images of unprocessed, unsectioned tissue. Tissue samples are imaged in the operating room using a mobile SRH microscope. Due to SRH's pseudocoloring, the images appear like conventional H&E staining. Compared to standard histopathologic analysis, formalin fixation, staining and level cutting can be omitted holding potential to decrease the time for diagnosis. OBJECTIVE: The aim of this prospective feasibility study was to evaluate SRH in the diagnosis of seminoma. METHODS: Patients with sonographic suspicion of testicular cancer, who underwent surgical exposure of the testis, were included in this study. A small biopsy of the suspicious lesion was separated ex vivo and scanned with an SRH microscope (NIO Laser Imaging System, Invenio Imaging Inc.). Three pathologists were tested on a 40-sample set, including benign and malignant images. We assessed combined accuracy, sensitivity and specificity and concordance (Cohen's Kappa and Fleiss Kappa) for inter-rater reliability. RESULTS: With the use of SRH the combined accuracy in identifying malignancy was 90%, with an individual pathologist sensitivity range from 79 to 97%. The combined accuracy in identifying benign tissue was 94.4%, with an individual pathologist specificity range from 83 to 100%. Cohen's Kappa for inter-rater reliability had a range from 0.342 to 0.918, which was classified as weak to almost perfect. The scanning time was less than 180 seconds for all tissue samples. CONCLUSION: Through this first-in-human feasibility study we were able to show that SRH is a reliable and rapid point of care-technique to produce high-resolution images of seminoma, which can be accurately interpreted by pathologists. After further evaluation for all testicular germ cell tumors, SRH may have an impact on clinical decision-making during interventions for testicular cancer.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".