Technical Note: Method to correlate whole‐specimen histopathology of radical prostatectomy with diagnostic MR imaging
Bibliographic record
Abstract
PURPOSE: Validation of MRI-guided tumor boundary delineation for targeted prostate cancer therapy is achieved via correlation with gold-standard histopathology of radical prostatectomy specimens. Challenges to accurate correlation include matching the pathology sectioning plane with the in vivo imaging slice plane and correction for the deformation that occurs between in vivo imaging and histology. A methodology is presented for matching of the histological sectioning angle and position to the in vivo imaging slices. METHODS: Patients (n = 4) with biochemical failure following external beam radiotherapy underwent diagnostic MRI to confirm localized recurrence of prostate cancer, followed by salvage radical prostatectomy. High-resolution 3-D MRI of the ex vivo specimens was acquired to determine the pathology sectioning angle that best matched the in vivo imaging slice plane, using matching anatomical features and implanted fiducials. A novel sectioning device was developed to guide sectioning at the correct angle, and to assist the insertion of reference dye marks to aid in histopathology reconstruction. RESULTS: The percentage difference in the positioning of the urethra in the ex vivo pathology sections compared to the positioning in in vivo images was reduced from 34% to 7% through slicing at the best match angle. Reference dye marks were generated, which were visible in ex vivo imaging, in the tissue sections before and after processing, and in histology sections. CONCLUSIONS: The method achieved an almost fivefold reduction in the slice-matching error and is readily implementable in combination with standard MRI technology. The technique will be employed to generate datasets for correlation of whole-specimen prostate histopathology with in vivo diagnostic MRI using 3-D deformable registration, allowing assessment of the sensitivity and specificity of MRI parameters for prostate cancer. Although developed specifically for prostate, the method is readily adaptable to other types of whole tissue specimen, such as mastectomy or liver resection.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".