MP70-02 DOES SALVAGE WHOLE GLAND CYROABLATION THERAPY CONFER SURVIVAL ADVANTAGE TO PATIENTS WHO FAILED PRIMARY RADIOTHERAPY FOR PROSTATE CANCER?
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVE: Image-guided destruction of individual prostate cancer foci while sparing benign prostate tissue is emerging as a viable alternative to radical prostatectomy (RP) or radiation for some patients.These focal therapies have lower rates of side effects than whole-gland treatment, yet long-term cancer control remains unknown.It is widely recognized that prostate cancers are usually larger than they appear on MRI, yet there is no universally accepted treatment margin size needed to achieve adequate cancer control for focal therapy.In this study, we used a 3D co-registration algorithm to fuse pre-operative MRI and RP whole-mount histopathology from 51 patients.We sought to assess the cancer control and ablative efficiency achieved using margins drawn by a genitourinary radiologist.METHODS: From March 2014 to November 2018, 277 patients underwent prostate MRI, targeted and systematic prostate biopsy, and RP with whole-mount sectioning at Stanford.Fifty-one putative focal therapy candidates with unilateral, Gleason !7 cancer on biopsy were chosen from this cohort.Prostates were sectioned using 3Dprinted personalized molds.Digitized whole-mount pathology slides were co-registered to pre-operative MRI using a 3D co-registration algorithm.Using MRI and biopsy data while blinded to final prostatectomy pathology, a radiologist (PG) generated focal therapy margins around biopsy-proven MR lesions by adding 1 cm margins around the lesion and then modifying based on biopsy result.RESULTS: Focal therapy cancer control, calculated as the volumetric percentage of pathologic cancer within the planned ablation region, ranged from 54-99% (median 80%).Ablative efficiency, calculated as the percentage of the ablation region that was pathologically cancerous, ranged from 5-26% (median 11%).CONCLUSIONS: We found that adding 1 cm ablation margins to MRI-visible cancer foci achieved cancer control of 80%, while ablating a large amount of benign tissue.Further improvements to margin treatment planning are needed.Using this dataset, we are now investigating crescentic ablation region planning based on the finding that that inadequately treated cancers often follow capsular borders (Fig 1, center panel).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".