Can MRI accurately define tumor boundaries to guide focal salvage after radiotherapy?
Bibliographic record
Abstract
124 Background: We evaluated the role of MRI (plus/minus biopsy) in delineating tumour boundaries for focal salvage therapy of prostate cancer recurrence after external beam radiotherapy. Methods: Patients with biochemical failure after radiotherapy were enrolled in a prospective clinical trial mapping sites of local recurrence. An integrated diagnostic MRI and interventional mapping biopsy procedure was performed under sedation in a 1.5T scanner. Patients were imaged with a pelvic coil and an endorectal coil attached to a stereotactic transperineal template assembly. Multiparametric MRI images were acquired, followed by targeted radial biopsy of suspicious regions and random sextant sampling of the normal-appearing peripheral zone. Histology maps were generated by delineation and registration of biopsy cores onto diagnostic images using point-based rigid image registration. Two independent blinded observers reviewed images offline and delineated tumours boundaries which were compared against overlaid histology maps. Coverage was considered accurate if all pathologically proven tumour sites were encompassed within delineated boundaries. Results: Of the 18 patients analysed to date, the majority (83%) were found to have local recurrence. Patients with <6 informative cores were excluded, leaving 15 patients for analysis. Observers performed comparably, whereby mean MRI sensitivity, specificity, PPV and NPV for detecting tumor was 0.76, 0.7, 0.7, and 0.75. The MRI tumour boundary was accurate in 5/15 patients, and improved to 8/15 patients with addition of a 5-mm expansion margin. Targeted radial biopsies improved accuracy to 14/15 patients, by excluding false positive regions (n=2), increasing tumor volumes (n=2) or both (n=2). Random sampling biopsy was only relevant in 1 patient by detecting tumor not identified by MRI and targeted biopsy. Conclusions: MRI alone is not sufficiently accurate to define boundaries for tumor-targeted salvage even with addition of an uncertainty margin. Targeted biopsy improved both detection and delineation accuracy for recurrent tumor regions, and changed salvage therapy planning in 40% of patients. No significant financial relationships to disclose.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".