Defining radio-recurrent intra-prostatic target volumes using PSMA-targeted PET/CT and multi-parametric MRI
Bibliographic record
Abstract
PURPOSE: Our purpose was to evaluate intra-prostatic cancer volumes for salvage radiotherapy in men with recurrent prostate cancer confined to the prostate post-primary radiotherapy using mpMRI and 18F-DCFPyL PET/CT (PET). METHODS: Men with biochemical failure post-primary radiotherapy were enrolled in a multi-centre trial investigating mpMRI and PET. All men with isolated intra-prostatic recurrence are included in this secondary analysis. The intra-prostatic gross tumour volume (GTV) was manually delineated on mpMRI and was also delineated on PET using three methods: 1. manually, 2. using a 30% threshold of maximum intra-prostatic standard uptake value (SUVmax), and 3. using a 67% threshold of this SUVmax. Clinical target volumes (CTV) including expansions on each GTV were generated. Conformity indices were performed between the mpMRI CTV and each PET CTV. Correlation with biopsy and clinical outcomes were performed. RESULTS: Of the 36 men included, 30 (83%) had disease in two quadrants or less using the combination of mpMRI and PET. Mean target volume (union of CTV on mpMRI and CTV manually delineated on PET) was 12.2 cc (49% of prostate gland volume). 12/36 (33%) men had a biopsy. Per-patient sensitivity was 91% for mpMRI and 82% for PET. CONCLUSIONS: mpMRI and PET provide complementary information for delineation of intra-prostatic recurrent disease. Union of CTV on mpMRI and PET is often less than 50% of the prostate, suggesting this imaging could help define a target for focal salvage therapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".