Patterns of failure with <sup>18</sup>F-DCFPyL PSMA PET/CT in the post-prostatectomy setting: A regional cohort analysis.
Bibliographic record
Abstract
309 Background: The participants are patients enrolled in the PSMA-PET Registry for Recurrent Prostate Cancer (PREP) who were referred for [18F]-DCFPyL PET/CT at our institution in Hamilton, Canada. The Registry is the only funded access to PSMA PET/CT for patients in Ontario. Methods: Our analysis includes all men who had a PSMA PET/CT on the Registry between April 2019 and December 2021 and are either node positive, or persistently detectable PSA after initial radical prostatectomy (RP), or biochemical failure after initial RP. Results: In total 177 men were enrolled on the Registry who met the above criteria. 170 men had complete pathological information available and were included. The pre-PSMA PET/CT median PSA was 0.27 ng/mL. Overall, the probability of a positive PSMA PET/CT result was 59.4%, and the incidence increased with increasing PSA. Lymph node (LN) and distant metastases (DM) were detected more frequently in patients with Grade groups 3-5 and higher pathologic tumor (pT) and nodal (pN1) disease. Across all 170 patients the most common site for LN recurrence was in the internal iliac chain (15.9%), followed by the external iliac (14.7%), obturator (11.8%), common iliac (10.0%), pre-sacral (8.8%), para-aortic (7.7%), and peri-rectal (5.9%) chains. The PSMA PET/CT recurrence rate, PSA, and pathological tumor stage is reported, with the location of the recurrence indicated. Conclusions: Our prospective study elucidates patterns of failure for prostate cancer patients with biochemical recurrence after RP and could impact management at diagnosis and after RP. There is a significant risk of pelvic LN positivity on PSMA PET/CT, which emphasizes the importance of including pelvic LNs within salvage radiation volumes. [Table: see text]
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".