Patterns of failure with 18F-DCFPyL PSMA-PET/CT in the post-prostatectomy setting
Bibliographic record
Abstract
INTRODUCTION: F-DCFPyL in patients with residual disease or biochemical recurrence (BCR), and its association with surgical pathology and prostate-specific antigen (PSA) kinetics. METHODS: Men from South Central Ontario enrolled in the PSMA Registry for Recurrent Prostate cancer (PREP) between April 2019 and December 2021 after radical prostatectomy (RP) and who had 1) pathologic stage N1 or persistent elevated PSA; or 2) BCR (PSA ≥0.10 ng/mL) where initial postoperative PSA was undetectable were included. RESULTS: A total of 169 men (median age 68 years; interquartile range [IQR] 62-71) with complete data met the above criteria. The median PSA was 0.27 ng/mL (IQR 0.16-0.85) prior to PSMA-PET. Overall positivity rate 59%; when PSA was <0.40 ng/mL, overall positivity rate 42% vs. 85% with PSA ≥0.40 ng/mL (p<0.001). Higher pathologic tumor stage increased detection of regional lymph nodes (LNs) (pT2-3a: 32% vs. pT3b: 69%, p<0.001) but not distant metastases (pT2-3a: 12% vs. pT3b: 24%, p=0.15). PSMA-PET detected 18% with prostate bed, 42% with regional LN disease, and 44% with pelvic-only disease. The three most involved LN chains were the internal (21%) and external (20%) iliac, and obturator chains (16%). CONCLUSIONS: This prospective study of patients with residual disease or BCR after RP illustrates patterns of failure that could impact diagnosis and postoperative management. Such patients have significant risk of regional LN positivity on PSMA-PET, highlighting a need to include pelvic LNs within salvage radiotherapy volumes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".