Mesorectal nodal metastasis with seminal vesicle invasion in biochemically recurrent prostate cancer
Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence and predictors of mesorectal lymph node (MLN) metastases on prostate-specific membrane antigen (PSMA)-based positron emission tomography/computed tomography (PET/CT) in patients with biochemically recurrent prostate cancer (PCa) following radical therapy. MATERIALS AND METHODS: F-DCFPyL-PSMA-PET/CT at the Princess Margaret Cancer Centre between December 2018 and February 2021. Lesions with PSMA scores ≥2 were considered positive for PCa involvement (PROMISE classification). Predictors of MLN metastasis were evaluated using univariable and multivariable logistic regression analyses. RESULTS: Our cohort consisted of 686 patients. The primary treatment method was radical prostatectomy and radiotherapy in 528 (77.0%) and 158 patients (23.0%), respectively. The median serum PSA level was 1.15 ng/mL. Overall, 384 patients (56.0%) had a positive scan. Seventy-eight patients (11.3%) had MLN metastasis, with 48/78 (61.5%) having MLN involvement as the only site of metastasis. On multivariable analysis, presence of pT3b disease (odds ratio 4.31, 95% confidence interval 1.44-14.2; P = 0.011) was significantly associated with increased odds of MLN metastasis, whereas surgical factors (radical prostatectomy vs radiotherapy; performance/extent of pelvic nodal dissection), surgical margin positivity, and Gleason Grade were not. CONCLUSIONS: F-DCFPyL-PET/CT. pT3b disease was associated with 4.31-fold significantly increased odds of MLN metastasis. These findings suggest alternate drainage routes for PCa cells, either via alternate lymphatic drainage from the seminal vesicles themselves or secondary to direct extension from posteriorly located tumours invading the seminal vesicles.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".