Salvage lymph node dissection for prostate-specific membrane antigen (PSMA) positron emission tomography (PET)-identified oligometastatic disease
Bibliographic record
Abstract
INTRODUCTION: The availability of prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/computed tomography (CT) imaging, particularly in the setting of rising prostate-specific antigen (PSA) after definitive treatment, has led to oligometastatic prostate cancer being increasingly identified. Despite the enthusiasm surrounding treating oligometastatic disease, it has been relatively understudied. We sought to review our salvage lymphadenectomy experience in the PSMA PET/CT era. METHODS: We retrospectively reviewed patients undergoing lymphadenectomy following curative-intent primary therapy with rising PSA who had undergone a PSMA PET/CT identifying oligometastatic disease (defined as ≤5 PSMA-avid lesions) between January 2016 and April 2020. The primary endpoint was complete response, defined as achieving a PSA <0.2 ng/ml without concomitant androgen deprivation therapy (ADT). RESULTS: Twenty-two patients were included. Primary curative therapy included radical prostatectomy (86.4%) and brachytherapy (13.6%). Median PSA at salvage surgery was 1.72 ng/ml. Pelvic lymph node dissection was the most performed procedure (72.7%). Median node yield was 10.5, with a median of 1.5 positive nodes on pathology. Eight patients (36.4%) achieved PSA <0.2, with six (27.3%) remaining with PSA <0.2 after a median followup of 23.1 months. Nine (40.9%) had an initial PSA decline, but nadired ≥0.2, and in five (22.7%) the PSA rose immediately after surgery. Overall, ADT was started in seven patients (31.8%) at a median of 10.1 months post-salvage surgery. CONCLUSIONS: In our series of salvage dissection for PSMA-PET-detected nodal oligometastases, approximately a third achieved PSA <0.2; yet, it was only durable in 27%. Prospective trials of salvage nodal radiation are ongoing, however, more prospective trials of salvage node dissection are needed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".