Salvage Ultrasound-Guided Robot-Assisted Video-Endoscopic Inguinal Lymphadenectomy (RAVEIL) as a Metastasis-Directed Therapy (MDT) in Oligoprogressive Metastatic Castration-Resistant Prostate Cancer (mCRPC): A Case Report and Review of the Literature
Bibliographic record
Abstract
Background: Metastatic castration-resistant prostate cancer (mCRPC) remains challenging due to progression despite androgen deprivation therapy (ADT). Current treatments, including androgen receptor-targeted agents, chemotherapy, bone-targeted agents, and PARP inhibitors, extend survival but face challenges, such as resistance, adverse effects, and limited durability. Metastasis-directed therapies (MDTs), such as stereotactic ablative radiotherapy (SABR), show promise in oligometastatic disease, but their role in oligoprogressive mCRPC is unclear. Salvage lymphadenectomy is rarely pursued due to invasiveness and limited data. This is the first report of robotic surgery as an MDT in this setting, demonstrating the potential of salvage robot-assisted video-endoscopic inguinal lymphadenectomy (RAVEIL) to manage oligoprogressive mCRPC and delay systemic progression. Methods: A 47-year-old male with metastatic hormone-sensitive prostate cancer (Gleason 10) underwent ADT, docetaxel chemotherapy, and radical retropubic prostatectomy with super-extended pelvic and retroperitoneal lymphadenectomy. Upon progression to oligoprogressive mCRPC, 68Ga-PSMA PET/CT detected a single metastatic inguinal lymph node. Salvage RAVEIL was performed using the da Vinci X™ Surgical System, guided by preoperative ultrasound mapping. Results: Histopathology confirmed metastasis in one of the eight excised lymph nodes. The patient achieved undetectable PSA levels and prolonged biochemical progression-free survival. Minor complications (lymphorrhea, cellulitis) resolved without sequelae. No further progression was observed for over 14 months. Conclusions: This case highlights RAVEIL as a viable MDT option for oligoprogressive mCRPC, potentially extending progression-free intervals while minimizing systemic treatment.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".