Impact of indocyanine green‐guided extended pelvic lymph node dissection during robot‐assisted radical prostatectomy
Bibliographic record
Abstract
OBJECTIVES: To evaluate the effectiveness of indocyanine green-guided extended pelvic lymph node dissection during robot-assisted radical prostatectomy for intermediate- to high-risk prostate cancer. MATERIALS AND METHODS: After institutional review board approval, between July 2017 and December 2018, we carried out 100 indocyanine green-guided extended pelvic lymph node dissections in patients with localized intermediate- and high-risk prostate cancer, using the Firefly (Novadaq Technologies, Mississauga, ON, Canada) and da Vinci Xi surgical system (Intuitive Surgical, Sunnyvale, CA, USA). Indocyanine green was injected transrectally using ultrasound sonography before each surgery. Then, lymphatic drainage patterns and pathological findings were recorded. RESULTS: Lymphatic drainage routes were successfully determined in 91 right-sided and 90 left-sided cases. Five main lymphatic pathways and sites were identified: (i) an internal route (57%); (ii) a lateral route (50%); (iii) a presacral route (20%); (iv) a paravesical artery site (20%); and (v) a pre-prostatic site (5%). Lymph node metastasis was positive in 15 patients, with 44 pathologically confirmed metastatic lymph nodes. Metastatic fluorescent lymph nodes were found in 15 out of 44 lymph nodes (34.1%). For sentinel lymph node identification, the 34% sensitivity and 64.8% specificity rates were detected in regard to identification of lymph node metastasis. CONCLUSIONS: Lymphatic drainage patterns from the prostate can be identified and classified using indocyanine green-guided extended pelvic lymph node dissections. Although the direct role of fluorescent lymph nodes in sentinel lymph node identification appears to be limited, the identification of lymphatic pathways could contribute to high-quality extended pelvic lymph node dissection during robot-assisted radical prostatectomy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".