Predicting lymph node invasion in patients treated with robot-assisted radical prostatectomy

Area of Science:

• Urology
• Oncology
• Surgical Innovation

Background:

• Robot-assisted radical prostatectomy (RARP) is a common treatment for prostate cancer.
• Accurate prediction of lymph node invasion (LNI) is crucial for treatment planning.
• Current predictive models may not fully capture LNI risk in contemporary RARP patients.

Purpose of the Study:

• To develop and validate a novel nomogram for predicting LNI in patients undergoing RARP.
• To compare the performance of the novel nomogram against existing models.

Main Methods:

• Analysis of 2,007 patients treated with RARP and pelvic lymph node dissection (PLND).
• Logistic regression to identify independent predictors of LNI.
• Development of a nomogram using PSA, clinical stage, and Gleason scores.
• Evaluation of nomogram performance using discrimination, calibration, and decision-curve analysis.

Main Results:

• 5.3% of patients had LNI; median 6 lymph nodes removed.
• Key predictors for LNI: PSA, clinical stage, primary and secondary Gleason scores.
• The novel nomogram demonstrated 86.2% discrimination and optimal calibration.
• A 2% nomogram cutoff could spare 58% of patients from PLND while missing only 9.4% of LNI cases.

Conclusions:

• A significant minority of contemporary RARP patients have LNI.
• The developed nomogram accurately identifies patients with LNI.
• This tool can improve patient selection and reduce unnecessary PLND procedures.