ArticleDiagnostics (Basel, Switzerland)2022
A Nomogram for Predicting Prostate Cancer with Lymph Node Involvement in Robot-Assisted Radical Prostatectomy Era: A Retrospective Multicenter Cohort Study in Japan (The MSUG94 Group).
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it, 4 citations in OpenAlex.
- Lymphocele After Radical Prostatectomy: A Systematic Review of Risk Factors and Quantitative Synthesis Preventive Strategies.World journal of urology · 2026Pooled it
- Prediction models in prostate cancer: a systematic review and meta-analysis.Frontiers in oncology · 2026Pooled it
- Non-invasive prediction of lymph node involvement in prostate cancer via machine learning on whole-prostate MRI.Frontiers in oncology · 2026Article
- [A preoperative prediction model for pelvic lymph node metastasis in prostate cancer: Integrating clinical characteristics and multiparametric MRI].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2025Article
- Utility of Machine Learning Models to Predict Lymph Node Metastasis of Japanese Localized Prostate Cancer.Cancers · 2024Article
- The Negative Impact of Inflammation-Related Parameters in Prostate Cancer after Robot-Assisted Radical Prostatectomy: A Retrospective Multicenter Cohort Study in Japan (the MSUG94 Group).Journal of clinical medicine · 2023Article
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Authors and funding
18 authors at 11 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo create a nomogram for predicting prostate cancer (PCa) with lymph node involvement (LNI) in the robot-assisted radical prostatectomy (RARP) era.
methodsA retrospective multicenter cohort study was conducted on 3195 patients with PCa who underwent RARP at nine institutions in Japan between September 2012 and August 2021. A multivariable logistic regression model was used to identify factors strongly associated with LNI. The Bootstrap-area under the curve (AUC) was calculated to assess the internal validity of the prediction model.
resultsA total of 1855 patients were enrolled in this study. Overall, 93 patients (5.0%) had LNI. On multivariable analyses, initial prostate-specific antigen, number of cancer-positive and-negative biopsy cores, biopsy Gleason grade, and clinical T stage were independent predictors of PCa with LNI. The nomogram predicting PCa with LNI has been demonstrated (AUC 84%). Using a nomogram cut-off of 6%, 492 of 1855 patients (26.5%) would avoid unnecessary pelvic lymph node dissection, and PCa with LNI would be missed in two patients (0.1%). The sensitivity, specificity, and negative predictive values associated with a cutoff of 6% were 74%, 80%, and 99.6%, respectively.
conclusionsWe developed a clinically applicable nomogram for predicting the probability of patients with PCa with LNI.
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