ArticleJournal of cancer research and clinical oncology2023
Development and validation of a preoperative nomogram to predict lymph node metastasis in patients with bladder urothelial carcinoma.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- A Clinically Practical Nomogram for Predicting Survival in Elderly Patients (≥ 65 Years) With Bladder Urothelial Carcinoma: A Study Based on SEER Database and External Validation.Cancer reports (Hoboken, N.J.) · 2025Article
- Using machine learning to develop preoperative model for lymph node metastasis in patients with bladder urothelial carcinoma.BMC cancer · 2024Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
purposePredicting lymph node metastasis (LNM) in patients with bladder urothelial carcinoma (BUC) before radical cystectomy aids clinical decision making. Here, we aimed to develop and validate a nomogram to preoperatively predict LNM in BUC patients.
methodsPatients with histologically confirmed BUC, who underwent radical cystectomy and bilateral lymphadenectomy, were retrospectively recruited from two institutions. Patients from one institution were enrolled in the primary cohort, while those from the other were enrolled in the external validation cohort. Patient demographic, pathological (using transurethral resection of the bladder tumor specimens), imaging, and laboratory data were recorded. Univariate and multivariate logistic regression analyses were performed to explore the independent preoperative risk factors and develop the nomogram. Internal and external validation was conducted to assess nomogram performance.
results522 and 215 BUC patients were enrolled in the primary and external validation cohorts, respectively. We identified tumor grade, infiltration, extravesical invasion, LNM on imaging, tumor size, and serum creatinine levels as independent preoperative risk factors, which were subsequently used to develop the nomogram. The nomogram showed a good predictive accuracy, with area under the receiver operator characteristic curve values of 0.817 and 0.825 for the primary and external validation cohorts, respectively. The corrected C-indexes, calibration curves (after 1000 bootstrap resampling), decision curve analysis results, and clinical impact curves demonstrated that the nomogram performed well in both cohorts and was highly clinically applicable.
conclusionWe developed a nomogram to preoperatively predict LNM in BUC, which was highly accurate, reliable, and clinically applicable.
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