Using machine learning to develop preoperative model for lymph node metastasis in patients with bladder urothelial carcinoma

Autor: Junjie Ji, Tianwei Zhang, Ling Zhu, Yu Yao, Jingchang Mei, Lijiang Sun, Guiming Zhang
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: BMC Cancer, Vol 24, Iss 1, Pp 1-12 (2024)
Druh dokumentu: article
ISSN: 1471-2407
DOI: 10.1186/s12885-024-12467-4
Popis: Abstract Background Lymph node metastasis (LNM) is associated with worse prognosis in bladder urothelial carcinoma (BUC) patients. This study aimed to develop and validate machine learning (ML) models to preoperatively predict LNM in BUC patients treated with radical cystectomy (RC). Methods We retrospectively collected demographic, pathological, imaging, and laboratory information of BUC patients who underwent RC and bilateral lymphadenectomy in our institution. Patients were randomly categorized into training set and testing set. Five ML algorithms were utilized to establish prediction models. The performance of each model was assessed by the area under the receiver operating characteristic curve (AUC) and accuracy. Finally, we calculated the corresponding variable coefficients based on the optimal model to reveal the contribution of each variable to LNM. Results A total of 524 and 131 BUC patients were finally enrolled into training set and testing set, respectively. We identified that the support vector machine (SVM) model had the best prediction ability with an AUC of 0.934 (95% confidence interval [CI]: 0.903–0.964) and accuracy of 0.916 in the training set, and an AUC of 0.855 (95%CI: 0.777–0.933) and accuracy of 0.809 in the testing set. The SVM model contained 14 predictors, and positive lymph node in imaging contributed the most to the prediction of LNM in BUC patients. Conclusions We developed and validated the ML models to preoperatively predict LNM in BUC patients treated with RC, and identified that the SVM model with 14 variables had the best performance and high levels of clinical applicability.
Databáze: Directory of Open Access Journals
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