Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Shandong Key Laboratory of Digital Medicine and Computer Assisted Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
BMC Cancer. 2024 Jun 13;24(1):725. doi: 10.1186/s12885-024-12467-4.
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).
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.
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.
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.
淋巴结转移（LNM）与接受根治性膀胱切除术（RC）治疗的膀胱癌患者的预后较差相关。本研究旨在开发和验证机器学习（ML）模型，以预测接受 RC 和双侧淋巴结清扫术的膀胱癌患者的 LNM。
我们回顾性收集了在我院接受 RC 和双侧淋巴结清扫术的膀胱癌患者的人口统计学、病理学、影像学和实验室信息。患者被随机分为训练集和测试集。使用五种 ML 算法建立预测模型。通过接受者操作特征曲线下面积（AUC）和准确性评估每个模型的性能。最后，我们根据最优模型计算了每个变量的相应系数，以揭示每个变量对 LNM 的贡献。
共有 524 例和 131 例膀胱癌患者最终分别纳入训练集和测试集。我们发现支持向量机（SVM）模型具有最佳的预测能力，在训练集中 AUC 为 0.934（95%置信区间[CI]：0.903-0.964），准确率为 0.916，在测试集中 AUC 为 0.855（95%CI：0.777-0.933），准确率为 0.809。SVM 模型包含 14 个预测因子，影像学中阳性淋巴结对膀胱癌患者 LNM 预测的贡献最大。
我们开发并验证了 ML 模型，以预测接受 RC 治疗的膀胱癌患者的 LNM，并确定了包含 14 个变量的 SVM 模型具有最佳性能和高水平的临床适用性。
