Wang Bin, Gong Zijian, Su Peide, Zhen Guanghao, Zeng Tao, Ye Yinquan
The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Department of Urology, Second Affiliated Hospital of Nanchang University, Nanchang, China.
BMC Cancer. 2025 Jul 1;25(1):1116. doi: 10.1186/s12885-025-14279-6.
OBJECTIVE: This study aims to construct a survival prognosis prediction model for muscle-invasive bladder cancer based on CT imaging features. MATERIALS AND METHODS: A total of 91 patients with muscle-invasive bladder cancer were sourced from the TCGA and TCIA dataset and were divided into a training group (64 cases) and a validation group (27 cases). Additionally, 54 patients with muscle-invasive bladder cancer were retrospectively collected from our hospital to serve as an external test group; their enhanced CT imaging data were analyzed and processed to identify the most relevant radiomic features. Five distinct machine learning methods were employed to develop the optimal radiomics model, which was then combined with clinical data to create a nomogram model aimed at accurately predicting the overall survival (OS) of patients with muscle-invasive bladder cancer. The model's performance was ultimately assessed using various evaluation methods, including the ROC curve, calibration curve, decision curve, and Kaplan-Meier (KM) analysis. RESULTS: Eight radiomic features were identified for modeling analysis. Among the models evaluated, the Gradient Boosting Machine (GBM) In the prediction of OS performed the best. the 2-year AUCs were 0.859, 95% CI (0.767-0.952) for the training group, 0.850, 95% CI (0.705-0.995) for the validation group, and 0.700, 95% CI (0.520-0.880) for the external test group. The 3-year AUCs were 0.809, 95% CI (0.704-0.913) for the training group, 0.895, 95% CI (0.768-1.000) for the validation group, and 0.730, 95% CI (0.569-0.891) for the external test group. The nomogram model incorporating clinical data achieved superior results, the AUCs for predicting 2-year OS were 0.913 (95% CI: 0.83-0.98) for the training group, 0.86 (95% CI: 0.78-0.96) for the validation group, and 0.778 (95% CI: 0.69-0.94) for the external test group; for predicting 3-year OS, the AUCs were 0.837 (95% CI: 0.83-0.98) for the training group, 0.982 (95% CI: 0.84-1.0) for the validation group, and 0.785 (95% CI: 0.75-0.96) for the external test group. The calibration curve demonstrated excellent calibration of the model, while the decision curve and KM analysis indicated that the model possesses substantial clinical utility. CONCLUSION: The GBM model, based on the radiomic features of enhanced CT imaging, holds significant potential for predicting the prognosis of patients with muscle-invasive bladder cancer. Furthermore, the combined model, which incorporates clinical features, demonstrates enhanced performance and is beneficial for clinical decision-making.
目的：本研究旨在基于CT成像特征构建肌肉浸润性膀胱癌的生存预后预测模型。 材料与方法：从TCGA和TCIA数据集中选取91例肌肉浸润性膀胱癌患者，分为训练组（64例）和验证组（27例）。另外，回顾性收集我院54例肌肉浸润性膀胱癌患者作为外部测试组；对其增强CT成像数据进行分析处理，以确定最相关的影像组学特征。采用五种不同的机器学习方法构建最佳影像组学模型，然后将其与临床数据相结合创建列线图模型，旨在准确预测肌肉浸润性膀胱癌患者的总生存期（OS）。最终使用包括ROC曲线、校准曲线、决策曲线和Kaplan-Meier（KM）分析在内的各种评估方法对模型性能进行评估。 结果：确定了8个影像组学特征用于建模分析。在评估的模型中，梯度提升机（GBM）在OS预测方面表现最佳。训练组2年AUC为0.859，95%CI（0.767-0.952）；验证组为0.850，95%CI（0.705-0.995）；外部测试组为0.700，95%CI（0.520-0.880）。训练组3年AUC为0.809，95%CI（0.704-0.913）；验证组为0.895，95%CI（0.768-1.000）；外部测试组为0.730，95%CI（0.569-0.891）。纳入临床数据的列线图模型取得了更好的结果，训练组预测2年OS的AUC为0.913（95%CI：0.83-0.98），验证组为0.86（95%CI：0.78-0.96），外部测试组为0.778（95%CI：0.69-0.94）；预测3年OS的AUC，训练组为0.837（95%CI：0.83-0.98），验证组为0.982（95%CI：0.84-1.0），外部测试组为0.785（95%CI：0.75-0.96）。校准曲线显示模型校准良好，决策曲线和KM分析表明该模型具有较大的临床实用性。 结论：基于增强CT成像的影像组学特征的GBM模型在预测肌肉浸润性膀胱癌患者预后方面具有巨大潜力。此外，结合临床特征的联合模型表现更优，有利于临床决策。
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