A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study

作者全名：Wei, Zongjie; Xv, Yingjie; Liu, Huayun; Li, Yang; Yin, Siwen; Xie, Yongpeng; Chen, Yong; Lv, Fajin; Jiang, Qing; Li, Feng; Xiao, Mingzhao

作者地址：[Wei, Zongjie; Xv, Yingjie; Liu, Huayun; Li, Yang; Xie, Yongpeng; Xiao, Mingzhao] Chongqing Med Univ, Affiliated Hosp 1, Dept Urol, 1 Youyi Rd, Chongqing 400016, Peoples R China; [Lv, Fajin] Chongqing Med Univ, Affiliated Hosp 1, Dept Radiol, Chongqing, Peoples R China; [Yin, Siwen; Chen, Yong] Chongqing Univ, Fuling Hosp, Dept Urol, Chongqing, Peoples R China; [Jiang, Qing] Chongqing Med Univ, Affiliated Hosp 2, Dept Urol, Chongqing, Peoples R China; [Li, Feng] Chongqing Univ, Three Gorges Hosp, Dept Urol, 165 Xincheng Rd, Chongqing 404100, Peoples R China

通信作者：Xiao, MZ (通讯作者)，Chongqing Med Univ, Affiliated Hosp 1, Dept Urol, 1 Youyi Rd, Chongqing 400016, Peoples R China.; Li, F (通讯作者)，Chongqing Univ, Three Gorges Hosp, Dept Urol, 165 Xincheng Rd, Chongqing 404100, Peoples R China.

来源：INTERNATIONAL JOURNAL OF SURGERY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001223156300063

JCR分区：Q1

影响因子：12.5

年份：2024

卷号：110

期号：5

开始页：2922

结束页：2932

文献类型：Article

关键词：deep learning; muscle invasive bladder cancer; survival prediction; tomography; X-ray computed

摘要：Background: Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy (RC). Postoperative survival stratification based on radiomics and deep learning (DL) algorithms may be useful for treatment decision-making and follow-up management. This study was aimed to develop and validate a DL model based on preoperative computed tomography (CT) for predicting postcystectomy overall survival (OS) in patients with MIBC. Methods: MIBC patients who underwent RC were retrospectively included from four centers, and divided into the training, internal validation, and external validation sets. A DL model incorporated the convolutional block attention module (CBAM) was built for predicting OS using preoperative CT images. The authors assessed the prognostic accuracy of the DL model and compared it with classic handcrafted radiomics model and clinical model. Then, a deep learning radiomics nomogram (DLRN) was developed by combining clinicopathological factors, radiomics score (Rad-score) and deep learning score (DL-score). Model performance was assessed by C-index, KM curve, and time-dependent ROC curve. Results: A total of 405 patients with MIBC were included in this study. The DL-score achieved a much higher C-index than Rad-score and clinical model (0.690 vs. 0.652 vs. 0.618 in the internal validation set, and 0.658 vs. 0.601 vs. 0.610 in the external validation set). After adjusting for clinicopathologic variables, the DL-score was identified as a significantly independent risk factor for OS by the multivariate Cox regression analysis in all sets (all P<0.01). The DLRN further improved the performance, with a C-index of 0.713 (95% CI: 0.627-0.798) in the internal validation set and 0.685 (95% CI: 0.586-0.765) in external validation set, respectively. Conclusions: A DL model based on preoperative CT can predict survival outcome of patients with MIBC, which may help in risk stratification and guide treatment decision-making and follow-up management.

基金机构：Chongqing Talent Program [CQYC202003]

基金资助正文：This study was supported by the Chongqing Talent Program(grant number: CQYC202003).