Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer

作者全名："Wei, Zongjie; Liu, Huayun; Xv, Yingjie; Liao, Fangtong; He, Quanhao; Xie, Yongpeng; Lv, Fajin; Jiang, Qing; Xiao, Mingzhao"

作者地址："[Wei, Zongjie; Liu, Huayun; Xv, Yingjie; Liao, Fangtong; He, Quanhao; 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; [Jiang, Qing] Chongqing Med Univ, Affiliated Hosp 2, Dept Urol, Chongqing, 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."

来源：HELIYON

ESI学科分类：　

WOS号：WOS:001167623900001

JCR分区：Q2

影响因子：4

年份：2024

卷号：10

期号：2

开始页：　

结束页：　

文献类型：Article

关键词：Radiomics; Deep learning; Bladder cancer; Computed tomography; Nomogram

摘要："Objective: This study aimed to develop a nomogram combining CT-based handcrafted radiomics and deep learning (DL) features to preoperatively predict muscle invasion in bladder cancer (BCa) with multi -center validation. Methods: In this retrospective study, 323 patients underwent radical cystectomy with pathologically confirmed BCa were enrolled and randomly divided into the training cohort (n = 226) and internal validation cohort (n = 97). And fifty-two patients from another independent medical center were enrolled as an independent external validation cohort. Handcrafted radiomics and DL features were constructed from preoperative nephrographic phase CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in train cohort. Multivariate logistic regression was used to develop the predictive model and a deep learning radiomics nomogram (DLRN) was constructed. The predictive performance of models was evaluated by area under the curves (AUC) in the three cohorts. The calibration and clinical usefulness of DLRN were estimated by calibration curve and decision curve analysis. Results: The nomogram that incorporated radiomics signature and DL signature demonstrated satisfactory predictive performance for differentiating non-muscle invasive bladder cancer (NMIBC) from muscle invasive bladder cancer (MIBC), with an AUC of 0.884 (95 % CI: 0.813-0.953) in internal validation cohort and 0.862 (95 % CI: 0.756-0.968) in external validation cohort, respectively. Decision curve analysis confirmed the clinical usefulness of the nomogram. Conclusions: A CT-based deep learning radiomics nomogram exhibited a promising performance for preoperative prediction of muscle invasion in bladder cancer, and may be helpful in the clinical decision-making process."

基金机构：Chongqing Talent Program [CQYC202003]

基金资助正文：This work was supported by the Chongqing Talent Program [grant number CQYC202003] .