Deep learning on T2WI to predict the muscle-invasive bladder cancer: a multi-center clinical study

• Published in: Scientific reports Vol. 15; no. 1; pp. 9942 - 9
• Main Authors: Cai, Lingkai, Yang, Xiao, Yu, Jie, Shao, Qiang, Wang, Gongcheng, Yuan, Baorui, Zhuang, Juntao, Li, Kai, Wu, Qikai, Liu, Peikun, Yu, Ruixi, Cao, Qiang, Li, Pengchao, Sun, Xueying, Zou, Yuan, Fu, Xue, Fang, Xiangming, Chen, Chunxiao, Lu, Qiang
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 22.03.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/2321; 631/67/589; Accuracy; Adult; Aged; Aged, 80 and over; Bladder cancer; Cancer; Deep Learning; Female; Humanities and Social Sciences; Humans; Invasiveness; Magnetic Resonance Imaging - methods; Male; MIBC; Middle Aged; MRI; multidisciplinary; Neoplasm Invasiveness; Predictive Value of Tests; Science; Science (multidisciplinary); Urinary Bladder - diagnostic imaging; Urinary Bladder - pathology; Urinary Bladder Neoplasms - diagnostic imaging; Urinary Bladder Neoplasms - pathology; Deep learning; MIBC; MRI; Bladder cancer

To develop a deep learning (DL) model based on MRI to predict muscle-invasive bladder cancer (MIBC). A total of 559 patients, including 521 patients in our center and 38 patients in external centers were collected from 2012 to 2023 to construct the DL model. In this study, the DL model was utilized...