Development of a CT radiomics model for detection of bladder invasion by colorectal carcinoma

• Published in: Scientific reports Vol. 15; no. 1; pp. 15389 - 11
• Main Authors: Wang, Jingui, Wang, Kexin, Zhang, Junling, Wu, Yingchao, Jiang, Yong, Chen, Guowei, Liu, Zhanbing, Wu, Tao, Wan, Yuanlian, Wang, Xiaoying, Wang, Xin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.05.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/1504/1885; 631/67/70; Adult; Aged; Aged, 80 and over; Bladder; Cancer; Colorectal cancer; Colorectal carcinoma; Colorectal Neoplasms - diagnostic imaging; Colorectal Neoplasms - pathology; Computed tomography; Datasets; Female; Humanities and Social Sciences; Humans; Invasion; Male; Middle Aged; multidisciplinary; Neoplasm Invasiveness; Radiomics; ROC Curve; Science; Science (multidisciplinary); Tomography, X-Ray Computed - methods; Training; Urinary Bladder - diagnostic imaging; Urinary Bladder - pathology; Urinary Bladder Neoplasms - diagnostic imaging; Urinary Bladder Neoplasms - pathology; Urinary Bladder Neoplasms - secondary; Bladder; Computed tomography; Invasion; Colorectal cancer; Radiomics
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-99222-2

To investigate the feasibility of a radiomics model for the detection of bladder invasion (BI) by colorectal cancer (CRC) on CT images. Ninety-six patients with CRC and a suspicion of BI who underwent tumor resection with partial or total cystectomy were reviewed. The 96 patients were randomly assigned to the training dataset ( n = 68) or test dataset ( n = 28) at a ratio of 7:3. The CT images were reviewed by two experienced radiologists, who provided a CT impression of the invasion of the bladder by CRC. A region of interest (ROI) on the CT images for each case was manually labeled by two radiologists. A radiomics model was constructed using a Categorical Boosting (CatBoost) classifier. The predicted probability by CatBoost was used to evaluate the efficacy of the radiomics model. The areas under the curve (AUCs) of the receiver operating characteristic were compared between the radiomics model and the CT impression. In the training dataset, the AUC of the radiomic model [0.864 (95% CI: 0.778, 0.951)] was significantly greater than that of CT impression [0.678 (95% CI: 0.569. 0.786), P = 0.007]. In the test dataset, the AUC of the radiomic model [0.883 (95% CI: 0.699, 1.000)] was also significantly greater than that of CT impression [0.570 (95% CI: 0.370, 0.770), P = 0.040]. It is feasible to use radiomics models for the prediction of BI by CRC, which might perform better than human radiologists.