Wang Jingui, Wang Kexin, Zhang Junling, Wu Yingchao, Jiang Yong, Chen Guowei, Liu Zhanbing, Wu Tao, Wan Yuanlian, Wang Xiaoying, Wang Xin
Department of Gastrointestinal Surgery, Peking University First Hospital, NO. 8 Xishiku Street, Xicheng District, Beijing, 100034, China.
Department of Radiology, Peking University First Hospital, NO. 8 Xishiku Street, Xicheng District, Beijing, 100034, China.
Sci Rep. 2025 May 2;15(1):15389. 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.
探讨基于CT图像的结直肠癌（CRC）膀胱侵犯（BI）的影像组学模型的可行性。回顾性分析96例疑似BI的CRC患者，这些患者均接受了部分或全膀胱切除术的肿瘤切除手术。按照7:3的比例将96例患者随机分为训练数据集（n = 68）和测试数据集（n = 28）。由两名经验丰富的放射科医生对CT图像进行评估，他们给出了关于CRC侵犯膀胱的CT诊断意见。两名放射科医生对每个病例的CT图像上的感兴趣区域（ROI）进行手动标注。使用分类提升（CatBoost）分类器构建影像组学模型。利用CatBoost预测的概率来评估影像组学模型的效能。比较影像组学模型和CT诊断意见的受试者操作特征曲线下面积（AUC）。在训练数据集中，影像组学模型的AUC为[0.864（95%CI：0.778，0.951）]，显著高于CT诊断意见的AUC[0.678（95%CI：0.569，0.786），P = 0.007]。在测试数据集中，影像组学模型的AUC为[0.883（95%CI：0.699，1.000）]，也显著高于CT诊断意见的AUC[0.570（95%CI：0.370，0.770），P = 0.040]。利用影像组学模型预测CRC的BI是可行的，其表现可能优于放射科医生。
