Zhang Hongwei, Wang Kexin, Liu Shurong, Chen Guowei, Jiang Yong, Wu Yingchao, Pang Xiaocong, Wang Xiaoying, Zhang Junling, Wang Xin
Department of Gastrointestinal Surgery, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034, China.
School of Basic Medical Sciences, Capital Medical University, No. 10, Xitoutiao, Youanmenwai Street, Fengtai District, Beijing, 100069, China.
Cancer Imaging. 2025 Apr 29;25(1):57. doi: 10.1186/s40644-025-00876-y.
The appropriate ligation level of the inferior mesenteric artery (IMA) in left-sided colorectal cancer (CRC) surgery is debated, with metastasis in No. 253 lymph node (No. 253 LN) being a key determining factor. This study aimed to develop a machine learning model for predicting metastasis in No. 253 LN.
We retrospectively collected clinical data from 2,118 patients with left-sided CRC and contrast-enhanced CT images from 310 of these patients. From this data, a test set, a training set, and a temporal validation set were constructed. Logistic regression models were used to develop a clinical model, a CT model, and a radiomics model, which were then integrated into a combined model using logical rules. Finally, these models were evaluated using metrics such as the area under the receiver operating characteristic curve (AUC), precision-recall (PR) curves, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).
A clinical model, a CT model, and a radiomics model were constructed using univariate logistic regression. A combined model was developed by integrating the clinical, CT, and radiomics models, with positivity defined as all three models being positive at a 90% sensitivity threshold. The clinical model included six predictive factors: tumor site, endoscopic obstruction, CEA levels, growth type, differentiation grade, and pathological classification. The CT model utilized largest lymph node average CT value, short-axis diameter and long-axis diameter. The radiomics model incorporated maximum gray level intensity within the region of interest, large area high gray level emphasis, small area high gray level emphasis and surface area to volume ratio. In the test set, the AUCs for the clinical, CT, radiomics, and combined models were 0.694, 0.663, 0.72, and 0.663, respectively, while in the temporal validation set, they were 0.743, 0.629, 0.716, and 0.8. Specifically, the combined model demonstrated a sensitivity of 0.8 and a specificity of 0.8 in the temporal validation set. By comparing the PR and DCA curves, the combined model demonstrated better performance. Additionally, the combined model showed moderate improvements in INR and IDI compared to other models.
A clinical and CT-based radiomics model shows promise in predicting No. 253 LN metastasis in left-sided CRC and provides insights for optimizing IMA ligation strategies.
在左侧结直肠癌（CRC）手术中，肠系膜下动脉（IMA）的合适结扎水平存在争议，253号淋巴结（No. 253 LN）转移是一个关键决定因素。本研究旨在开发一种用于预测No. 253 LN转移的机器学习模型。
我们回顾性收集了2118例左侧CRC患者的临床数据以及其中310例患者的对比增强CT图像。基于这些数据，构建了一个测试集、一个训练集和一个时间验证集。使用逻辑回归模型开发了一个临床模型、一个CT模型和一个放射组学模型，然后使用逻辑规则将它们整合为一个联合模型。最后，使用受试者操作特征曲线下面积（AUC）、精确召回率（PR）曲线、决策曲线分析（DCA）、净重新分类改善（NRI）和综合鉴别改善（IDI）等指标对这些模型进行评估。
使用单变量逻辑回归构建了临床模型、CT模型和放射组学模型。通过整合临床、CT和放射组学模型开发了一个联合模型，将阳性定义为在90%敏感性阈值下所有三个模型均为阳性。临床模型包括六个预测因素：肿瘤部位、内镜梗阻、癌胚抗原（CEA）水平、生长类型、分化程度和病理分类。CT模型利用最大淋巴结平均CT值、短轴直径和长轴直径。放射组学模型纳入了感兴趣区域内的最大灰度强度、大面积高灰度强调、小面积高灰度强调和表面积与体积比。在测试集中，临床、CT、放射组学和联合模型的AUC分别为0.694、0.663、0.72和0.663，而在时间验证集中，它们分别为0.743、0.629、0.716和0.8。具体而言，联合模型在时间验证集中显示出0.8的敏感性和0.8的特异性。通过比较PR和DCA曲线，联合模型表现出更好的性能。此外，与其他模型相比，联合模型在INR和IDI方面显示出适度改善。
基于临床和CT的放射组学模型在预测左侧CRC中No. 253 LN转移方面显示出前景，并为优化IMA结扎策略提供了见解。
