直肠癌侧方淋巴结转移的诊断标准尚未建立。本研究旨在探讨侧方淋巴结转移的危险因素，并开发结合这些危险因素的机器学习模型，以提高标准影像的诊断性能。这项多中心前瞻性研究纳入了术前未接受直肠癌治疗的患者进行侧方淋巴结清扫术。 2017年和2019年在15个日本机构进行。首先，使用多变量分析评估术前临床病理因素和磁共振成像结果与侧淋巴结转移的相关性。接下来，结合这些危险因素，开发了侧淋巴结转移的机器学习诊断模型。这些模型在训练集和内部验证队列中进行了测试，并使用受试者工作特征曲线分析来测试其诊断性能。在 212 例直肠癌中，选择了 122 名患者，其中 232 侧骨盆侧，其中 30 侧有病理侧侧淋巴结转移。多因素分析显示，低分化/粘液性腺癌、壁外血管侵犯、肿瘤沉积、外侧淋巴结短轴直径≥6.0 mm是外侧淋巴结转移的独立危险因素。患者被随机分为训练队列（139侧）和测试队列（93侧），并根据显着特征​​（包括：组织学类型、壁外血管侵犯、肿瘤沉积、短时间）的组合计算机器学习模型。侧淋巴结长轴直径、体重指数、血清癌胚抗原水平、cT、cN、cM、不规则边界和混合信号强度）。训练队列中灵敏度最高的三个模型如下：支持向量机（灵敏度，1.000；特异性，0.773）、光梯度增强机（灵敏度，0.950；特异性，0.918）和集成学习（灵敏度，0.950；特异性，0.918）。特异性，0.917）。测试队列中这些模型的诊断性能如下：支持向量机（灵敏度，0.750；特异性，0.667）、光梯度增强机（灵敏度，0.500；特异性，0.852）和集成学习（灵敏度，0.667；特异性， 0.864)。结合多种风险因素的机器学习模型有助于提高侧淋巴结转移的诊断性能。© 作者 2024。由牛津大学出版社代表 BJS Foundation Ltd 出版。

The diagnostic criteria for lateral lymph node metastasis in rectal cancer have not been established. This research aimed to investigate the risk factors for lateral lymph node metastasis and develop machine learning models combining these risk factors to improve the diagnostic performance of standard imaging.This multicentre prospective study included patients who underwent lateral lymph node dissection without preoperative treatment for rectal cancer between 2017 and 2019 in 15 Japanese institutions. First, preoperative clinicopathological factors and magnetic resonance imaging findings were evaluated using multivariable analyses for their correlation with lateral lymph node metastasis. Next, machine learning diagnostic models for lateral lymph node metastasis were developed combining these risk factors. The models were tested in a training set and in an internal validation cohort and their diagnostic performance was tested using receiver operating characteristic curve analyses.Of 212 rectal cancers, 122 patients were selected, including 232 lateral pelvic sides, 30 sides of which had pathological lateral lymph node metastasis. Multivariable analysis revealed that poorly differentiated/mucinous adenocarcinoma, extramural vascular invasion, tumour deposit and a short-axis diameter of lateral lymph node ≥ 6.0 mm were independent risk factors for lateral lymph node metastasis. Patients were randomly divided into a training cohort (139 sides) and a test cohort (93 sides) and machine learning models were computed on the basis of a combination of significant features (including: histological type, extramural vascular invasion, tumour deposit, short- and long-axis diameter of lateral lymph node, body mass index, serum carcinoembryonic antigen level, cT, cN, cM, irregular border and mixed signal intensity). The top three models with the highest sensitivity in the training cohort were as follows: support vector machine (sensitivity, 1.000; specificity, 0.773), light gradient boosting machine (sensitivity, 0.950; specificity, 0.918) and ensemble learning (sensitivity, 0.950; specificity, 0.917). The diagnostic performances of these models in the test cohort were as follows: support vector machine (sensitivity, 0.750; specificity, 0.667), light gradient boosting machine (sensitivity, 0.500; specificity, 0.852) and ensemble learning (sensitivity, 0.667; specificity, 0.864).Machine learning models combining multiple risk factors can contribute to improving diagnostic performance of lateral lymph node metastasis.© The Author(s) 2024. Published by Oxford University Press on behalf of BJS Foundation Ltd.