直肠癌侧淋巴结转移的风险因素和机器学习诊断模型的开发:多中心研究。

IF 3.5 3区 医学 Q1 SURGERY BJS Open Pub Date : 2024-07-02 DOI:10.1093/bjsopen/zrae073
Shunsuke Kasai, Akio Shiomi, Hideyuki Shimizu, Monami Aoba, Yusuke Kinugasa, Takuya Miura, Kay Uehara, Jun Watanabe, Kazushige Kawai, Yoichi Ajioka
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引用次数: 0

摘要

背景:直肠癌侧淋巴结转移的诊断标准尚未确立。本研究旨在调查侧淋巴结转移的风险因素,并结合这些风险因素开发机器学习模型,以提高标准成像的诊断性能:这项多中心前瞻性研究纳入了2017年至2019年间在日本15家机构接受侧淋巴结清扫术而未进行术前治疗的直肠癌患者。首先,通过多变量分析评估了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。然后,结合这些风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组中进行了测试,并通过接收器操作特征曲线分析检验了它们的诊断性能:结果:在212例直肠癌中,有122例患者被选中,包括232例盆腔侧位癌,其中30例有病理侧位淋巴结转移。多变量分析显示,分化差/粘液腺癌、壁外血管侵犯、肿瘤沉积和侧淋巴结短轴直径≥6.0毫米是侧淋巴结转移的独立危险因素。患者被随机分为训练组(139例)和测试组(93例),并根据重要特征(包括:组织学类型、壁外血管侵犯、肿瘤沉积、侧淋巴结短轴和长轴直径、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)的组合计算机器学习模型。训练队列中灵敏度最高的前三个模型如下:支持向量机(灵敏度 1.000;特异性 0.773)、轻梯度提升机(灵敏度 0.950;特异性 0.918)和集合学习(灵敏度 0.950;特异性 0.917)。这些模型在测试队列中的诊断表现如下:支持向量机(灵敏度,0.750;特异性,0.667)、轻梯度提升机(灵敏度,0.500;特异性,0.852)和集合学习(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。
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Risk factors and development of machine learning diagnostic models for lateral lymph node metastasis in rectal cancer: multicentre study.

Background: 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.

Method: 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.

Results: 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).

Conclusion: Machine learning models combining multiple risk factors can contribute to improving diagnostic performance of lateral lymph node metastasis.

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来源期刊
BJS Open
BJS Open SURGERY-
CiteScore
6.00
自引率
3.20%
发文量
144
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