Value of enhanced CT machine learning models combined with clinicoradiological characteristics in predicting lymphatic tissue metastasis in colon cancer.

Xinyi Li, Ziwei Tang, Yong Liu, Yanni Du, Yuxue Xing, Zixin Zhang, Ruming Xie
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Abstract

This study aimed to assess the effectiveness of various machine learning models in identifying lymph node metastasis in colon cancer patients and to explore the potential benefits of combining clinicoradiological and radiomics features for improved diagnosis. A total of 260 patients with pathologically confirmed colon cancer were retrospectively included in study center 1 and study center 2 from January 2015 to August 2024. A total of 198 patients with colon cancer in center 1 were randomly divided into a training set (n = 138) and an internal testing set (n = 60) at a ratio of 7:3. Patients in center 2 were included in the external testing set (n = 62). Five clinical radiological features were used to establish a clinical model. Radiomics features were extracted from the computed tomography venous phase images, and four classifiers, including logistic regression, support vector machine, decision tree, and k‑nearest neighbor, were used to build machine learning models. In addition, a combined model was constructed by joining clinical, radiological, and radiogenomic features. The performance of these models was evaluated in terms of accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), receiver operating curve (ROC) and calibration curves in the training set, internal testing set, and external testing set to determine the diagnostic model with the highest predictive efficiency and to evaluate the stability of the model. Among the four machine learning models, the SVM model had the best predictive performance, with an area under the ROC (AUC) of 0.813, 0.724, and 0.721 for the training set, internal testing set, and external testing set, respectively. The clinical model, radiomics model, and combined model had an AUC of 0.823, 0.813, 0.817, 0.508, 0.724, 0.751, 0.582, 0.721, and 0.744 in the training set, internal testing set, and external testing set, respectively. In conclusion, the combined model performed significantly better than the clinical model (p = 0.017, 0.038), but there was no significant difference between the radiomics model and the combined model (p = 0.556, 0.614).

结合临床放射学特征的增强型 CT 机器学习模型在预测结肠癌淋巴组织转移中的价值。
本研究旨在评估各种机器学习模型在识别结肠癌患者淋巴结转移方面的有效性,并探索结合临床放射学和放射组学特征改善诊断的潜在益处。自2015年1月至2024年8月,研究中心1和研究中心2共回顾性纳入了260名病理确诊的结肠癌患者。研究中心1共有198名结肠癌患者,按7:3的比例随机分为训练集(n = 138)和内部测试集(n = 60)。中心 2 的患者被纳入外部测试集(n = 62)。五个临床放射学特征用于建立临床模型。从计算机断层扫描静脉相位图像中提取放射组学特征,并使用四个分类器(包括逻辑回归、支持向量机、决策树和 k 最近邻)建立机器学习模型。此外,还结合临床、放射学和放射基因组学特征构建了综合模型。在训练集、内部测试集和外部测试集中,从准确性、灵敏度、特异性、阳性预测值(PPV)、阴性预测值(NPV)、接收者操作曲线(ROC)和校准曲线等方面评估了这些模型的性能,以确定预测效率最高的诊断模型,并评估模型的稳定性。在四种机器学习模型中,SVM 模型的预测效果最好,其训练集、内部测试集和外部测试集的 ROC 下面积(AUC)分别为 0.813、0.724 和 0.721。临床模型、放射组学模型和组合模型在训练集、内部测试集和外部测试集中的 AUC 分别为 0.823、0.813、0.817、0.508、0.724、0.751、0.582、0.721 和 0.744。总之,综合模型的表现明显优于临床模型(p = 0.017,0.038),但放射组学模型与综合模型之间没有明显差异(p = 0.556,0.614)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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