Liu Xiangchun, Yang Qi, Zhang Chunyu, Sun Jianqing, He Kan, Xie Yunming, Zhang Yiying, Fu Yu, Zhang Huimao
Department of Radiology, The First Hospital of Jilin University, Changchun, China.
Clinical Science Team, Philips Investment Co. Ltd., Shanghai, China.
Front Oncol. 2021 Feb 18;10:585767. doi: 10.3389/fonc.2020.585767. eCollection 2020.
To develop and validate a multiregional-based magnetic resonance imaging (MRI) radiomics model and combine it with clinical data for individual preoperative prediction of lymph node (LN) metastasis in rectal cancer patients.
186 rectal adenocarcinoma patients from our retrospective study cohort were randomly selected as the training (n = 123) and testing cohorts (n = 63). Spearman's rank correlation coefficient and the least absolute shrinkage and selection operator were used for feature selection and dimensionality reduction. Five support vector machine (SVM) classification models were built using selected clinical and semantic variables, single-regional radiomics features, multiregional radiomics features, and combinations, for predicting LN metastasis in rectal cancer. The performance of the five SVM models was evaluated the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity in the testing cohort. Differences in the AUCs among the five models were compared using DeLong's test.
The clinical, single-regional radiomics and multiregional radiomics models showed moderate predictive performance and diagnostic accuracy in predicting LN metastasis with an AUC of 0.725, 0.702, and 0.736, respectively. A model with improved performance was created by combining clinical data with single-regional radiomics features (AUC = 0.827, (95% CI, 0.711-0.911), = 0.016). Incorporating clinical data with multiregional radiomics features also improved the performance (AUC = 0.832 (95% CI, 0.717-0.915), = 0.015).
Multiregional-based MRI radiomics combined with clinical data can improve efficacy in predicting LN metastasis and could be a useful tool to guide surgical decision-making in patients with rectal cancer.
开发并验证一种基于多区域的磁共振成像（MRI）放射组学模型，并将其与临床数据相结合，用于直肠癌患者术前个体淋巴结（LN）转移的预测。
从我们的回顾性研究队列中随机选取186例直肠腺癌患者作为训练队列（n = 123）和测试队列（n = 63）。采用Spearman等级相关系数和最小绝对收缩与选择算子进行特征选择和降维。使用选定的临床和语义变量、单区域放射组学特征、多区域放射组学特征及其组合构建了五个支持向量机（SVM）分类模型，用于预测直肠癌中的LN转移。在测试队列中，通过受试者操作特征曲线（AUC）下面积、准确性、敏感性和特异性评估这五个SVM模型的性能。使用DeLong检验比较五个模型之间AUC的差异。
临床、单区域放射组学和多区域放射组学模型在预测LN转移方面显示出中等的预测性能和诊断准确性，AUC分别为0.725、0.702和0.736。通过将临床数据与单区域放射组学特征相结合创建了一个性能得到改善的模型（AUC = 0.827，95%CI，0.711 - 0.911，P = 0.016）。将临床数据与多区域放射组学特征相结合也提高了性能（AUC = 0.832（95%CI，0.717 - 0.915），P = 0.015）。
基于多区域的MRI放射组学与临床数据相结合可提高预测LN转移的效能，可能成为指导直肠癌患者手术决策的有用工具。
