Yu Xin-Ping, Wang Lei, Yu Hai-Yang, Zou Yu-Wei, Wang Chang, Jiao Jin-Wen, Hong Hao, Zhang Shuai
Department of Gynecology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, People's Republic of China.
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong Province, People's Republic of China.
Cancer Manag Res. 2021 Jan 12;13:329-336. doi: 10.2147/CMAR.S284220. eCollection 2021.
To investigate whether multidetector computed tomography (MDCT)-based radiomics features can discriminate between serous borderline ovarian tumors (SBOTs) and serous malignant ovarian tumors (SMOTs).
Eighty patients with SBOTs and 102 patients with SMOTs, confirmed by pathology (training set: n = 127; validation set: n = 55) from December 2017 to June 2020, were enrolled in this study. The interclass correlation coefficient (ICC) and least absolute shrinkage and selection operator (LASSO) regression were applied to select radiomics parameters derived from MDCT images on the arterial phase (AP), venous phase (VP), and equilibrium phase (EP). Receiver operating characteristic (ROC) analysis of each selected parameter was carried out. Heat maps were created to illustrate the distribution of the radiomics parameters. Three models incorporating selected radiomics parameters generated by support vector machine (SVM) classifiers in each phase were analyzed by ROC and compared using the DeLong test.
The most predictive features selected by ICC and LASSO regression between SBOTs and SMOTs included 9 radiomics parameters on AP, VP, and EP each. Three models on AP, VP, and EP incorporating the selected features generated by SVM classifiers produced AUCs of 0.80 (accuracy, 0.75; sensitivity, 0.74; specificity, 0.75), 0.86 (accuracy, 0.78; sensitivity, 0.80; specificity, 0.75), and 0.73 (accuracy, 0.69; sensitivity, 0.71; specificity, 0.67), respectively. There were no significant differences in the AUCs among the three models (AP vs. VP, P = 0.199; AP vs. EP, P = 0.260; VP vs. EP, P = 0.793).
MDCT-based radiomics features could be used as biomarkers for the differentiation of SBOTs and SMOTs.
探讨基于多排螺旋计算机断层扫描（MDCT）的影像组学特征能否区分浆液性交界性卵巢肿瘤（SBOT）和浆液性恶性卵巢肿瘤（SMOT）。
纳入2017年12月至2020年6月期间经病理确诊的80例SBOT患者和102例SMOT患者（训练集：n = 127；验证集：n = 55）。应用组内相关系数（ICC）和最小绝对收缩与选择算子（LASSO）回归来选择从动脉期（AP）、静脉期（VP）和平衡期（EP）的MDCT图像中提取的影像组学参数。对每个选定参数进行受试者操作特征（ROC）分析。绘制热图以说明影像组学参数的分布。分析由支持向量机（SVM）分类器在每个阶段生成的包含选定影像组学参数的三个模型，并使用德龙检验进行比较。
ICC和LASSO回归在SBOT和SMOT之间选择的最具预测性的特征包括AP、VP和EP各9个影像组学参数。由SVM分类器生成的包含选定特征的AP、VP和EP三个模型的曲线下面积（AUC）分别为0.80（准确率，0.75；灵敏度，0.74；特异度，0.75）、0.86（准确率，0.78；灵敏度，0.80；特异度，0.75）和0.73（准确率，0.69；灵敏度，0.71；特异度，0.67）。三个模型的AUC之间无显著差异（AP与VP，P = 0.199；AP与EP，P = 0.260；VP与EP，P = 0.793）。
基于MDCT的影像组学特征可作为区分SBOT和SMOT的生物标志物。
