Gao Xin-Yi, Wang Yi-Da, Wu Shi-Man, Rui Wen-Ting, Ma De-Ning, Duan Yi, Zhang An-Ni, Yao Zhen-Wei, Yang Guang, Yu Yan-Ping
Institute of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, People's Republic of China.
Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences, Hangzhou, People's Republic of China.
Cancer Manag Res. 2020 May 7;12:3191-3201. doi: 10.2147/CMAR.S244262. eCollection 2020.
We propose three support vector machine (SVM) classifiers, using pre-and post-contrast T2 fluid-attenuated inversion recovery (FLAIR) subtraction and/or pre-and post-contrast T1WI subtraction, to differentiate treatment-related effects (TRE) from glioma recurrence.
Fifty-six postoperative high-grade glioma patients with suspicious progression after radiotherapy and chemotherapy from two centers were studied. Pre-and post-contrast T1WI and T2 FLAIR were collected. Each pre-contrast image was voxel-wise subtracted from the co-registered post-contrast image. Dataset was randomly split into training, and testing on a 7:3 ratio, accordingly subjected to a five fold cross validation. Best feature subsets were selected by Pearson correlation coefficient and recursive feature elimination, whereupon a radiomics classifier was built with SVM. The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).
In all, 186 features were extracted on each subtraction map. Top nine T1WI subtraction features, top thirteen T2 FLAIR subtraction features and top thirteen combination features were selected to build optimal SVM classifiers accordingly. The accuracies/AUCs/sensitivity/specificity/PPV/NPV of SVM based on sole T1WI subtraction were 80.00%/80.00% (CI: 0.5370-1.0000)/100%/70.00%/62.50%/100%. Those results of SVM based on sole T2 FLAIR subtraction were 86.67%/84.00% (CI: 0.5962-1.0000)/100%/80%/71.43%/100%. Those results of SVM based on both T1WI subtraction and T2 FLAIR subtraction were 93.33%/94.00% (CI: 0.7778-1.0000)/100%/90%/83.33%/100%, respectively.
Pre- and post-contrast T2 FLAIR subtraction provided added value for diagnosis between recurrence and TRE. SVM based on a combination of T1WI and T2 FLAIR subtraction maps was superior to the sole use of T1WI or T2 FLAIR for differentiating TRE from recurrence. The SVM classifier based on combination of pre-and post-contrast subtraction T2 FLAIR and T1WI imaging allowed for the accurate differential diagnosis of TRE from recurrence, which is of paramount importance for treatment management of postoperative glioma patients after radiation therapy.
我们提出了三种支持向量机（SVM）分类器，使用对比剂前和对比剂后T2液体衰减反转恢复（FLAIR）相减和/或对比剂前和对比剂后T1加权成像（T1WI）相减，以区分治疗相关效应（TRE）和胶质瘤复发。
研究了来自两个中心的56例术后高级别胶质瘤患者，这些患者在放化疗后有可疑进展。采集了对比剂前和对比剂后T1WI以及T2 FLAIR图像。将每个对比剂前图像与配准后的对比剂后图像进行逐体素相减。数据集按7:3的比例随机分为训练集和测试集，并进行五折交叉验证。通过Pearson相关系数和递归特征消除选择最佳特征子集，然后用支持向量机构建放射组学分类器。用受试者操作特征曲线下面积（AUC）、准确率、敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）评估鉴别性能。
每个相减图共提取186个特征。分别选择前九个T1WI相减特征、前十三个T2 FLAIR相减特征和前十三个组合特征来构建最佳支持向量机分类器。基于单独T1WI相减的支持向量机的准确率/AUC/敏感性/特异性/PPV/NPV分别为80.00%/80.00%（CI：0.5370 - 1.0000）/100%/70.00%/62.50%/100%。基于单独T2 FLAIR相减的支持向量机的这些结果分别为86.67%/84.00%（CI：0.5962 - 1.0000）/100%/80%/71.43%/100%。基于T1WI相减和T2 FLAIR相减两者的支持向量机的这些结果分别为93.33%/94.00%（CI：0.7778 - 1.0000）/100%/90%/83.33%/100%。
对比剂前和对比剂后T2 FLAIR相减为复发和TRE的诊断提供了附加价值。基于T1WI和T2 FLAIR相减图组合的支持向量机在区分TRE和复发方面优于单独使用T1WI或T2 FLAIR。基于对比剂前和对比剂后相减T2 FLAIR与T1WI成像组合的支持向量机分类器能够准确鉴别TRE和复发，这对放疗后术后胶质瘤患者的治疗管理至关重要。
