College of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi Province, China.
Department of Radiology, The Sixth Hospital, Shanxi Medical University, Taiyuan, Shanxi Province, China.
Biomed Res Int. 2022 Sep 30;2022:4667117. doi: 10.1155/2022/4667117. eCollection 2022.
To evaluate the diagnostic value of multimodal MRI radiomics based on T2-weighted fluid attenuated inversion recovery imaging (T2WI-FLAIR) combined with T1-weighted contrast enhanced imaging (T1WI-CE) in the early differentiation of high-grade glioma recurrence from pseudoprogression.
A total of one hundred eighteen patients with brain gliomas who were diagnosed from March 2014 to April 2020 were retrospectively analyzed. According to the clinical characteristics, the patients were randomly split into a training group ( = 83) and a test group ( = 35) at a 7 : 3 ratio. The region of interest (ROI) was delineated, and 2632 radiomic features were extracted. We used multiple logistic regression to establish a classification model, including the 1 model, 2 model, and 1 + 2 model, to differentiate recurrence from pseudoprogression. The diagnostic efficiency of the model was evaluated by calculating the area under the receiver operating characteristic curve (AUC) and accuracy (ACC) and by analyzing the calibration curve of the nomogram and decision curve.
There were 75 cases of recurrence and 43 cases of pseudoprogression. The diagnostic efficacies of the multimodal MRI-based radiomic model were relatively high. The AUC values and ACC of the training group were 0.831 and 77.11%, respectively, and the AUC values and ACC of the test group were 0.829 and 88.57%, respectively. The calibration curve of the nomogram showed that the discrimination probability was consistent with the actual occurrence in the training group, and the discrimination probability was roughly the same as the actual occurrence in the test group. In the decision curve analysis, the 1 + 2 model showed greater overall net efficiency.
The multimodal MRI radiomic model has relatively high efficiency in the early differentiation of recurrence from pseudoprogression, and it could be helpful for clinicians in devising correct treatment plans so that patients can be treated in a timely and accurate manner.
评估基于 T2 加权液体衰减反转恢复成像（T2WI-FLAIR）联合 T1 加权对比增强成像（T1WI-CE）的多模态 MRI 放射组学在高级别胶质瘤复发与假性进展早期鉴别中的诊断价值。
回顾性分析 2014 年 3 月至 2020 年 4 月诊断的 118 例脑胶质瘤患者，根据临床特征，以 7：3 的比例将患者随机分为训练组（n=83）和测试组（n=35）。描绘感兴趣区（ROI），提取 2632 个放射组学特征。采用多变量逻辑回归建立包括 1 模型、2 模型和 1+2 模型的分类模型，以区分复发与假性进展。通过计算受试者工作特征曲线（ROC）下面积（AUC）和准确率（ACC），以及分析列线图和决策曲线的校准曲线，评估模型的诊断效能。
共 75 例复发，43 例假性进展。基于多模态 MRI 的放射组学模型的诊断效能较高。训练组的 AUC 值和 ACC 分别为 0.831 和 77.11%，测试组的 AUC 值和 ACC 分别为 0.829 和 88.57%。列线图的校准曲线显示，训练组中判别概率与实际发生率一致，测试组中判别概率与实际发生率大致相同。在决策曲线分析中，1+2 模型显示出更大的整体净效率。
多模态 MRI 放射组学模型在高级别胶质瘤复发与假性进展的早期鉴别中具有较高的效率，有助于临床医生制定正确的治疗方案，使患者能够得到及时、准确的治疗。
