Predictive value of magnetic resonance imaging radiomics-based machine learning for disease progression in patients with high-grade glioma.
Predictive value of magnetic resonance imaging radiomics-based machine learning for disease progression in patients with high-grade glioma.
复制标题
DOI：
10.21037/qims-22-459
复制
发表时间：
2023-01-01
影响因子：
2.8
通讯作者：
Bai, Sen
中科院分区：
文献类型：
--
作者：
Li, Zhibin;Chen, Li;Song, Ying;Dai, Guyu;Duan, Lian;Luo, Yong;Wang, Guangyu;Xiao, Qing;Li, Guangjun;Bai, Sen
关键词：
准确预测高级别胶质瘤（HGG）患者的预后对治疗具有潜在的重要意义。然而，在不同的时间点，各种磁共振成像（MRI）序列的图像对预后的预测价值是未知的。我们使用MRI放射组学建立了HGG疾病进展和复发的预测机器学习模型，并探讨了影响预测准确性的因素。从162例HGG患者的T1加权（T1 WI）、对比增强T1加权（CE-T1 WI）、T2加权（T2 WI）和液体衰减反转恢复（FLAIR）图像（术后放疗计划MRI图像）中提取放射组学特征。采用Mann-Whitney U检验和最小绝对收缩选择算子（LASSO）算法进行特征选择。机器学习模型用于构建预测模型，以估计疾病进展或复发。还探讨了不同MRI序列、感兴趣区域（ROI）和预测时间点的影响。采用受试者工作特征（ROC）曲线评价各模型的判别性能，并采用DeLong检验比较ROC曲线。与T1 WI或CE-TIWI相比，T2 WI和FLAIR的放射组学特征对疾病进展具有更高的预测价值。预测放疗后第6、9、12、15和18个月疾病进展的ROC曲线下面积（AUC）分别为0.70、0.68、0.78、0.78和0.78，结合T2 WI和FLAIR图像提取的肿瘤体积（GTV）和临床靶体积（CTV）特征，结合临床特征，得到肿瘤的T2 WI和FLAIR图像。放射治疗前获得的结构MRI可用于预测HGG的疾病进展或治疗后复发。当使用MRI放射组学预测长期结果而不是短期结果时，可以获得更好的预测结果。
Accurately predicting the prognosis of patients with high-grade glioma (HGG) is potentially important for treatment. However, the predictive value of images of various magnetic resonance imaging (MRI) sequences for prognosis at different time points is unknown. We established predictive machine learning models of HGG disease progression and recurrence using MRI radiomics and explored the factors influencing prediction accuracy. Radiomics features were extracted from T1-weighted (T1WI), contrast-enhanced T1-weighted (CE-T1WI), T2-weighted (T2WI), and fluid-attenuated inversion recovery (FLAIR) images (postoperative radiotherapy planning MRI images) obtained from 162 patients with HGG. The Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) algorithm were used for feature selection. Machine learning models were used to build prediction models to estimate disease progression or recurrence. The influence of different MRI sequences, regions of interest (ROIs), and prediction time points was also explored. The receiver operating characteristic (ROC) curve was used to evaluate the discriminative performance of each model, and the DeLong test was employed to compare the ROC curves. Radiomics features from T2WI and FLAIR demonstrated greater predictive value for disease progression compared with T1WI or CE-TIWI. The best predictive models, with areas under the ROC curves (AUCs) of 0.70, 0.68, 0.78, 0.78, and 0.78 for predicting disease progression at the 6th, 9th, 12th, 15th, and 18th month after radiotherapy, respectively, were obtained by combining clinical features with gross tumor volume (GTV) and clinical target volume (CTV) features extracted from T2WI and FLAIR. Structural MRI obtained before radiotherapy can be used to predict the disease progression or posttreatment recurrence of HGG. When using MRI radiomics to predict long-term outcomes as opposed to short-term outcomes, better predictive results may be obtained.
登录
查看更多内容
影响因子：
12.7
作者：
Hartmann, Christian;Meyer, Jochen;von Deimling, Andreas
通讯作者：
von Deimling, Andreas
新诊断的胶质母细胞瘤对放化疗的单指数、扩散峰度和拉伸指数扩散 MR 成像反应。
影响因子：
3.9
作者：
Chakhoyan A;Woodworth DC;Harris RJ;Lai A;Nghiemphu PL;Liau LM;Pope WB;Cloughesy TF;Ellingson BM
通讯作者：
Ellingson BM
放射组学特征作为低级别神经胶质瘤患者无进展生存的非侵入性预测因子。
DOI：
10.1016/j.nicl.2018.10.014
发表时间：
2018
期刊：
NeuroImage. Clinical
影响因子：
--
作者：
Liu X;Li Y;Qian Z;Sun Z;Xu K;Wang K;Liu S;Fan X;Li S;Zhang Z;Jiang T;Wang Y
通讯作者：
Wang Y
影响因子：
2
作者：
Aulakh, Sonikpreet;Dedeo, Michelle R.;Ailawadhi, Sikander
通讯作者：
Ailawadhi, Sikander
影响因子：
4.3
作者：
Sun, Li;Zhang, Songtao;Luo, Lin
通讯作者：
Luo, Lin