Department of Radiology and Research Institute of Radiological Science, College of Medicine, Yonsei University College of Medicine, 50 Yonsei-ro, Seodaemun-gu, Seoul, 120-752, South Korea.
Department of Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore.
Eur Radiol. 2020 Jul;30(7):3834-3842. doi: 10.1007/s00330-020-06737-5. Epub 2020 Mar 11.
Recent studies have highlighted the importance of isocitrate dehydrogenase (IDH) mutational status in stratifying biologically distinct subgroups of gliomas. This study aimed to evaluate whether MRI-based radiomic features could improve the accuracy of survival predictions for lower grade gliomas over clinical and IDH status.
Radiomic features (n = 250) were extracted from preoperative MRI data of 296 lower grade glioma patients from databases at our institutional (n = 205) and The Cancer Genome Atlas (TCGA)/The Cancer Imaging Archive (TCIA) (n = 91) datasets. For predicting overall survival, random survival forest models were trained with radiomic features; non-imaging prognostic factors including age, resection extent, WHO grade, and IDH status on the institutional dataset, and validated on the TCGA/TCIA dataset. The performance of the random survival forest (RSF) model and incremental value of radiomic features were assessed by time-dependent receiver operating characteristics.
The radiomics RSF model identified 71 radiomic features to predict overall survival, which were successfully validated on TCGA/TCIA dataset (iAUC, 0.620; 95% CI, 0.501-0.756). Relative to the RSF model from the non-imaging prognostic parameters, the addition of radiomic features significantly improved the overall survival prediction accuracy of the random survival forest model (iAUC, 0.627 vs. 0.709; difference, 0.097; 95% CI, 0.003-0.209).
Radiomic phenotyping with machine learning can improve survival prediction over clinical profile and genomic data for lower grade gliomas.
• Radiomics analysis with machine learning can improve survival prediction over the non-imaging factors (clinical and molecular profiles) for lower grade gliomas, across different institutions.
最近的研究强调了异柠檬酸脱氢酶（IDH）突变状态在分层胶质瘤生物学上不同亚群中的重要性。本研究旨在评估基于 MRI 的放射组学特征是否可以提高对低级别胶质瘤生存预测的准确性，超过临床和 IDH 状态。
从我们机构（n=205）和癌症基因组图谱/癌症成像档案（TCGA/TCIA）（n=91）数据库中 296 例低级别胶质瘤患者的术前 MRI 数据中提取放射组学特征（n=250）。为了预测总生存期，使用放射组学特征对随机生存森林模型进行训练；非影像预测因素包括机构数据集上的年龄、切除程度、WHO 分级和 IDH 状态，在 TCGA/TCIA 数据集上进行验证。通过时间依赖性接收者操作特征评估随机生存森林（RSF）模型的性能和放射组学特征的增量价值。
放射组学 RSF 模型确定了 71 个放射组学特征来预测总生存期，并在 TCGA/TCIA 数据集上成功验证（iAUC，0.620；95%CI，0.501-0.756）。与非影像预测参数的 RSF 模型相比，放射组学特征的加入显著提高了随机生存森林模型的总生存预测准确性（iAUC，0.627 与 0.709；差异，0.097；95%CI，0.003-0.209）。
使用机器学习进行放射组学表型分析可以提高低级别胶质瘤的生存预测，超过临床特征和基因组数据。
放射组学分析与机器学习可以提高对低级别胶质瘤的生存预测，超过非影像因素（临床和分子特征），适用于不同机构。
