A novel image signature-based radiomics method to achieve precise diagnosis and prognostic stratification of gliomas.

Luo, Huigao; Zhuang, Qiyuan; Wang, Yuanyuan; Abudumijiti, Aibaidula; Shi, Kuangyu; Rominger, Axel; Chen, Hong; Yang, Zhong; Tran, Vanessa; Wu, Guoqing; Li, Zeju; Fan, Zhen; Qi, Zengxin; Guo, Yuxiao; Yu, Jinhua; Shi, Zhifeng (2021). A novel image signature-based radiomics method to achieve precise diagnosis and prognostic stratification of gliomas. Laboratory investigation, 101(4), pp. 450-462. Springer Nature 10.1038/s41374-020-0472-x

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Radiomics has potential advantages in the noninvasive histopathological and molecular diagnosis of gliomas. We aimed to develop a novel image signature (IS)-based radiomics model to achieve multilayered preoperative diagnosis and prognostic stratification of gliomas. Herein, we established three separate case cohorts, consisting of 655 glioma patients, and carried out a retrospective study. Image and clinical data of three cohorts were used for training (N = 188), cross-validation (N = 411), and independent testing (N = 56) of the IS model. All tumors were segmented from magnetic resonance (MR) images by the 3D U-net, followed by extraction of high-throughput network features, which were referred to as IS. IS was then used to perform noninvasive histopathological diagnosis and molecular subtyping. Moreover, a new IS-based clustering method was applied for prognostic stratification in IDH-wild-type lower-grade glioma (IDHwt LGG) and triple-negative glioblastoma (1p19q retain/IDH wild-type/TERTp-wild-type GBM). The average accuracies of histological diagnosis and molecular subtyping were 89.8 and 86.1% in the cross-validation cohort, while these numbers reached 83.9 and 80.4% in the independent testing cohort. IS-based clustering method was demonstrated to successfully divide IDHwt LGG into two subgroups with distinct median overall survival time (48.63 vs 38.27 months respectively, P = 0.023), and two subgroups in triple-negative GBM with different median OS time (36.8 vs 18.2 months respectively, P = 0.013). Our findings demonstrate that our novel IS-based radiomics model is an effective tool to achieve noninvasive histo-molecular pathological diagnosis and prognostic stratification of gliomas. This IS model shows potential for future routine use in clinical practice.

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