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

Journal: Laboratory investigation; a journal of technical methods and pathology
Published Date: Aug 22, 2020

Abstract

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.

Authors

  • Huigao Luo
    Department of Electronic Engineering, Fudan University, Shanghai, China.
  • Qiyuan Zhuang
    Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
  • Yuanyuan Wang
    Department of Biotechnology, College of Life Science and Technology, Jinan University Guangzhou, 510632, China.
  • Aibaidula Abudumijiti
    Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
  • Kuangyu Shi
    Universitätsklinik für Nuklearmedizin, Inselspital University Hospital Bern, University of Bern, Bern, Switzerland.
  • Axel Rominger
  • Hong Chen
    Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Zhong Yang
    Department of Clinical Hematology, Southwestern Hospital, Third Military Medical University (Army Medical University), Chongqing, China.
  • Vanessa Tran
    B-BMed, The University of Melbourne, Melbourne, VIC, Australia.
  • Guoqing Wu
    Department of Electronic Engineering, Fudan University, Shanghai, China.
  • Zeju Li
    Department of Electronic Engineering, Fudan University, Shanghai, China.
  • Zhen Fan
    Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
  • Zengxin Qi
    Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
  • Yuxiao Guo
    Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
  • Jinhua Yu
    Department of Electronic Engineering, Fudan University, Shanghai, 200433, China. jhyu@fudan.edu.cn.
  • Zhifeng Shi
    Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.

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