Radiomics and Qualitative Features From Multiparametric MRI Predict Molecular Subtypes in Patients With Lower-Grade Glioma

Frontiers in Oncology

Jing Yan¹  Jingliang Cheng¹  Liyuan Fan²  Dongling Pei³  Yu Guo³  Wenqing Wang³  Zhen Liu³  Xianzhi Liu³  Wenchao Duan³  Haibiao Zhao³  Xuanke Hong³  Xiangxiang Wang³  Tao Sun³  Yunbo Zhan³  Chen Sun³  Zhenyu Zhang³  Wencai Li⁴  Weiwei Wang⁴  Lei Liu⁵  Zhicheng Li⁵ 

[1] Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China;Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China;Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China;Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China;Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;

关键词: lower-grade glioma;    radiomics;    Visually Accessible Rembrandt Images;    molecular subtypes;    machine learning;   

DOI  :  10.3389/fonc.2021.756828

来源: DOAJ

【 摘 要 】

BackgroundIsocitrate dehydrogenase (IDH) mutation and 1p19q codeletion status have been identified as significant markers for therapy and prognosis in lower-grade glioma (LGG). The current study aimed to construct a combined machine learning-based model for predicting the molecular subtypes of LGG, including (1) IDH wild-type astrocytoma (IDHwt), (2) IDH mutant and 1p19q non-codeleted astrocytoma (IDHmut-noncodel), and (3) IDH-mutant and 1p19q codeleted oligodendroglioma (IDHmut-codel), based on multiparametric magnetic resonance imaging (MRI) radiomics, qualitative features, and clinical factors.MethodsA total of 335 patients with LGG (WHO grade II/III) were retrospectively enrolled. The sum of 5,929 radiomics features were extracted from multiparametric MRI. Selected robust, non-redundant, and relevant features were used to construct a random forest model based on a training cohort (n = 269) and evaluated on a testing cohort (n = 66). Meanwhile, preoperative MRIs of all patients were scored in accordance with Visually Accessible Rembrandt Images (VASARI) annotations and T2-fluid attenuated inversion recovery (T2-FLAIR) mismatch sign. By combining radiomics features, qualitative features (VASARI annotations and T2-FLAIR mismatch signs), and clinical factors, a combined prediction model for the molecular subtypes of LGG was built.ResultsThe 17-feature radiomics model achieved area under the curve (AUC) values of 0.6557, 0.6830, and 0.7579 for IDHwt, IDHmut-noncodel, and IDHmut-codel, respectively, in the testing cohort. Incorporating qualitative features and clinical factors into the radiomics model resulted in improved AUCs of 0.8623, 0.8056, and 0.8036 for IDHwt, IDHmut-noncodel, and IDHmut-codel, with balanced accuracies of 0.8924, 0.8066, and 0.8095, respectively.ConclusionThe combined machine learning algorithm can provide a method to non-invasively predict the molecular subtypes of LGG preoperatively with excellent predictive performance.

【 授权许可】

Unknown