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Radiomic study on preoperative multi-modal magnetic resonance images identifies IDH-mutant TERT promoter-mutant gliomas

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机构： [1]Department of Neurosurgery, West China Hospital of Sichuan University, Chengdu, China. [2]Department of Neurosurgery, Xinhua Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China. [3]Department of Head and Neck Surgery, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. [4]Department of Radiology, West China Hospital of Sichuan University, Chengdu, China. [5]Frontier Science Center for Disease Molecular Network, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China. [6]Department of Pathology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

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Gliomas with comutations of isocitrate dehydrogenase (IDH) genes and telomerase reverse transcriptase (TERT) gene promoter (IDHmut pTERTmut) show distinct biological features and respond to first-line treatment differently in comparison with other gliomas. This study aimed to characterize the IDHmut pTERTmut gliomas in multimodal MRI using the radiomic method and establish a precise diagnostic model identifying this group of gliomas.A total of 140 patients with untreated primary gliomas were admitted between 2016 and 2020 to West China Hospital as a discovery cohort, including 22 IDHmut pTERTmut patients. Thirty-four additional cases from a different hospital were included in the study as an independent validation cohort. A total of 3654 radiomic features were extracted from the preoperative multimodal MRI images (T1c, FLAIR, and ADC maps) and filtered in a data-driven approach. The discovery cohort was split into training and test sets by a 4:1 ratio. A diagnostic model (multilayer perceptron classifier) for detecting the IDHmut pTERTmut gliomas was trained using an automatic machine-learning algorithm named tree-based pipeline optimization tool (TPOT). The most critical radiomic features in the model were identified and visualized.The model achieved an area under the receiver-operating curve (AUROC) of 0.971 (95% CI, 0.902-1.000), the sensitivity of 0.833 (95% CI, 0.333-1.000), and the specificity of 0.966 (95% CI, 0.931-1.000) in the test set. The area under the precision-recall curve (AUCPR) was 0.754 (95% CI, 0.572-0.833) and the F1 score was 0.833 (95% CI, 0.500-1.000). In the independent validation set, the model reached 0.952 AUROC, 0.714 sensitivity, 0.963 specificity, 0.841 AUCPR, and 0.769 F1 score. MR radiomic features of the IDHmut pTERTmut gliomas represented homogenous low-complexity texture in three modalities.An accurate diagnostic model was constructed for detecting IDHmut pTERTmut gliomas using multimodal radiomic features. The most important features were associated with the homogenous simple texture of IDHmut pTERTmut gliomas in MRI images transformed using Laplacian of Gaussian and wavelet filters.© 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

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出版当年[2023]版：

大类 | 2 区医学

小类 | 3 区肿瘤学

最新[2023]版：

大类 | 2 区医学

小类 | 3 区肿瘤学

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出版当年[2023]版：

Q2 ONCOLOGY

最新[2023]版：

Q2 ONCOLOGY

影响因子： 2.9 最新[2023版] 3.9 最新五年平均 2.9 出版当年[2023版] 3.9 出版当年五年平均 4 出版前一年[2022版]

第一作者：

第一作者机构： [1]Department of Neurosurgery, West China Hospital of Sichuan University, Chengdu, China. [2]Department of Neurosurgery, Xinhua Hospital, Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

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通讯作者：

通讯机构： [1]Department of Neurosurgery, West China Hospital of Sichuan University, Chengdu, China. [*1]Department of Neurosurgery, West China Hospital of Sichuan University, #37 Guo Xue Xiang, Chengdu 610041, P. R. China.

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