Volume 21 Issue 12
Dec.  2023
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Article Navigation >  Chinese Journal of General Practice  > 2023  >  21(12): 2106-2110
ZHAO Shasha, XIN Yongkang, ZHANG Kai, WANG Ying, LIU Jinlin, YANG Yang, WANG Wen. Prediction of IDH-1 mutation status in WHO grade Ⅱ and Ⅲ gliomas by radiomics combined with T1-weighted contrast-enhanced image[J]. Chinese Journal of General Practice, 2023, 21(12): 2106-2110. doi: 10.16766/j.cnki.issn.1674-4152.003301
Citation: ZHAO Shasha, XIN Yongkang, ZHANG Kai, WANG Ying, LIU Jinlin, YANG Yang, WANG Wen. Prediction of IDH-1 mutation status in WHO grade Ⅱ and Ⅲ gliomas by radiomics combined with T1-weighted contrast-enhanced image[J]. Chinese Journal of General Practice, 2023, 21(12): 2106-2110. doi: 10.16766/j.cnki.issn.1674-4152.003301
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Prediction of IDH-1 mutation status in WHO grade Ⅱ and Ⅲ gliomas by radiomics combined with T1-weighted contrast-enhanced image

doi: 10.16766/j.cnki.issn.1674-4152.003301

  • Department of Radiology, the Second Affiliated Hospital, Air Force Military Medical University, Xi'an, Shaanxi 710038, China

Funds:

 82102127

  • Received Date: 2023-05-12
    Available Online: 2024-01-29
    Abstract
  •   Objective  To explore the diagnostic efficiency of T1-weighted contrast-enhanced image (T1CE) radiomic features and clinical-related parameters in predicting isocitrate dehydrogenase-1 (IDH-1) gene mutations in WHO grade Ⅱ and Ⅲ gliomas.  Methods  MRI data of 135 patients with WHO grade Ⅱ and Ⅲ gliomas (51 cases in the IDH-1 wild type group and 84 cases in the IDH-1 mutant type group) confirmed by surgery and pathology from the Second Affiliated Hospital of Air Force Military Medical University between January 2017 and July 2019 were selected. The volume of interest (VOI) of the whole tumor-enhanced part was manually drawn on T1CE using ITK-SNAP, and 1 044 radiomic features from the VOI were extracted by using the software of A.K. software. Random forest (RF) algorithm and 5-fold cross-validation method were used to verify the radiomic model in predicting the diagnostic efficiency of IDH-1 mutations of WHO grade Ⅱ and Ⅲ gliomas.  Results  There were statistical differences in the location of the disease and nodular/ring enhancement between IDH-1 mutant and IDH-1 wild-type groups (all P < 0.05), but no significant differences in other MRI morphological characteristics between the two groups. The AUC value of the area under the ROC curve from the iradiomic model was 0.794, the sensitivity was 61.4%, the specificity was 76.7%, and the accuracy was 70.9%. After the feature selection of the RF classifier, the first 30 optimal features were selected to predict IDH-1 mutation, which had the same efficiency as all the radiomic features, and significantly reduced the redundant information.  Conclusion  Radiomics combined with T1CE can effectively predict the IDH-1 mutation status of WHO grade Ⅱ and Ⅲ gliomas. RF classifier model has the potential to predict IDH-1 mutations, which may provide an imaging basis for early diagnosis and individualized treatment of glioma patients.

     

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