Article ; Online: T2-FLAIR mismatch sign and machine learning-based multiparametric MRI radiomics in predicting IDH mutant 1p/19q non-co-deleted diffuse lower-grade gliomas.
2024 Volume 79, Issue 5, Page(s) e750–e758
Abstract: Aim: To investigate the application of the T2-weighted (T2)-fluid-attenuated inversion recovery (FLAIR) mismatch sign and machine learning-based multiparametric magnetic resonance imaging (MRI) radiomics in predicting 1p/19q non-co-deletion of lower- ... ...
| Abstract | Aim: To investigate the application of the T2-weighted (T2)-fluid-attenuated inversion recovery (FLAIR) mismatch sign and machine learning-based multiparametric magnetic resonance imaging (MRI) radiomics in predicting 1p/19q non-co-deletion of lower-grade gliomas (LGGs). Materials and methods: One hundred and forty-six patients, who had pathologically confirmed isocitrate dehydrogenase (IDH) mutant LGGs were assigned randomly to the training cohort (n=102) and the testing cohort (n=44) at a ratio of 7:3. The T2-FLAIR mismatch sign and conventional MRI features were evaluated. Radiomics features extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), FLAIR, apparent diffusion coefficient (ADC), and contrast-enhanced T1WI images (CE-T1WI). The models that displayed the best performance of each sequence were selected, and their predicted values as well as the T2-FLAIR mismatch sign data were collected to establish a final stacking model. Receiver operating characteristic curve (ROC) analyses and area under the curve (AUC) values were applied to evaluate and compare the performance of the models. Results: The T2-FLAIR mismatch sign was more common in the IDH mutant 1p/19q non-co-deleted group (p<0.05) and the area under the curve (AUC) value was 0.692 with sensitivity 0.397, specificity 0.987, and accuracy 0.712, respectively. The stacking model showed a favourable performance with an AUC of 0.925 and accuracy of 0.882 in the training cohort and an AUC of 0.886 and accuracy of 0.864 in the testing cohort. Conclusion: The stacking model based on multiparametric MRI can serve as a supplementary tool for pathological diagnosis, offering valuable guidance for clinical practice. |
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| MeSH term(s) | Humans ; Multiparametric Magnetic Resonance Imaging ; Isocitrate Dehydrogenase/genetics ; Radiomics ; Brain Neoplasms/diagnostic imaging ; Brain Neoplasms/genetics ; Brain Neoplasms/pathology ; Mutation/genetics ; Glioma/diagnostic imaging ; Glioma/genetics ; Glioma/pathology ; Magnetic Resonance Imaging/methods ; Machine Learning ; Retrospective Studies |
| Chemical Substances | Isocitrate Dehydrogenase (EC 1.1.1.41) |
| Language | English |
| Publishing date | 2024-02-02 |
| Publishing country | England |
| Document type | Randomized Controlled Trial ; Journal Article |
| ZDB-ID | 391227-9 |
| ISSN | 1365-229X ; 0009-9260 |
| ISSN (online) | 1365-229X |
| ISSN | 0009-9260 |
| DOI | 10.1016/j.crad.2024.01.021 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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