Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma
| Autor: | Seo Young Park, Ho Sung Kim, Yeongheun Jo, So Yeong Jung, Soo Jung Nam, Jeong Hoon Kim, Minjae Kim, Ji Eun Park |
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| Rok vydání: | 2019 |
| Předmět: |
Adult
Male medicine.medical_specialty Oligodendroglioma Astrocytoma 030218 nuclear medicine & medical imaging Machine Learning 03 medical and health sciences Young Adult 0302 clinical medicine Radiomics Glioma medicine Effective diffusion coefficient Cerebral Blood Volume Humans Radiology Nuclear Medicine and imaging Multiparametric Magnetic Resonance Imaging Neuroradiology Aged Retrospective Studies Aged 80 and over Neoplasm Grading medicine.diagnostic_test Receiver operating characteristic business.industry Brain Neoplasms Computational Biology Magnetic resonance imaging General Medicine Middle Aged medicine.disease Magnetic Resonance Imaging Isocitrate Dehydrogenase Isocitrate dehydrogenase Diffusion Magnetic Resonance Imaging ROC Curve 030220 oncology & carcinogenesis Area Under Curve Mutation Female Radiology business Algorithms Magnetic Resonance Angiography |
| Zdroj: | European radiology. 30(4) |
| ISSN: | 1432-1084 |
| Popis: | To determine whether diffusion- and perfusion-weighted MRI–based radiomics features can improve prediction of isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in lower grade gliomas (LGGs) Radiomics features (n = 6472) were extracted from multiparametric MRI including conventional MRI, apparent diffusion coefficient (ADC), and normalized cerebral blood volume, acquired on 127 LGG patients with determined IDH mutation status and grade (WHO II or III). Radiomics models were constructed using machine learning–based feature selection and generalized linear model classifiers. Segmentation stability was calculated between two readers using concordance correlation coefficients (CCCs). Diagnostic performance to predict IDH mutation and tumor grade was compared between the multiparametric and conventional MRI radiomics models using the area under the receiver operating characteristics curve (AUC). The models were tested using a temporally independent validation set (n = 28). The multiparametric MRI radiomics model was optimized with a random forest feature selector, with segmentation stability of a CCC threshold of 0.8. For IDH mutation, multiparametric MR radiomics showed similar performance (AUC 0.795) to the conventional radiomics model (AUC 0.729). In tumor grading, multiparametric model with ADC features showed higher performance (AUC 0.932) than the conventional model (AUC 0.555). The independent validation set showed the same trend with AUCs of 0.747 for IDH prediction and 0.819 for tumor grading with multiparametric MRI radiomics model. Multiparametric MRI radiomics model showed improved diagnostic performance in tumor grading and comparable diagnostic performance in IDH mutation status, with ADC features playing a significant role. • The multiparametric MRI radiomics model was comparable with conventional MRI radiomics model in predicting IDH mutation. • The multiparametric MRI radiomics model outperformed conventional MRI in glioma grading. • Apparent diffusion coefficient played an important role in glioma grading and predicting IDH mutation status using radiomics. |
| Databáze: | OpenAIRE |
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