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Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas

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收录情况： ◇ CPCI(ISTP) ◇ EI

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机构： [a]School of Life Science and Technology, Xidian University, Xi'an, 710126, China [b]Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China [c]Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China [d]Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China [e]Department of Neuroradiology, Beijing Neurosurgical Institute, Capital Medical University, No.6 Tiantanxili, Dongcheng District, Beijing, 100050, China [f]State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China [g]University of Chinese Academy of Sciences, Beijing, 100049, China [h]China National Clinical Research Center for Neurological Diseases, Beijing, 100050, China

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关键词： 1p/19q Co-deletion Genotype Prediction Lower-grade glioma Magnetic Resonance Imaging Radiomics

摘要：

To pre-operatively and non-invasively predict 1p/19q co-deletion in grade II and III (lower-grade) glioma based on a radiomics method using magnetic resonance imaging (MRI). We obtained 105 patients pathologically diagnosed with lower-grade glioma. We extracted 647 MRI-based features from T2-weighted images and selected discriminative features by lasso logistic regression approaches on the training cohort (n=69). Radiomics, clinical, and combined models were constructed separately to verify the predictive performance of the radiomics signature. The predictability of the three models were validated on a time-independent validation cohort (n = 36). Finally, 7 discriminative radiomic features were used constructed radiomics signature, which demonstrated satisfied performance on both the training and validation cohorts with AUCs of 0.822 and 0.731, respectively. Particularly, the combined model incorporating the radiomics signature and the clinic-radiological factors achieved the best discriminative capability with AUCs of 0.911 and 0.866 for training and validation cohorts, respectively. © 2019 SPIE.

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第一作者：

第一作者机构： [a]School of Life Science and Technology, Xidian University, Xi'an, 710126, China [b]Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China [d]Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China

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通讯作者：

通讯机构： [b]Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China [c]Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China [d]Beijing Key Laboratory of Molecular Imaging, Beijing, 100190, China [f]State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China [g]University of Chinese Academy of Sciences, Beijing, 100049, China [h]China National Clinical Research Center for Neurological Diseases, Beijing, 100050, China

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Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas

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