Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2–4 glioma patients: a deep Radiomics Approach

Yuan，Jialin1,2; Siakallis，Loizos2; Li，Hongwei Bran3,4; Brandner，Sebastian5; Zhang，Jianguo6; Li，Chenming6; Mancini，Laura2,7; Bisdas，Sotirios2,7

2024-12-01

10.1186/s12880-024-01274-9

BMC Medical Imaging   影响因子和分区

1471-2342

Background: The role of isocitrate dehydrogenase (IDH) mutation status for glioma stratification and prognosis is established. While structural magnetic resonance image (MRI) is a promising biomarker, it may not be sufficient for non-invasive characterisation of IDH mutation status. We investigated the diagnostic value of combined diffusion tensor imaging (DTI) and structural MRI enhanced by a deep radiomics approach based on convolutional neural networks (CNNs) and support vector machine (SVM), to determine the IDH mutation status in Central Nervous System World Health Organization (CNS WHO) grade 2–4 gliomas. Methods: This retrospective study analyzed the DTI-derived fractional anisotropy (FA) and mean diffusivity (MD) images and structural images including fluid attenuated inversion recovery (FLAIR), non-enhanced T1-, and T2-weighted images of 206 treatment-naïve gliomas, including 146 IDH mutant and 60 IDH-wildtype ones. The lesions were manually segmented by experienced neuroradiologists and the masks were applied to the FA and MD maps. Deep radiomics features were extracted from each subject by applying a pre-trained CNN and statistical description. An SVM classifier was applied to predict IDH status using imaging features in combination with demographic data. Results: We comparatively assessed the CNN-SVM classifier performance in predicting IDH mutation status using standalone and combined structural and DTI-based imaging features. Combined imaging features surpassed stand-alone modalities for the prediction of IDH mutation status [area under the curve (AUC) = 0.846; sensitivity = 0.925; and specificity = 0.567]. Importantly, optimal model performance was noted following the addition of demographic data (patients’ age) to structural and DTI imaging features [area under the curve (AUC) = 0.847; sensitivity = 0.911; and specificity = 0.617]. Conclusions: Imaging features derived from DTI-based FA and MD maps combined with structural MRI, have superior diagnostic value to that provided by standalone structural or DTI sequences. In combination with demographic information, this CNN-SVM model offers a further enhanced non-invasive prediction of IDH mutation status in gliomas.

CNN Deep neural network DTI IDH Mutation status Prediction Supervised learning SVM

SCI

英语

第一

CLINICAL MEDICINE

2-s2.0-85191980472

Scopus

1.Department of Radiology,Shenzhen People’s Hospital,Second Clinical Medical College of Jinan University,First Affiliated Hospital of Southern University of Science and Technology,Shenzhen,China
2.Queen Square Institute of Neurology,University College London,London,United Kingdom
3.Department of Informatics,Technical University of Munich,Munich,Germany
4.Martinos Center for Biomedical Imaging,Massachusetts General Hospital,Harvard Medical School,Charlestown,United States
5.Division of Neuropathology,Queen Square Institute of Neurology,University College London,London,United Kingdom
6.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China
7.Lysholm Department of Neuroradiology,University College London Hospitals NHS Foundation Trust,London,United Kingdom

Yuan，Jialin,Siakallis，Loizos,Li，Hongwei Bran,等. Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2–4 glioma patients: a deep Radiomics Approach[J]. BMC Medical Imaging,2024,24(1).

Yuan，Jialin.,Siakallis，Loizos.,Li，Hongwei Bran.,Brandner，Sebastian.,Zhang，Jianguo.,...&Bisdas，Sotirios.(2024).Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2–4 glioma patients: a deep Radiomics Approach.BMC Medical Imaging,24(1).

Yuan，Jialin,et al."Structural- and DTI- MRI enable automated prediction of IDH Mutation Status in CNS WHO Grade 2–4 glioma patients: a deep Radiomics Approach".BMC Medical Imaging 24.1(2024).