Deep Convolutional Radiomic Features on Diffusion Tensor Images for Classification of Glioma Grades
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ORIGINAL PAPER
Deep Convolutional Radiomic Features on Diffusion Tensor Images for Classification of Glioma Grades Zhiwei Zhang 1 & Jingjing Xiao 2,3 & Shandong Wu 4 & Fajin Lv 1 & Junwei Gong 1 & Lin Jiang 5 & Renqiang Yu 1 & Tianyou Luo 1
# Society for Imaging Informatics in Medicine 2020
Abstract The grading of glioma has clinical significance in determining a treatment strategy and evaluating prognosis to investigate a novel set of radiomic features extracted from the fractional anisotropy (FA) and mean diffusivity (MD) maps of brain diffusion tensor imaging (DTI) sequences for computer-aided grading of gliomas. This retrospective study included 108 patients who had pathologically confirmed brain gliomas and DTI scanned during 2012–2018. This cohort included 43 low-grade gliomas (LGGs; all grade II) and 65 high-grade gliomas (HGGs; grade III or IV). We extracted a set of radiomic features, including traditional texture, morphological, and novel deep features derived from pre-trained convolutional neural network models, in the manually-delineated tumor regions. We employed support vector machine and these radiomic features for two classification tasks: LGGs vs HGGs, and grade III vs IV. The area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity was reported as the performance metrics using the leave-one-out cross-validation method. When combining FA+MD, AUC = 0.93, accuracy = 0.94, sensitivity = 0.98, and specificity = 0.86 in classifying LGGs from HGGs, while AUC = 0.99, accuracy = 0.98, sensitivity = 0.98, and specificity = 1.00 in classifying grade III from IV. The AUC and accuracy remain close when features were extracted from only the solid tumor or additionally including necrosis, cyst, and peritumoral edema. Still, the effects in terms of sensitivity and specificity are mixed. Deep radiomic features derived from pre-trained convolutional neural networks showed higher prediction ability than the traditional texture and shape features in both classification experiments. Radiomic features extracted on the FA and MD maps of brain DTI images are useful for noninvasively classification/grading of LGGs vs HGGs, and grade III vs IV. Keywords Brain tumor . Glioma grading . Diffusion tensor imaging . Radiomic features . Deep learning
Introduction Gliomas are the most common type of primary brain tumor in adults and a critical cause of brain cancer mortality [1]. * Tianyou Luo [email protected] 1
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
2
Department of Medical Engineering, Xinqiao Hospital, Third Military Medical University (Army Medical University), Chongqing 400037, China
3
School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK
4
Departments of Radiology, Biomedical Informatics, Bioengineering, and Intelligent Systems, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA 15260, USA
5
Department of Radiology, The Third Affiliated Hospital of Zunyi Medical Co
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