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Deep Convolutional Radiomic Features on Diffusion Tensor Images for Classification of Glioma Grades

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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.

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Funding

This work was partially supported by the National Key Research and Development Plan of China (NO. 2016YFC0103100) and the National Natural Science Foundation of China (No. 61701506).

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Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China

    Zhiwei Zhang, Fajin Lv, Junwei Gong, Renqiang Yu & Tianyou Luo

  2. Department of Medical Engineering, Xinqiao Hospital, Third Military Medical University (Army Medical University), Chongqing, 400037, China

    Jingjing Xiao

  3. School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK

    Jingjing Xiao

  4. Departments of Radiology, Biomedical Informatics, Bioengineering, and Intelligent Systems, University of Pittsburgh, 4200 Fifth Ave, Pittsburgh, PA, 15260, USA

    Shandong Wu

  5. Department of Radiology, The Third Affiliated Hospital of Zunyi Medical College, Zunyi, 563000, Guizhou, China

    Lin Jiang

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Correspondence to Tianyou Luo.

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The Institutional Review Board approved this retrospective study of our institution, and the requirement for informed consent was waived.

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Zhang, Z., Xiao, J., Wu, S. et al. Deep Convolutional Radiomic Features on Diffusion Tensor Images for Classification of Glioma Grades. J Digit Imaging 33, 826–837 (2020). https://doi.org/10.1007/s10278-020-00322-4

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