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      Multimodal 3D DenseNet for IDH Genotype Prediction in Gliomas

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          Abstract

          Non-invasive prediction of isocitrate dehydrogenase ( IDH) genotype plays an important role in tumor glioma diagnosis and prognosis. Recently, research has shown that radiology images can be a potential tool for genotype prediction, and fusion of multi-modality data by deep learning methods can further provide complementary information to enhance prediction accuracy. However, it still does not have an effective deep learning architecture to predict IDH genotype with three-dimensional (3D) multimodal medical images. In this paper, we proposed a novel multimodal 3D DenseNet (M3D-DenseNet) model to predict IDH genotypes with multimodal magnetic resonance imaging (MRI) data. To evaluate its performance, we conducted experiments on the BRATS-2017 and The Cancer Genome Atlas breast invasive carcinoma (TCGA-BRCA) dataset to get image data as input and gene mutation information as the target, respectively. We achieved 84.6% accuracy (area under the curve (AUC) = 85.7%) on the validation dataset. To evaluate its generalizability, we applied transfer learning techniques to predict World Health Organization (WHO) grade status, which also achieved a high accuracy of 91.4% (AUC = 94.8%) on validation dataset. With the properties of automatic feature extraction, and effective and high generalizability, M3D-DenseNet can serve as a useful method for other multimodal radiogenomics problems and has the potential to be applied in clinical decision making.

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            The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).

            In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.
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              Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

              Gliomas belong to a group of central nervous system tumors, and consist of various sub-regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for both clinical and computational studies, including radiomic and radiogenomic analyses. Towards this end, we release segmentation labels and radiomic features for all pre-operative multimodal magnetic resonance imaging (MRI) (n=243) of the multi-institutional glioma collections of The Cancer Genome Atlas (TCGA), publicly available in The Cancer Imaging Archive (TCIA). Pre-operative scans were identified in both glioblastoma (TCGA-GBM, n=135) and low-grade-glioma (TCGA-LGG, n=108) collections via radiological assessment. The glioma sub-region labels were produced by an automated state-of-the-art method and manually revised by an expert board-certified neuroradiologist. An extensive panel of radiomic features was extracted based on the manually-revised labels. This set of labels and features should enable i) direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as ii) performance evaluation of computer-aided segmentation methods, and comparison to our state-of-the-art method.
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                Author and article information

                Journal
                Genes (Basel)
                Genes (Basel)
                genes
                Genes
                MDPI
                2073-4425
                30 July 2018
                August 2018
                : 9
                : 8
                : 382
                Affiliations
                [1 ]Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, and College of Computer Science and Technology, Jilin University, Changchun 130012, China; hawkcoder@ 123456gmail.com
                [2 ]Advanced Institute, Infervision, Beijing 100000, China; zrongguo@ 123456infervision.com (R.Z.); songtianci1993@ 123456hotmail.com (T.S.); xchen@ 123456infervision.com (C.X.)
                [3 ]School of Mechatronics Engineering, Nanchang University, Nanchang 330031, China; 5910116336@ 123456email.ncu.edu.cn
                [4 ]Department of Radiology, Tongji Hospital, Wuhan 430030, China; aitao007@ 123456hotmail.com
                [5 ]Cancer Systems Biology Center, China-Japan Union Hospital, Jilin University, Changchun 130033, China
                Author notes
                [* ]Correspondence: xialiming2017@ 123456outlook.com (L.X.); wy6868@ 123456jlu.edu.cn (Y.W.); Tel.: +86-136-0431-0096 (Y.W.)
                Author information
                https://orcid.org/0000-0001-5511-2614
                Article
                genes-09-00382
                10.3390/genes9080382
                6115744
                30061525
                0419dc57-b0ac-4202-bbec-159b320b1d3d
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 26 May 2018
                : 16 July 2018
                Categories
                Article

                multimodal deep learning,three-dimensional densenet model,isocitrate dehydrogenase genotype,magnetic resonance imaging,gliomas,world health organization grade

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