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Development of a Fully Automated Glioma-Grading Pipeline Using Post-Contrast T1-Weighted Images Combined with Cloud-Based 3D Convolutional Neural Network

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Development of a Fully Automated Glioma-Grading Pipeline Using Post-Contrast T1-Weighted Images Combined with Cloud-Based 3D Convolutional Neural Network applied sciences Article Development of a Fully Automated Glioma-Grading Pipeline Using Post-Contrast T1-Weighted Images Combined with Cloud-Based 3D Convolutional Neural Network Hiroto Yamashiro 1, Atsushi Teramoto 1,* , Kuniaki Saito 1 and Hiroshi Fujita 2 1 Graduate School of Health Sciences, Fujita Health University, 1-98 Dengakugakubo, Kutsukake, Toyoake 470-1192, Aichi, Japan; [email protected] (H.Y.); [email protected] (K.S.) 2 Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu 501-1194, Gifu, Japan; [email protected] * Correspondence: [email protected] Featured Application: The proposed grading pipeline combined a cloud-based trained 3D CNN and our original 3D CNN is useful for early treatment of patients and prediction of their prognosis. Abstract: Glioma is the most common type of brain tumor, and its grade influences its treatment pol- icy and prognosis. Therefore, artificial-intelligence-based tumor grading methods have been studied. However, in most studies, two-dimensional (2D) analysis and manual tumor-region extraction were performed. Additionally, deep learning research that uses medical images experiences difficulties in collecting image data and preparing hardware, thus hindering its widespread use. Therefore, we developed a 3D convolutional neural network (3D CNN) pipeline for realizing a fully automated Citation: Yamashiro, H.; Teramoto, glioma-grading system by using the pretrained Clara segmentation model provided by NVIDIA A.; Saito, K.; Fujita, H. Development and our original classification model. In this method, the brain tumor region was extracted using of a Fully Automated the Clara segmentation model, and the volume of interest (VOI) created using this extracted region Glioma-Grading Pipeline Using was assigned to a grading 3D CNN and classified as either grade II, III, or IV. Through evaluation Post-Contrast T1-Weighted Images using 46 regions, the grading accuracy of all tumors was 91.3%, which was comparable to that of Combined with Cloud-Based 3D the method using multi-sequence. The proposed pipeline scheme may enable the creation of a fully Convolutional Neural Network. Appl. Sci. 2021, 11, 5118. https://doi.org/ automated glioma-grading pipeline in a single sequence by combining the pretrained 3D CNN and 10.3390/app11115118 our original 3D CNN. Academic Editor: Keun-Ho Ryu Keywords: brain tumor; magnetic resonance imaging; grading; convolutional neural network Received: 7 May 2021 Accepted: 29 May 2021 Published: 31 May 2021 1. Introduction Glioma is a type of primary brain tumor and is the most common type of brain tumors. Publisher’s Note: MDPI stays neutral The grade of glioma is given as an index of its malignancy, and it significantly influences with regard to jurisdictional claims in its treatment policy and prognosis [1]. In clinical practice, the treatment policy for grade IV published maps and institutional affil- glioma is different from that of the other grades because it progresses rapidly and has a iations. poor prognosis. Therefore, accurate diagnosis of grade leads to accurate early treatment. Nevertheless, grading as a definite diagnosis cannot be performed without the pathological examination of the removed tumor tissue. Therefore, before the surgical operation, neurol- ogists estimate the tumor grade using magnetic resonance imaging (MRI) findings, such as Copyright: © 2021 by the authors. the presence or absence of the ring enhancement effect. However, these characteristics vary Licensee MDPI, Basel, Switzerland. among patients and it makes diagnosis difficult [2–5]. Therefore, many researchers are This article is an open access article trying to solve this problem of low accuracy using a convolutional neural network (CNN), distributed under the terms and which is one of the excellent image-analysis technologies. Yang et al. [6] classified the conditions of the Creative Commons grade of glioma using fine-tuned GoogLeNet. Furthermore, Abd-Ellah et al. [7] proposed a Attribution (CC BY) license (https:// glioma detection and grading system using a parallel deep CNN. In addition to gliomas, a creativecommons.org/licenses/by/ computer-aided diagnosis (CAD) system for brain tumors using CNN is being developed. 4.0/). Appl. Sci. 2021, 11, 5118. https://doi.org/10.3390/app11115118 https://www.mdpi.com/journal/applsci Appl. Sci. 2021, 11, 5118 2 of 11 Appl. Sci. 2021, 11, 5118 2 of 11 CNN is being developed. Abd El Kader et al. [8] proposed a differential deep CNN model to classify abnormal or normal MR brain images, and Díaz-Pernas et al. [9] proposed a Abdmultiscale El Kader CNN et al.model [8] proposed for region a differentialextraction an deepd classification CNN model of tothree classify types abnormal of brain ortu- normalmors, including MR brain glioma, images, in and post-contrast Díaz-Pernas T1-w et al.eighted [9] proposed images. a multiscaleHowever, CNNin these model studies, for region3D MR extraction images were and analyzed classification on a of slice-by-slice three types ofbasis. brain It tumors,is considered including that glioma,a more accurate in post- contrastanalysis T1-weighted will be possible images. using However, 3D images in these because studies, tumors 3D MR grow images in multiple were analyzed directions. on aAdditionally, slice-by-slice basis.when Itthe is consideredtumor regions that awere more ma accuratenually analysisextracted, will the be variations possible using in these 3D images because tumors grow in multiple directions. Additionally, when the tumor regions regions influenced the grading accuracy. Therefore, these problems can be solved by ap- were manually extracted, the variations in these regions influenced the grading accuracy. plying automated tumor-region extraction and grading using 3D MR images. In fact, Chen Therefore, these problems can be solved by applying automated tumor-region extraction et al. [10] developed an automatic CAD system of gliomas that combines automatic seg- and grading using 3D MR images. In fact, Chen et al. [10] developed an automatic CAD mentation and radiomics. By training and evaluation using Multimodal Brain Tumor Seg- system of gliomas that combines automatic segmentation and radiomics. By training mentation Challenge 2015 (BraTS2015) dataset, the grading accuracy was observed to be and evaluation using Multimodal Brain Tumor Segmentation Challenge 2015 (BraTS2015) 91.3%. Furthermore, Zhuge et al. [11] proposed automated glioma-grading system using dataset, the grading accuracy was observed to be 91.3%. Furthermore, Zhuge et al. [11] 3D U-Net and 3D CNN. By training and evaluation using BraTS2018 dataset, the grading proposed automated glioma-grading system using 3D U-Net and 3D CNN. By training accuracy was observed to be 97.1%. and evaluation using BraTS2018 dataset, the grading accuracy was observed to be 97.1%. Nevertheless, many of these studies such as Chen et al. [10] proposed method re- Nevertheless, many of these studies such as Chen et al. [10] proposed method required multi-sequencequired multi-sequence MR images MR images as input, as whichinput, leadswhich to leads a reduction to a reduction in the numberin the number of cases of duecases to due the to lack the of lack specific of specific sequences sequences and increasesand increases the computationalthe computational cost cost by enlargingby enlarg- theinginput the input data data size. size. In addition, In addition, a significant a significant number number of datasets of datasets and high and computationalhigh computa- costtional are cost required are required for training for training a deep a deep learning learning network network [12 ].[12]. However, However, in in the the case case of of usingusing medicalmedical images,images, thethe amountamount ofof availableavailable datadata isis limitedlimited owingowing toto severalseveral challenges,challenges, includingincluding ethicalethical problemsproblems andand lacklack ofof cooperationcooperation amongamong hospitalshospitals [[13,14].13,14]. Additionally, notnot everyoneeveryone cancan installinstall aa high-performancehigh-performance machinemachine thatthat cancan withstandwithstand aasubstantial substantial computationalcomputational loadload [[15,16].15,16]. Therefore,Therefore, fine-tuningfine-tuning aa modelmodel pretrainedpretrained byby natural natural images images contributescontributes towardtoward reducingreducing thethe amountamount ofof medicalmedical imageimage datadata requiredrequired asas wellwell asas thethe computationalcomputational cost cost [17 [17,18].,18]. However, However, it wasit was difficult difficult to adapt to adapt the modelthe model to 3D to images, 3D images, such assuch to brainas to MRbrain images. MR images. Therefore,Therefore, NVIDIA NVIDIA is is attempting attempting to to solve solve this this problem problem by by providing providing models models trained trained by variousby various medical medical images images in the in Clarathe Clara project projec [19].t [19]. A grading A grading system system can be can easily be easily developed devel- usingoped theusing brain the tumorbrain segmentationtumor segmentation model formodel single for sequence single sequence MR images MR ofimages the project. of the Therefore,project. Therefore, in this study, in this we study, developed we devel a fullyoped automated a fully automated glioma-grading glioma-grading pipeline pipeline using post-contrastusing post-contrast T1-weighted T1-weighted images images using usin twog 3D two CNNs—the 3D CNNs—the trained trained model
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