Automatic Prediction of MGMT Status in Glioblastoma via Deep Learning-Based MR Image Analysis

Chen, Xin; Zeng, Min; Tong, Yichen; Fu, Yan; Li, Haixia; Zhang, Zhongping; Cheng, Zixuan; Xu, Xiangdong; Yang, Ruimeng; Liu, Zaiyi; Wei, Xinhua; Jiang, Xinqing

doi:10.1155/2020/9258649

Cited by 24 publications

(19 citation statements)

References 40 publications

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“…Radiomics has been widely explored for intelligent diagnosis [ 18 – 20 ]. It extracts quantitative features from medical images using advanced algorithms [ 21 – 23 ], and the features are further mined for disease diagnosis and cancer staging [ 24 – 27 ]. However, to the best of our knowledge, no machine learning-based radiomics models have yet been designed for orbital lymphoma and IgG4-ROD.…”

Section: Introductionmentioning

confidence: 99%

To Explore MR Imaging Radiomics for the Differentiation of Orbital Lymphoma and IgG4-Related Ophthalmic Disease

Yuan

Chu

Gong

et al. 2021

BioMed Research International

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Among orbital lymphoproliferative disorders, about 55% of diagnosed cancerous tumors are orbital lymphomas, and nearly 50% of benign cases are immunoglobulin G4-related ophthalmic disease (IgG4-ROD). However, due to nonspecific characteristics, the differentiation of the two diseases is challenging. In this study, conventional magnetic resonance imaging-based radiomics approaches were explored for clinical recognition of orbital lymphomas and IgG4-ROD. We investigated the value of radiomics features of axial T1- (T1WI-) and T2-weighted (T2WI), contrast-enhanced T1WI in axial (CE-T1WI) and coronal (CE-T1WI-cor) planes, and 78 patients (orbital lymphoma, 36; IgG4-ROD, 42) were retrospectively reviewed. The mass lesions were manually annotated and represented with 99 features. The performance of elastic net-based radiomics models using single or multiple modalities with or without feature selection was compared. The demographic features showed orbital lymphoma patients were significantly older than IgG4-ROD patients ( p < 0.01 ), and most of the patients were male (72% in the orbital lymphoma group vs. 23% in the IgG4-ROD group; p = 0.03 ). The MR imaging findings revealed orbital lymphomas were mostly unilateral (81%, p = 0.02 ) and wrapped eyeballs or optic nerves frequently (78%, p = 0.02 ). In addition, orbital lymphomas showed isointense in T1WI (100%, p < 0.01 ), and IgG4-ROD was isointense (60%, p < 0.01 ) or hyperintense (40%, p < 0.01 ) in T1WI with well-defined shape (64%, p < 0.01 ). The experimental comparison indicated that using CE-T1WI radiomics features achieved superior results, and the features in combination with CE-T1WI-cor features and the feature preselection method could further improve the classification performance. In conclusion, this study comparatively analyzed orbital lymphoma and IgG4-ROD from demographic features, MR imaging findings, and radiomics features. It might deepen our understanding and benefit disease management.

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Section: Introductionmentioning

confidence: 99%

To Explore MR Imaging Radiomics for the Differentiation of Orbital Lymphoma and IgG4-Related Ophthalmic Disease

Yuan

Chu

Gong

et al. 2021

BioMed Research International

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“…e model presented in this manuscript provides a 0.915 Dice Score for glioma segmentation with the Figshare data set [11,13], with the faster R-CNN and Chan-Vese algorithms. In comparison, the authors in [41] have developed a [20] for an accurate glioma segmentation algorithm which obtained 0.897 Dice Score. An automatic semantic segmentation model was developed on the BRATS 2013 dataset by the authors in [42] and the Dice Score was around 0.80.…”

Section: Results Of the Segmentation Modelmentioning

confidence: 99%

“…Due to the easy accessibility and the ready availability, the Figshare MRI brain tumour dataset also has been used in many brain tumor classification and segmentation related research [15][16][17][18]. e dataset, which was initiated in 2015 and last updated in 2017 [13,16], carries an average classification accuracy in the range of 90-95% [14,16,19,20]. e authors in [16] achieved a classification average of 95% accuracy by using a modified CNN architecture while the authors in [15] achieved around 96% accuracy with an automatic content-based image retrieval (CBIR) system.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring

Gunasekara

Kaldera

Dissanayake

2021

Journal of Healthcare Engineering

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One of the main requirements of tumor extraction is the annotation and segmentation of tumor boundaries correctly. For this purpose, we present a threefold deep learning architecture. First, classifiers are implemented with a deep convolutional neural network (CNN) and second a region-based convolutional neural network (R-CNN) is performed on the classified images to localize the tumor regions of interest. As the third and final stage, the concentrated tumor boundary is contoured for the segmentation process by using the Chan–Vese segmentation algorithm. As the typical edge detection algorithms based on gradients of pixel intensity tend to fail in the medical image segmentation process, an active contour algorithm defined with the level set function is proposed. Specifically, the Chan–Vese algorithm was applied to detect the tumor boundaries for the segmentation process. To evaluate the performance of the overall system, Dice Score, Rand Index (RI), Variation of Information (VOI), Global Consistency Error (GCE), Boundary Displacement Error (BDE), Mean Absolute Error (MAE), and Peak Signal to Noise Ratio (PSNR) were calculated by comparing the segmented boundary area which is the final output of the proposed, against the demarcations of the subject specialists which is the gold standard. Overall performance of the proposed architecture for both glioma and meningioma segmentation is with an average Dice Score of 0.92 (also, with RI of 0.9936, VOI of 0.0301, GCE of 0.004, BDE of 2.099, PSNR of 77.076, and MAE of 52.946), pointing to the high reliability of the proposed architecture.

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“…The CNN-based classifiers achieved the accuracy from 82.7% to 94.9% in predicting the methylation status of MGMT promoter in GB ( 74 , 75 ). Chen et al.…”

Section: Progress Of Imaging-genomics In Gbmentioning

confidence: 99%

“…Chen et al. ( 74 ) utilized a deep learning pipeline for automatic tumor segmentation and MGMT promoter status prediction in an end-to-end manner for GB patients. The better tumor segmentation and MGMT prediction performance both came from Fluid-attenuated inversion recovery (FLAIR) images.…”

Section: Progress Of Imaging-genomics In Gbmentioning

confidence: 99%

Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures

Liu

Chen

et al. 2021

Front. Oncol.

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Based on artificial intelligence (AI), computer-assisted medical diagnosis can scientifically and efficiently deal with a large quantity of medical imaging data. AI technologies including deep learning have shown remarkable progress across medical image recognition and genome analysis. Imaging-genomics attempts to explore the associations between potential gene expression patterns and specific imaging phenotypes. These associations provide potential cellular pathophysiology information, allowing sampling of the lesion habitat with high spatial resolution. Glioblastoma (GB) poses spatial and temporal heterogeneous characteristics, challenging to current precise diagnosis and treatments for the disease. Imaging-genomics provides a powerful tool for non-invasive global assessment of GB and its response to treatment. Imaging-genomics also has the potential to advance our understanding of underlying cancer biology, gene alterations, and corresponding biological processes. This article reviews the recent progress in the utilization of the imaging-genomics analysis in GB patients, focusing on its implications and prospects in individualized diagnosis and management.

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Automatic Prediction of MGMT Status in Glioblastoma via Deep Learning-Based MR Image Analysis

Cited by 24 publications

References 40 publications

To Explore MR Imaging Radiomics for the Differentiation of Orbital Lymphoma and IgG4-Related Ophthalmic Disease

To Explore MR Imaging Radiomics for the Differentiation of Orbital Lymphoma and IgG4-Related Ophthalmic Disease

A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring

Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures

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