Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging

Shaver, Madeleine; Kohanteb, Paul A; Chiou, Catherine; Bardis, Michelle; Chantaduly, Chanon; Bota, Daniela; Filippi, Christopher G.; Weinberg, Brent D.; Grinband, Jack; Chow, Daniel; Chang, Peter D.

doi:10.3390/cancers11060829

Cited by 84 publications

(79 citation statements)

References 69 publications

(87 reference statements)

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“…Fast and reliable labeling (contouring) of these segments is a crucial task in many areas of neuro-oncology such as radiotherapy and image-based follow-up [ 44 ]. Numerous algorithms have been developed for this purpose [ 45 ] and a periodically held challenge (the Multimodal Brain Tumor Image Segmentation Benchmark, BRATS) has been set up to compare their efficacy [ 41 , 46 ]. Today, the best-performing tools are usually based on CNNs [ 47 , 48 ], which achieve high segmentation accuracies (Dice similarity coefficients) where almost 90% of the voxels are correctly labeled, which is the order of magnitude that experienced physicians can achieve [ 44 ].…”

Section: Gliomamentioning

confidence: 99%

Applications of radiomics and machine learning for radiotherapy of malignant brain tumors

et al. 2020

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Background Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors. Methods This study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases. Results Feature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80–90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods. Conclusion Clinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.

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Section: Gliomamentioning

confidence: 99%

Applications of radiomics and machine learning for radiotherapy of malignant brain tumors

et al. 2020

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“…[14] Machine [15] Human brain study using AI 6317 bibliometric analysis from 19 to 350). Besides, the existing reviews focus on narrowed and particular topics, for example, deep learning approaches for glioma imaging [7], machine learning in acute ischemic stroke [8], and AI in stroke imaging [6], failing to provide a general overview of the community of AI-enhanced human brain research. In addition, these qualitative reviews on specific topics or bibliometric analyses based primarily on metadata of scientific publications (e.g., year of publication or citation index) cannot accommodate the wide and fast-growing research and application scopes of modern AI-assisted human brain research.…”

Section: -2019mentioning

confidence: 99%

Topics and trends in artificial intelligence assisted human brain research

Chen

Cheng

et al. 2020

PLoS ONE

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Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in application scope of AIassisted human brain research. A comprehensive understanding of this field is necessary to assess research efficacy, (re)allocate research resources, and conduct collaborations. This paper combines the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale, unstructured text of AIassisted human brain research publications in the past decade. Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics, promising research orientations, and diverse topical distributions in influential countries/regions and research institutes. These findings help better understand scientific and technological AIassisted human brain research, provide insightful guidance for resource (re)allocation, and promote effective international collaborations.

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“…Artificial intelligence (AI) is a promising tool to improve prostate lesion detection, lesion characterization, and lesion volume quantification. AI can systematically evaluate mpMRI images [38]. Machine learning (ML), a branch of AI, and its sub-discipline, deep learning (DL), have become attractive techniques in medical imaging because of their ability to interpret large amounts of data [39].…”

Section: Introductionmentioning

confidence: 99%

Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends

Bardis

Houshyar

Chang

et al. 2020

Cancers

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Prostate carcinoma is one of the most prevalent cancers worldwide. Multiparametric magnetic resonance imaging (mpMRI) is a non-invasive tool that can improve prostate lesion detection, classification, and volume quantification. Machine learning (ML), a branch of artificial intelligence, can rapidly and accurately analyze mpMRI images. ML could provide better standardization and consistency in identifying prostate lesions and enhance prostate carcinoma management. This review summarizes ML applications to prostate mpMRI and focuses on prostate organ segmentation, lesion detection and segmentation, and lesion characterization. A literature search was conducted to find studies that have applied ML methods to prostate mpMRI. To date, prostate organ segmentation and volume approximation have been well executed using various ML techniques. Prostate lesion detection and segmentation are much more challenging tasks for ML and were attempted in several studies. They largely remain unsolved problems due to data scarcity and the limitations of current ML algorithms. By contrast, prostate lesion characterization has been successfully completed in several studies because of better data availability. Overall, ML is well situated to become a tool that enhances radiologists’ accuracy and speed.

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Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging

Cited by 84 publications

References 69 publications

Applications of radiomics and machine learning for radiotherapy of malignant brain tumors

Applications of radiomics and machine learning for radiotherapy of malignant brain tumors

Topics and trends in artificial intelligence assisted human brain research

Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends

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