Editorial for “Deep Learning for Noninvasive Assessment of <scp>H3 K27M</scp> Mutation Status in Diffuse Midline Gliomas Using <scp>MR</scp> Imaging”

Bonacchi, Raffaello; Meo, Ermelinda De

doi:10.1002/jmri.28615

Magnetic Resonance Imaging

2023

DOI: 10.1002/jmri.28615

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Editorial for “Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging”

Raffaello Bonacchi

Ermelinda De Meo

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2024

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Cited by 1 publication

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Deep Learning‐Based Techniques in Glioma Brain Tumor Segmentation Using Multi‐Parametric MRI: A Review on Clinical Applications and Future Outlooks

Ghadimi,

Vahdani,

Karimi

et al. 2024

Magnetic Resonance Imaging

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This comprehensive review explores the role of deep learning (DL) in glioma segmentation using multiparametric magnetic resonance imaging (MRI) data. The study surveys advanced techniques such as multiparametric MRI for capturing the complex nature of gliomas. It delves into the integration of DL with MRI, focusing on convolutional neural networks (CNNs) and their remarkable capabilities in tumor segmentation. Clinical applications of DL‐based segmentation are highlighted, including treatment planning, monitoring treatment response, and distinguishing between tumor progression and pseudo‐progression. Furthermore, the review examines the evolution of DL‐based segmentation studies, from early CNN models to recent advancements such as attention mechanisms and transformer models. Challenges in data quality, gradient vanishing, and model interpretability are discussed. The review concludes with insights into future research directions, emphasizing the importance of addressing tumor heterogeneity, integrating genomic data, and ensuring responsible deployment of DL‐driven healthcare technologies.Evidence LevelN/ATechnical EfficacyStage 2

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Deep Learning‐Based Techniques in Glioma Brain Tumor Segmentation Using Multi‐Parametric MRI: A Review on Clinical Applications and Future Outlooks

Ghadimi,

Vahdani,

Karimi

et al. 2024

Magnetic Resonance Imaging

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Editorial for “Deep Learning for Noninvasive Assessment of H3 K27M Mutation Status in Diffuse Midline Gliomas Using MR Imaging”

Cited by 1 publication

References 10 publications

Deep Learning‐Based Techniques in Glioma Brain Tumor Segmentation Using Multi‐Parametric MRI: A Review on Clinical Applications and Future Outlooks

Deep Learning‐Based Techniques in Glioma Brain Tumor Segmentation Using Multi‐Parametric MRI: A Review on Clinical Applications and Future Outlooks

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