Spatial–Texture Hybrid MRI Model for Orbital Lymphoma Typing

The ability to distinguish between mucosa‐associated lymphoid tissue (MALT) and non‐MALT orbital lymphomas aids ophthalmologists in opting for either conservative or aggressive treatment strategies. Radiographic assessment is a noninvasive approach to diagnose orbital lesions. This study aims to develop a hybrid model leveraging magnetic resonance imaging scans to discern between MALT and non‐MALT orbital lymphomas. The occupation of the tumor alters the relative positions of structures in the orbit. Hence, for the first time, the relative spatial positional features are extracted between different orbital structures and the tumor, complemented by the texture characteristics of the tumor area, to perform hybrid modeling. To validate this idea, 114 orbital lymphoma patients were are included. Statistical analysis reveals significant differences between the two groups in terms of four spatial features (lymphoma lesion, eyeball, inferior rectus, and optic nerve) and two texture features (angular second moment and contrast). The accuracy of the classifier based on spatial, texture, and hybrid features is 84.7, 83.1, and 88.3%, respectively. The innovative hybrid model offers a supportive approach for the differentiation of MALT and non‐MALT orbital lymphomas, enhancing the clinical decision‐making process. To facilitate the use of this hybrid model, a web‐based diagnostic tool has been launched at https://ads.testop.top.

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Publication Details

Journal
Advanced Intelligent Systems
Published
2025-03-02
DOI
https://doi.org/10.1002/aisy.202400595
Citations
1
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
1.36
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Spatial–Texture Hybrid MRI Model for Orbital Lymphoma Typing

Wei Lai, Yixiong Zhou, Menghan Hu, Lunhao Li et al.
1 citations
Advanced Intelligent Systems
Radiomics and Machine Learning in Medical Imaging
1.36
article

Spatial–Texture Hybrid MRI Model for Orbital Lymphoma Typing

Wei Lai, Yixiong Zhou, Menghan Hu, Lunhao Li, Jiahao Shi
article en
1 citations

Abstract

The ability to distinguish between mucosa‐associated lymphoid tissue (MALT) and non‐MALT orbital lymphomas aids ophthalmologists in opting for either conservative or aggressive treatment strategies. Radiographic assessment is a noninvasive approach to diagnose orbital lesions. This study aims to develop a hybrid model leveraging magnetic resonance imaging scans to discern between MALT and non‐MALT orbital lymphomas. The occupation of the tumor alters the relative positions of structures in the orbit. Hence, for the first time, the relative spatial positional features are extracted between different orbital structures and the tumor, complemented by the texture characteristics of the tumor area, to perform hybrid modeling. To validate this idea, 114 orbital lymphoma patients were are included. Statistical analysis reveals significant differences between the two groups in terms of four spatial features (lymphoma lesion, eyeball, inferior rectus, and optic nerve) and two texture features (angular second moment and contrast). The accuracy of the classifier based on spatial, texture, and hybrid features is 84.7, 83.1, and 88.3%, respectively. The innovative hybrid model offers a supportive approach for the differentiation of MALT and non‐MALT orbital lymphomas, enhancing the clinical decision‐making process. To facilitate the use of this hybrid model, a web‐based diagnostic tool has been launched at https://ads.testop.top.

Advanced Intelligent SystemsVol. 7(5)
Shanghai Jiao Tong University (CN), Shanghai Ninth People's Hospital (CN), East China Normal University (CN)
Openalex Percentile: Top 23%
Radiomics and Machine Learning in Medical Imaging
1.36
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