Abstract
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.
Highlights
Orbital lymphoproliferative disorders (OLPDs) consist of a broad range of benign and malignant tumors [1, 2]
The patterns were further confirmed by Jin et al who evaluated the computed tomography (CT) imaging and magnetic resonance imaging (MRI) features of primary orbital lymphoma to establish a differential diagnosis in 14 cases, reporting that periorbital preseptal tissues were mainly involved in the upper lateral quadrant of the orbit [7]
Since previous studies suggested that MR imaging is a promising tool to accurately visualize the location, shape, and internal structure of orbital lymphoma [1, 2, 7, 11]; in this study, we assessed the value of conventional MR images in machine learning-based radiomics approaches for clinical identification of orbital lymphoma and IgG4-ROD
Summary
Orbital lymphoproliferative disorders (OLPDs) consist of a broad range of benign and malignant tumors [1, 2]. The patterns were further confirmed by Jin et al who evaluated the computed tomography (CT) imaging and magnetic resonance imaging (MRI) features of primary orbital lymphoma to establish a differential diagnosis in 14 cases, reporting that periorbital preseptal tissues were mainly involved in the upper lateral quadrant of the orbit [7] They suggested that MRI may be very useful for assessing the location, configuration, inner structure, BioMed Research International and characteristic manifestations of orbital lymphomas [7]. Since previous studies suggested that MR imaging is a promising tool to accurately visualize the location, shape, and internal structure of orbital lymphoma [1, 2, 7, 11]; in this study, we assessed the value of conventional MR images in machine learning-based radiomics approaches for clinical identification of orbital lymphoma and IgG4-ROD
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