Literature DB >> 33628805

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

Ye Yuan1,2, Guangyu Chu1, Tingting Gong1, Lianze Du1, Lizhi Xie2, Qinghai Yuan1, Qinghe Han1.   

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.
Copyright © 2021 Ye Yuan et al.

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Year:  2021        PMID: 33628805      PMCID: PMC7884128          DOI: 10.1155/2021/6668510

Source DB:  PubMed          Journal:  Biomed Res Int            Impact factor:   3.411


  33 in total

Review 1.  Diagnostic criteria for IgG4-related ophthalmic disease.

Authors:  Hiroshi Goto; Masayuki Takahira; Masahiro Takahira; Atsushi Azumi
Journal:  Jpn J Ophthalmol       Date:  2014-11-14       Impact factor: 2.447

2.  Quantitative characterization of extraocular orbital lesions in children using diffusion-weighted imaging.

Authors:  Francisco R Maldonado; Juan P Princich; Lucia Micheletti; María S Toronchik; José I Erripa; Carlos Rugilo
Journal:  Pediatr Radiol       Date:  2020-09-08

3.  Differentiation of clear cell and non-clear cell renal cell carcinomas by all-relevant radiomics features from multiphase CT: a VHL mutation perspective.

Authors:  Zhi-Cheng Li; Guangtao Zhai; Jinheng Zhang; Zhongqiu Wang; Guiqin Liu; Guang-Yu Wu; Dong Liang; Hairong Zheng
Journal:  Eur Radiol       Date:  2018-12-06       Impact factor: 5.315

Review 4.  Imaging of orbital lymphoproliferative disorders.

Authors:  G E Valvassori; S S Sabnis; R F Mafee; M S Brown; A Putterman
Journal:  Radiol Clin North Am       Date:  1999-01       Impact factor: 2.303

Review 5.  Orbital lymphoma.

Authors:  Tine Gadegaard Olsen; Steffen Heegaard
Journal:  Surv Ophthalmol       Date:  2018-08-23       Impact factor: 6.048

6.  Intravoxel incoherent motion (IVIM) 3 T MRI for orbital lesion characterization.

Authors:  Augustin Lecler; Loïc Duron; Mathieu Zmuda; Kevin Zuber; Olivier Bergès; Marc Putterman; Julien Savatovsky; Laure Fournier
Journal:  Eur Radiol       Date:  2020-08-01       Impact factor: 5.315

7.  Differential Diagnosis of Solitary Fibrous Tumor/Hemangiopericytoma and Angiomatous Meningioma Using Three-Dimensional Magnetic Resonance Imaging Texture Feature Model.

Authors:  Junyi Dong; Meimei Yu; Yanwei Miao; Huicong Shen; Yi Sui; Yangyingqiu Liu; Liang Han; Xiaoxin Li; Meiying Lin; Yan Guo; Lizhi Xie
Journal:  Biomed Res Int       Date:  2020-12-01       Impact factor: 3.411

8.  Characterization of diffuse orbital mass using Apparent diffusion coefficient in 3-tesla MRI.

Authors:  Sahar M ElKhamary; Alicia Galindo-Ferreiro; Laila AlGhafri; Rajiv Khandekar; Silvana Artioli Schellini
Journal:  Eur J Radiol Open       Date:  2018-03-26

9.  Differentiation of orbital lymphoma and idiopathic orbital inflammatory pseudotumor: combined diagnostic value of conventional MRI and histogram analysis of ADC maps.

Authors:  Jiliang Ren; Ying Yuan; Yingwei Wu; Xiaofeng Tao
Journal:  BMC Med Imaging       Date:  2018-05-02       Impact factor: 1.930

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

Authors:  Xin Chen; Min Zeng; Yichen Tong; Tianjing Zhang; Yan Fu; Haixia Li; Zhongping Zhang; Zixuan Cheng; Xiangdong Xu; Ruimeng Yang; Zaiyi Liu; Xinhua Wei; Xinqing Jiang
Journal:  Biomed Res Int       Date:  2020-09-23       Impact factor: 3.411

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  2 in total

1.  A deep learning model combining multimodal radiomics, clinical and imaging features for differentiating ocular adnexal lymphoma from idiopathic orbital inflammation.

Authors:  Xiaoyang Xie; Lijuan Yang; Fengjun Zhao; Dong Wang; Hui Zhang; Xuelei He; Xin Cao; Huangjian Yi; Xiaowei He; Yuqing Hou
Journal:  Eur Radiol       Date:  2022-06-08       Impact factor: 7.034

2.  Clinical and Radiological Features of Diffuse Lacrimal Gland Enlargement: Comparisons among Various Etiologies in 91 Biopsy-Confirmed Patients.

Authors:  Sae Rom Chung; Gye Jung Kim; Young Jun Choi; Kyung-Ja Cho; Chong Hyun Suh; Soo Chin Kim; Jung Hwan Baek; Jeong Hyun Lee; Min Kyu Yang; Ho-Seok Sa
Journal:  Korean J Radiol       Date:  2022-08-31       Impact factor: 7.109

  2 in total

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