Machine learning-based radiomics to differentiate immune-mediated necrotizing myopathy from limb-girdle muscular dystrophy R2 using MRI

Ping Wei; Huahua Zhong; Qian Xie; Jin Li; Sushan Luo; Xueni Guan; Zonghui Liang; Dongyue Yue

doi:10.3389/fneur.2023.1251025

Machine learning-based radiomics to differentiate immune-mediated necrotizing myopathy from limb-girdle muscular dystrophy R2 using MRI

Front Neurol. 2023 Oct 23:14:1251025. doi: 10.3389/fneur.2023.1251025. eCollection 2023.

Authors

Ping Wei¹, Huahua Zhong², Qian Xie¹, Jin Li¹, Sushan Luo², Xueni Guan¹, Zonghui Liang¹, Dongyue Yue³

Affiliations

¹ Department of Radiology, Jing'an District Center Hospital of Shanghai, Fudan University, Shanghai, China.
² Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
³ Department of Neurology, Jing'an District Center Hospital of Shanghai, Fudan University, Shanghai, China.

Abstract

Objectives: This study aimed to assess the feasibility of a machine learning-based radiomics tools to discriminate between Limb-girdle muscular dystrophy R2 (LGMDR2) and immune-mediated necrotizing myopathy (IMNM) using lower-limb muscle magnetic resonance imaging (MRI) examination.

Methods: After institutional review board approval, 30 patients with genetically proven LGMDR2 (12 females; age, 34.0 ± 11.3) and 45 patients with IMNM (28 females; age, 49.2 ± 16.6) who underwent lower-limb MRI examination including T1-weighted and interactive decomposition water and fat with echos asymmetric and least-squares estimation (IDEAL) sequences between July 2014 and August 2022 were included. Radiomics features of muscles were obtained, and four machine learning algorithms were conducted to select the optimal radiomics classifier for differential diagnosis. This selected algorithm was performed to construct the T1-weighted (TM), water-only (WM), or the combined model (CM) for calf-only, thigh-only, or the calf and thigh MR images, respectively. And their diagnostic performance was studied using area under the curve (AUC) and compared to the semi-quantitative model constructed by the modified Mercuri scale of calf and thigh muscles scored by two radiologists specialized in musculoskeletal imaging.

Results: The logistic regression (LR) model was the optimal radiomics model. The performance of the WM and CM for thigh-only images (AUC 0.893, 0.913) was better than those for calf-only images (AUC 0.846, 0.880) except the TM. For "calf + thigh" images, the TM, WM, and CM models always performed best (AUC 0.953, 0.907, 0.953) with excellent accuracy (92.0, 84.0, 88.0%). The AUCs of the Mercuri model of the calf, thigh, and "calf + thigh" images were 0.847, 0.900, and 0.953 with accuracy (84.0, 84.0, 88.0%).

Conclusion: Machine learning-based radiomics models can differentiate LGMDR2 from IMNM, performing better than visual assessment. The model built by combining calf and thigh images presents excellent diagnostic efficiency.

Keywords: fatty infiltration; immune-mediated necrotizing myopathy; limb-girdle muscular dystrophy; machine learning; muscle MRI; radiomics.

Grants and funding

This research was funded by Medical Discipline Construction Plan of Shanghai Jing’an District Health Commission (grant number 2021PY01), special project of medical innovation research of Shanghai Science and Technology Innovation Action Plan (grant number 21Y11910600), the Scientific Research Foundation of Shanghai Municipal Health Commission (grant number 202140244), and the Medical Scientific Research Foundation of Jing’an District (grant number 2019MS01).