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Li Y, Deng W, Zhou Y, Luo Y, Wu Y, Wen J, Cheng L, Liang X, Wu T, Wang F, Huang Z, Tan C, Liu Y. A nomogram based on clinical factors and CT radiomics for predicting anti-MDA5+ DM complicated by RP-ILD. Rheumatology (Oxford) 2024; 63:809-816. [PMID: 37267146 DOI: 10.1093/rheumatology/kead263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/30/2023] [Accepted: 05/08/2023] [Indexed: 06/04/2023] Open
Abstract
OBJECTIVES Anti-melanoma differentiation-associated gene 5 antibody-positive (anti-MDA5+) DM complicated by rapidly progressive interstitial lung disease (RP-ILD) has a high incidence and poor prognosis. The objective of this study was to establish a model for the prediction and early diagnosis of anti-MDA5+ DM-associated RP-ILD based on clinical manifestations and imaging features. METHODS A total of 103 patients with anti-MDA5+ DM were included. The patients were randomly split into training and testing sets of 72 and 31 patients, respectively. After image analysis, we collected clinical, imaging and radiomics features from each patient. Feature selection was performed first with the minimum redundancy and maximum relevance algorithm and then with the best subset selection method. The final remaining features comprised the radscore. A clinical model and imaging model were then constructed with the selected independent risk factors for the prediction of non-RP-ILD and RP-ILD. We also combined these models in different ways and compared their predictive abilities. A nomogram was also established. The predictive performances of the models were assessed based on receiver operating characteristics curves, calibration curves, discriminability and clinical utility. RESULTS The analyses showed that two clinical factors, dyspnoea (P = 0.000) and duration of illness in months (P = 0.001), and three radiomics features (P = 0.001, 0.044 and 0.008, separately) were independent predictors of non-RP-ILD and RP-ILD. However, no imaging features were significantly different between the two groups. The radiomics model built with the three radiomics features performed worse than the clinical model and showed areas under the curve (AUCs) of 0.805 and 0.754 in the training and test sets, respectively. The clinical model demonstrated a good predictive ability for RP-ILD in MDA5+ DM patients, with an AUC, sensitivity, specificity and accuracy of 0.954, 0.931, 0.837 and 0.847 in the training set and 0.890, 0.875, 0.800 and 0.774 in the testing set, respectively. The combination model built with clinical and radiomics features performed slightly better than the clinical model, with an AUC, sensitivity, specificity and accuracy of 0.994, 0.966, 0.977 and 0.931 in the training set and 0.890, 0.812, 1.000 and 0.839 in the testing set, respectively. The calibration curve and decision curve analyses showed satisfactory consistency and clinical utility of the nomogram. CONCLUSION Our results suggest that the combination model built with clinical and radiomics features could reliably predict the occurrence of RP-ILD in MDA5+ DM patients.
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Affiliation(s)
- Yanhong Li
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Wen Deng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Zhou
- Department of Respiratory and Critical Care Medicine, Chengdu First People's Hospital, Chengdu, China
| | - Yubin Luo
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Yinlan Wu
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Ji Wen
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Lu Cheng
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Xiuping Liang
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Tong Wu
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Fang Wang
- Department of Research and Development, Shanghai United Imaging Intelligence, Shanghai, China
| | - Zixing Huang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Chunyu Tan
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
| | - Yi Liu
- Department of Rheumatology and Immunology, West China Hospital, Sichuan University, Chengdu, China
- Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China
- Institute of Immunology and Inflammation, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Chengdu, China
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