Clinical variables and quantitative CT radiomics for mortality risk stratification after acute exacerbation of idiopathic inflammatory myopathy-associated interstitial lung disease
- 1Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University , ,
Abstract
Background: Acute exacerbation (AE) is the most lethal event of idiopathic inflammatory myopathy-associated interstitial lung disease (IIM-ILD). This exploratory study compared clinical, radiomics, and combined predictor strategies across five survival-learning algorithms for survival risk stratification.
Methods: We retrospectively analyzed 78 patients with AE-IIM-ILD and divided them into a training cohort (n = 54; 20 deaths) and a held-out internal testing cohort (n = 24; 9 deaths), stratified by observed mortality event status. Three predictor strategies were evaluated: a clinical strategy comprising PaO ₂ /FiO ₂ ratio, neutrophil percentage (NEU%), and arthritis-related symptoms; a radiomics strategy comprising two selected radiomics features; and a combined strategy comprising PaO ₂ /FiO ₂ ratio, arthritis-related symptoms, and a composite radiomics score (Rad-score). Each strategy was evaluated using five survival-learning algorithms. Harrell’s concordance index (C-index) was the primary discrimination metric, with mean time-dependent area under the curve (AUC) used as a secondary measure.
Results: During the 1-year follow-up, 29 deaths were observed. Performance rankings varied across predictor strategies, algorithms, and evaluation metrics. The combined-Random Survival Forest configuration demonstrated the highest training-cohort C-index among the 15 configurations. Its mean time-dependent AUC was 0.923 (95% CI: 0.860–0.970) in the training cohort and 0.915 (95% CI: 0.736–0.986) in the testing cohort, with corresponding C-indices of 0.874 (95% CI: 0.803–0.940) and 0.842 (95% CI: 0.673–0.971), respectively. The model stratified patients into high- and low-risk groups with distinct observed survival trajectories.
Conclusion: In this exploratory cohort, integrating selected clinical variables with a quantitative CT Rad-score using RSF showed promising numerical discrimination and risk-stratification performance. These findings suggest that clinical and quantitative imaging information may provide complementary prognostic information after AE-IIM-ILD.
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BibTeX
@article{he2026clinical,
title = {{Clinical variables and quantitative CT radiomics for mortality risk stratification after acute exacerbation of idiopathic inflammatory myopathy-associated interstitial lung disease}},
author = {Liyu He and Yanrong Chen and Jingping Zhang and Kai Yang and Shuo Yu and Yaqi Yan and Yuhan Bian and Youmin Guo and Chenwang Jin},
journal = {Frontiers in immunology},
year = {2026},
month = oct,
doi = {10.3389/fimmu.2026.1917500},
url = {https://europepmc.org/article/PMC/PMC13672662},
note = {CC BY},
}
RIS
TY - JOUR
TI - Clinical variables and quantitative CT radiomics for mortality risk stratification after acute exacerbation of idiopathic inflammatory myopathy-associated interstitial lung disease
AU - Liyu He
AU - Yanrong Chen
AU - Jingping Zhang
AU - Kai Yang
AU - Shuo Yu
AU - Yaqi Yan
AU - Yuhan Bian
AU - Youmin Guo
AU - Chenwang Jin
PY - 2026
DA - 2026/10/02
JO - Frontiers in immunology
DO - 10.3389/fimmu.2026.1917500
UR - https://europepmc.org/article/PMC/PMC13672662
AB - Background: Acute exacerbation (AE) is the most lethal event of idiopathic inflammatory myopathy-associated interstitial lung disease (IIM-ILD). This exploratory study compared clinical, radiomics, and combined predictor strategies across five survival-learning algorithms for survival risk stratification. Methods: We retrospectively analyzed 78 patients with AE-IIM-ILD and divided them into a training cohort (n = 54; 20 deaths) and a held-out internal testing cohort (n = 24; 9 deaths), stratified by observed mortality event status. Three predictor strategies were evaluated: a clinical strategy comprising PaO ₂ /FiO ₂ ratio, neutrophil percentage (NEU%), and arthritis-related symptoms; a radiomics strategy comprising two selected radiomics features; and a combined strategy comprising PaO ₂ /FiO ₂ ratio, arthritis-related symptoms, and a composite radiomics score (Rad-score). Each strategy was evaluated using five survival-learning algorithms. Harrell’s concordance index (C-index) was the primary discrimination metric, with mean time-dependent area under the curve (AUC) used as a secondary measure. Results: During the 1-year follow-up, 29 deaths were observed. Performance rankings varied across predictor strategies, algorithms, and evaluation metrics. The combined-Random Survival Forest configuration demonstrated the highest training-cohort C-index among the 15 configurations. Its mean time-dependent AUC was 0.923 (95% CI: 0.860–0.970) in the training cohort and 0.915 (95% CI: 0.736–0.986) in the testing cohort, with corresponding C-indices of 0.874 (95% CI: 0.803–0.940) and 0.842 (95% CI: 0.673–0.971), respectively. The model stratified patients into high- and low-risk groups with distinct observed survival trajectories. Conclusion: In this exploratory cohort, integrating selected clinical variables with a quantitative CT Rad-score using RSF showed promising numerical discrimination and risk-stratification performance. These findings suggest that clinical and quantitative imaging information may provide complementary prognostic information after AE-IIM-ILD.
SN - 1664-3224
ER -