2026-10-12T07:49:24Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34054521 2026-10-02T05:36:08Z category_696 portal_316 item_type_3 month_year_10_2026

Supplementary file 1_Clinical variables and quantitative CT radiomics for mortality risk stratification after acute exacerbation of idiopathic inflammatory myopathy-associated interstitial lung disease.pdf Liyu He (4616383) Yanrong Chen (2611009) Jingping Zhang (523269) Kai Yang (73590) Shuo Yu (3276831) Yaqi Yan (11774396) Yuhan Bian (14299118) Youmin Guo (527309) Chenwang Jin (479314) Genetic Immunology acute exacerbation idiopathic inflammatory myopathy interstitial lung disease PaO2/FiO2 prognostic model radiomics risk stratification SHAP Background<p>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.</p>Methods<p>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 PaO₂/FiO₂ ratio, neutrophil percentage (NEU%), and arthritis-related symptoms; a radiomics strategy comprising two selected radiomics features; and a combined strategy comprising PaO₂/FiO₂ 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.</p>Results<p>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.</p>Conclusion<p>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.</p> 2026-10-02T05:36:08Z Dataset Dataset 10.3389/fimmu.2026.1917500.s001 https://figshare.com/articles/dataset/Supplementary_file_1_Clinical_variables_and_quantitative_CT_radiomics_for_mortality_risk_stratification_after_acute_exacerbation_of_idiopathic_inflammatory_myopathy-associated_interstitial_lung_disease_pdf/34054521 CC BY 4.0