A computed tomography-based radiomics nomogram for predicting overall survival in patients with connective tissue disease-associated interstitial lung disease

Abstract

ObjectivesAccurate prognostic prediction is beneficial for the management of patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). The purpose of the present study was to develop and validate a nomogram using clinical features and computed tomography (CT) based radiomics features to predict overall survival (OS) in patients with CTD-ILD, and to assess the incremental prognostic value the radiomics might add to clinical risk factors. Materials & methodsPatients from two clinical centers with CTD-ILD were enrolled in the present retrospective study. A radiomics signature, a clinical model and a combined nomogram were developed and assessed in the cohorts. The incremental value of radiomics signature to the clinical independent risk factors in survival prediction was evaluated. The models were externally validated to evaluate the model generalization ability. ResultsA total of 215 patients (mean age, 53 years ± 14 [standard deviation], 45 men) were evaluated. Patients with higher radiomics scores had higher mortality risk than those with lower radiomics scores (Hazard ratio, 12.396; 95% CI, 3.364–45.680; P < 0.001). The combined nomogram showed better predictive capability than the clinical model did with higher C-indices (0.800, 0.738, 0.742 vs. 0.747, 0.631, 0.587 in the training, internal- and external-validation cohort, respectively), time-AUCs and overall net-benefit. ConclusionThe radiomics signature is a potential prognostic biomarker of CTD-ILD and add incremental value to the clinical independent risk factors. The combined nomogram can provide a more accurate estimation of OS than the clinical model for CTD-ILD patients. Clinical relevance statementThe developed combined nomogram showed accurate prognostic prediction performance, which is beneficial for the management of CTD-ILD patients. It also proved radiomics could extract prognostic information from CT images.