Radiomic features detect interstitial lung disease in patients with systemic sclerosis

Abstract

Rationale: Interstitial lung disease (ILD) is a common complication of systemic sclerosis (SSc) associated with significant morbidity and mortality. Professional society guidelines vary but some recommend screening all patients with SSc at diagnosis with computed tomography (CT). Early detection of ILD, however, remains challenging, as subtle CT features can be of unclear significance. Radiomics, which involves extracting quantitative features from information-rich medical imaging, can capture subtle textural and structural changes that are not easily observable to radiologists. We hypothesize that by leveraging radiomic features, machine learning models can aid clinicians in early ILD detection and risk stratification in SSc. Methods: We analyzed CT scans performed between 2015-2024 on patients with SSc enrolled in the Northwestern Scleroderma Registry. Radiomic feature extraction yielded 107 first-order features that detailed the lung's texture, shape, and intensity patterns. Principal component analysis (PCA) was utilized to reduce these features to 12 principal components, capturing the majority of variance in the data. Optuna, an optimization library, was employed to build models to detect ILD using radiomic features. These models included XGBoost, random forest, logistic regression, LightGBM, and gradient boosting. Receiver operator characteristic analysis was implemented to assess the accuracy of model predictions using an 64:16:20 train:validation:test split. Large Language Models (LLMs) were used to assist in plot generation with authors reviewing output. Results: 1221 CT scans were available for analysis from 434 patients with SSc (83% female, median age 58yr [IQR: 50, 66]). 808 CTs (66%) had evidence of ILD reported by a thoracic radiologist. PCA plots of distinct patterns of radiomic features from CT scans clustered patients with and without ILD (Figure 1A). Using ML algorithms via the Optuna framework, radiomic features successfully detected ILD in SSc patients. We used the highest validation area under the receiver operating characteristic curve (AUROC) to select the model and apply on an unseen test set and obtained an AUROC of 0.88 (Figure 1B). Conclusion: Distinct radiomic patterns were identified through ML algorithms that detect ILD in SSc patients with good discrimination. Our findings underscore the utility of radiomics in diagnosis in this high-risk population. Future research will integrate clinical data with radiomic features via deep learning models to improve predictive performance, thereby improving the care of patients with systemic sclerosis.