Computed tomography-based radiomics decodes prognostic and molecular differences in interstitial lung disease related to systemic sclerosis

Abstract

BackgroundRadiomic features calculated from routine medical images show great potential for personalized medicine in cancer. Patients with systemic sclerosis (SSc), a rare, multi-organ autoimmune disorder, have a similarly poor prognosis due to interstitial lung disease (ILD).ObjectivesTo explore computed tomography (CT)-based high-dimensional image analysis (radiomics) for disease characterisation, risk stratification, and relaying information on lung pathophysiology in SSc-ILD.MethodsWe investigated two independent, prospectively followed SSc-ILD cohorts (Zurich, derivation cohort, n=90; Oslo, validation cohort, n=66). For every subject, we defined 1′355 robust radiomic features from standard-of-care CT images. We performed unsupervised clustering to identify and characterize imaging-based patient clusters. A clinically applicable prognostic quantitative radiomic risk score (qRISSc) for progression-free survival was derived from radiomic profiles using supervised analysis. The biological basis of qRISSc was assessed in a cross-species approach by correlation with lung proteomics, histological and gene expression data derived from mice with bleomycin-induced lung fibrosis.ResultsRadiomic profiling identified two clinically and prognostically distinct SSc-ILD patient clusters. To evaluate the clinical applicability, we derived and externally validated a binary, quantitative radiomic risk score composed of 26 features, qRISSc, that accurately predicted progression-free survival and significantly improved upon clinical risk stratification parameters in multivariable Cox regression analyses in the pooled cohorts. A high qRISSc score, which identifies patients at risk for progression, was reverse translatable from human to experimental ILD and correlated with fibrotic pathway activation.ConclusionsRadiomics-based risk stratification using routine CT images provides complementary phenotypic, clinical and prognostic information significantly impacting clinical decision-making in SSc-ILD.

Systemic sclerosis: one year in review 2022

“…In contrast, radiomics is an objective imaging-based tool that enables a more detailed and reliable quantitative assessment of lesion features. Schniering et al proposed a binary radiomic risk score composed of 26 features which accurately predicted progression-free survival (65) stage with a sensitivity of 84% and a specificity of almost 100% (66). Quantitative computed tomography (qCT) may become an objective tool for the assessment of ILD in SSc, complementary to PFTs and visual fibrosis scores.…”

“…• Radiomics and lung ultrasound (LUS) are precise, sensitive, and specific tools able to improve the detection and the characterisation of SSc-ILD severity (65,66,72,73).…”

The Use and Utility of Machine Learning in Achieving Precision Medicine in Systemic Sclerosis: A Narrative Review

“…The added value of radiomic analysis lies in the ability to capture tissue phenotypes on different spatial scales ranging from the radiological/macroscopic to the molecular/microscopic levels [ 50 ]. Schniering et al [ 35 ] used an ML approach for radiomic analysis of lung CT in SSc patients to identify homogeneous imaging-based ILD clusters. The analysis produced two distinct and stable patient clusters based on their radiomic profiles.…”

“…The differences in clinical characteristics were substantial, with patients in cluster 2 ( n = 31) having a significantly more impaired lung performance in the 6 min walk test and a higher frequency of pulmonary hypertension than patients in cluster 1 ( n = 59). Cluster 2 was also significantly enriched for honeycombing as a radiological sign of more severe fibrotic lung remodeling [ 35 ].…”

The potential and challenges of radiomics in uncovering prognostic and molecular differences in interstitial lung disease associated with systemic sclerosis

“…Lu Zhang 1* , PhD; Jieling Zheng 1* , PhD; Zhe Jin, MD; Qiuying Chen, PhD; Shuyi Liu, PhD; Bin To the editor: We read with great interest the study by Dr. Janine and colleagues [1], recently published in European Respiratory Journal. This study highlighted the potential of radiomics as a non-invasive tool for disease characterization, prognosis stratification, and lung pathophysiology evaluation in systemic sclerosis-associated interstitial lung disease (SSc-ILD).…”