Automated AI-based Image Analysis for Quantification and Prediction of Interstitial Lung Disease in Systemic Sclerosis Patients

Overview

Journal Respir Res

Date 2025 Jan 25

PMID 39856708

Authors

Julien Guiot

Monique Henket

Fanny Gester

Beatrice Andre

Benoit Ernst

Anne-Noelle Frix

Dirk Smeets

Simon Van Eyndhoven

Katerina Antoniou

Lennart Conemans

Janine Gote-Schniering

Hans Slabbynck

Michael Kreuter

Jacobo Sellares

Ioannis Tomos

Guang Yang

Clio Ribbens

Renaud Louis

Vincent Cottin

Sara Tomassetti

Vanessa Smith

Simon L F Walsh

Affiliations

Soon will be listed here.

Abstract

Background: Systemic sclerosis (SSc) is a rare connective tissue disease associated with rapidly evolving interstitial lung disease (ILD), driving its mortality. Specific imaging-based biomarkers associated with the evolution of lung disease are needed to help predict and quantify ILD.

Methods: We evaluated the potential of an automated ILD quantification system (icolung) from chest CT scans, to help in quantification and prediction of ILD progression in SSc-ILD. We used a retrospective cohort of 75 SSc-ILD patients to evaluate the potential of the AI-based quantification tool and to correlate image-based quantification with pulmonary function tests and their evolution over time.

Results: We evaluated a group of 75 patients suffering from SSc-ILD, either limited or diffuse, of whom 30 presented progressive pulmonary fibrosis (PPF). The patients presenting PPF exhibited more extensive lesions (in % of total lung volume (TLV)) based on image analysis than those without PPF: 3.93 (0.36-8.12)* vs. 0.59 (0.09-3.53) respectively, whereas pulmonary functional test showed a reduction in Force Vital Capacity (FVC)(pred%) in patients with PPF compared to the others : 77 ± 20% vs. 87 ± 19% (p < 0.05). Modifications of FVC and diffusing capacity of the lungs for carbon monoxide (DLCO) over time were correlated with longitudinal radiological ILD modifications (r=-0.40, p < 0.01; r=-0.40, p < 0.01 respectively).

Conclusion: AI-based automatic quantification of lesions from chest-CT images in SSc-ILD is correlated with physiological parameters and can help in disease evaluation. Further clinical multicentric validation is necessary in order to confirm its potential in the prediction of patient's outcome and in treatment management.

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