학술논문
Prognostic value of automated assessment of interstitial lung disease on CT in systemic sclerosis.
Document Type
Article
Author
Gall, Aëlle Le; Hoang-Thi, Trieu-Nghi; Porcher, Raphaël; Dunogué, Bertrand; Berezné, Alice; Guillevin, Loïc; Guern, Véronique Le; Cohen, Pascal; Chaigne, Benjamin; London, Jonathan; Groh, Matthieu; Paule, Romain; Chassagnon, Guillaume; Vakalopoulou, Maria; Dinh-Xuan, Anh-Tuan; Revel, Marie Pierre; Mouthon, Luc; Régent, Alexis
Source
Subject
*DEEP learning
*PATIENT aftercare
*CONFIDENCE intervals
*INTERSTITIAL lung diseases
*SYSTEMIC scleroderma
*RISK assessment
*AUTOMATION
*DESCRIPTIVE statistics
*COMPUTED tomography
*DEATH
*PREDICTION models
*RECEIVER operating characteristic curves
*ALGORITHMS
*DISEASE complications
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Language
ISSN
1462-0324
Abstract
Objective Stratifying the risk of death in SSc-related interstitial lung disease (SSc-ILD) is a challenging issue. The extent of lung fibrosis on high-resolution CT (HRCT) is often assessed by a visual semiquantitative method that lacks reliability. We aimed to assess the potential prognostic value of a deep-learning–based algorithm enabling automated quantification of ILD on HRCT in patients with SSc. Methods We correlated the extent of ILD with the occurrence of death during follow-up, and evaluated the additional value of ILD extent in predicting death based on a prognostic model including well-known risk factors in SSc. Results We included 318 patients with SSc, among whom 196 had ILD; the median follow-up was 94 months (interquartile range 73–111). The mortality rate was 1.6% at 2 years and 26.3% at 10 years. For each 1% increase in the baseline ILD extent (up to 30% of the lung), the risk of death at 10 years was increased by 4% (hazard ratio 1.04, 95% CI 1.01, 1.07, P = 0.004). We constructed a risk prediction model that showed good discrimination for 10-year mortality (c index 0.789). Adding the automated quantification of ILD significantly improved the model for 10-year survival prediction (P = 0.007). Its discrimination was only marginally improved, but it improved prediction of 2-year mortality (difference in time-dependent area under the curve 0.043, 95% CI 0.002, 0.084, P = 0.040). Conclusion The deep-learning–based, computer-aided quantification of ILD extent on HRCT provides an effective tool for risk stratification in SSc. It might help identify patients at short-term risk of death. [ABSTRACT FROM AUTHOR]