Prognostic value of automated assessment of interstitial lung disease on CT in systemic sclerosis
© The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissionsoup.com..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:63 |
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| Enthalten in: |
Rheumatology (Oxford, England) - 63(2024), 1 vom: 04. Jan., Seite 103-110 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Le Gall, Aëlle [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Completed 05.01.2024 Date Revised 05.01.2024 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/rheumatology/kead164 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM355787660 |
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| 245 | 1 | 0 | |a Prognostic value of automated assessment of interstitial lung disease on CT in systemic sclerosis |
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| 500 | |a Date Revised 05.01.2024 | ||
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author(s) 2023. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissionsoup.com. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a SSc | |
| 650 | 4 | |a artificial intelligence | |
| 650 | 4 | |a high-resolution CT | |
| 650 | 4 | |a interstitial lung disease | |
| 650 | 4 | |a prognosis | |
| 700 | 1 | |a Hoang-Thi, Trieu-Nghi |e verfasserin |4 aut | |
| 700 | 1 | |a Porcher, Raphaël |e verfasserin |4 aut | |
| 700 | 1 | |a Dunogué, Bertrand |e verfasserin |4 aut | |
| 700 | 1 | |a Berezné, Alice |e verfasserin |4 aut | |
| 700 | 1 | |a Guillevin, Loïc |e verfasserin |4 aut | |
| 700 | 1 | |a Le Guern, Véronique |e verfasserin |4 aut | |
| 700 | 1 | |a Cohen, Pascal |e verfasserin |4 aut | |
| 700 | 1 | |a Chaigne, Benjamin |e verfasserin |4 aut | |
| 700 | 1 | |a London, Jonathan |e verfasserin |4 aut | |
| 700 | 1 | |a Groh, Matthieu |e verfasserin |4 aut | |
| 700 | 1 | |a Paule, Romain |e verfasserin |4 aut | |
| 700 | 1 | |a Chassagnon, Guillaume |e verfasserin |4 aut | |
| 700 | 1 | |a Vakalopoulou, Maria |e verfasserin |4 aut | |
| 700 | 1 | |a Dinh-Xuan, Anh-Tuan |e verfasserin |4 aut | |
| 700 | 1 | |a Revel, Marie Pierre |e verfasserin |4 aut | |
| 700 | 1 | |a Mouthon, Luc |e verfasserin |4 aut | |
| 700 | 1 | |a Régent, Alexis |e verfasserin |4 aut | |
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