Cottin V, Brown KK. Interstitial lung disease associated with systemic sclerosis (SSc-ILD). Respir Res Engl. 2019;20:13.

Article  Google Scholar 

Vonk MC, Smith V, Sfikakis PP, Cutolo M, Del Galdo F, Seibold JR. Pharmacological treatments for SSc-ILD: systematic review and critical appraisal of the evidence. Autoimmun Rev Neth. 2021;20:102978.

Article  CAS  Google Scholar 

Levin D, Osman MS, Durand C, Kim H, Hemmati I, Jamani K, et al. Hematopoietic cell transplantation for systemic Sclerosis-A review. Switzerland: Cells; 2022. p. 11.

Google Scholar 

Fischer A, du Bois R. Interstitial lung disease in connective tissue disorders. Lancet (London, England). Engl; 2012;380:689–98.

O’Reilly S. Metabolic perturbations in systemic sclerosis. Curr Opin Rheumatol United States. 2022;34:91–4.

Article  Google Scholar 

van den Hoogen F, Khanna D, Fransen J, Johnson SR, Baron M, Tyndall A, et al. 2013 classification criteria for systemic sclerosis: an American college of rheumatology/European league against rheumatism collaborative initiative. Ann Rheum Dis Engl. 2013;72:1747–55.

Article  Google Scholar 

LeRoy EC, Black C, Fleischmajer R, Jablonska S, Krieg T, Medsger TAJ, et al. Scleroderma (systemic sclerosis): classification, subsets and pathogenesis. J Rheumatol Can. 1988;15:202–5.

CAS  Google Scholar 

Khanna D, Tashkin DP, Denton CP, Renzoni EA, Desai SR, Varga J, Etiology. Risk factors, and biomarkers in systemic sclerosis with interstitial lung disease. Am J Respir Crit Care Med United States. 2020;201:650–60.

Article  CAS  Google Scholar 

Moinzadeh P, Bonella F, Oberste M, Weliwitage J, Blank N, Riemekasten G, et al. Impact of systemic sclerosis-Associated interstitial lung Disease With and without Pulmonary Hypertension on Survival: a large cohort study of the German Network for systemic sclerosis. Chest. 2024;165(1):132–45.

Article  PubMed  Google Scholar 

Distler O, Assassi S, Cottin V, Cutolo M, Danoff SK, Denton CP et al. Predictors of progression in systemic sclerosis patients with interstitial lung disease. Eur Respir J Engl; 2020;55.

Dack E, Christe A, Fontanellaz M, Brigato L, Heverhagen JT, Peters AA, et al. Artificial Intelligence and interstitial lung disease: diagnosis and prognosis. Invest Radiol United States. 2023;58:602–9.

Google Scholar 

Soffer S, Morgenthau AS, Shimon O, Barash Y, Konen E, Glicksberg BS, et al. Artificial Intelligence for Interstitial Lung Disease Analysis on chest computed tomography: a systematic review. Acad Radiol Elsevier. 2022;29:S226–35.

Article  Google Scholar 

Handa T, Tanizawa K, Oguma T, Uozumi R, Watanabe K, Tanabe N, et al. Novel Artificial Intelligence-based technology for chest computed Tomography Analysis of Idiopathic Pulmonary Fibrosis. Ann Am Thorac Soc United States. 2022;19:399–406.

Article  Google Scholar 

Frix A-N, Cousin F, Refaee T, Bottari F, Vaidyanathan A, Desir C et al. Radiomics in Lung diseases Imaging: state-of-the-art for clinicians. J Pers Med. 2021.

Schniering J, Maciukiewicz M, Gabrys HS, Brunner M, Blüthgen C, Meier C et al. Computed tomography-based radiomics decodes prognostic and molecular differences in interstitial lung disease related to systemic sclerosis. Eur Respir J Engl; 2022;59.

Martini K, Baessler B, Bogowicz M, Blüthgen C, Mannil M, Tanadini-Lang S, et al. Applicability of radiomics in interstitial lung disease associated with systemic sclerosis: proof of concept. Eur Radiol Ger. 2021;31:1987–98.

Article  CAS  Google Scholar 

Walsh SLF, Calandriello L, Silva M, Sverzellati N. Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study. Lancet Respir Med. Else; 2018;6:837–45.

Bonhomme O, André B, Gester F, de Seny D, Moermans C, Struman I, et al. Biomarkers in systemic sclerosis-associated interstitial lung disease: review of the literature. Rheumatol (Oxford) Engl. 2019;58:1534–46.

Article  CAS  Google Scholar 

Makol A, Nagaraja V, Amadi C, Pugashetti JV, Caoili E, Khanna D. Recent innovations in the screening and diagnosis of systemic sclerosis-associated interstitial lung disease. Expert Rev Clin Immunol Engl. 2023;19:613–26.

Article  CAS  Google Scholar 

Hoffmann-Vold A-M, Maher TM, Philpot EE, Ashrafzadeh A, Distler O. Assessment of recent evidence for the management of patients with systemic sclerosis-associated interstitial lung disease: a systematic review. ERJ open Res Engl. 2021;7.

Flaherty KR, Wells AU, Cottin V, Devaraj A, Walsh SLF, Inoue Y, et al. Nintedanib in progressive fibrosing interstitial lung diseases. N Engl J Med. 2019;381:1718–27.

Article  CAS  PubMed  Google Scholar 

Stanojevic S, Kaminsky DA, Miller M, Thompson B, Aliverti A, Barjaktarevic I et al. ERS/ATS technical standard on interpretive strategies for routine lung function tests. Eur Respir J. 2021;2101499.

Schober P, Boer C, Schwarte LA. Correlation coefficients: appropriate use and interpretation. Anesth Analg. 2018;126.

MacFarland TW, Yates JM. In: MacFarland TW, Yates JM, editors. Wilcoxon matched-pairs signed-ranks test BT - introduction to nonparametric statistics for the Biological sciences using R. Cham: Springer International Publishing; 2016. pp. 133–75.

Google Scholar 

Fay MP, Proschan MA. Wilcoxon-Mann-Whitney or t-test? On assumptions for hypothesis tests and multiple interpretations of decision rules. Stat Surv. 2010;4:1–39.

Article  PubMed  PubMed Central  Google Scholar 

Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-net: learning dense volumetric segmentation from sparse annotation. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics). 2016.

Ronneberger O, Fischer P, Brox T. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. U-Net: Convolutional Networks for Biomedical Image Segmentation BT - Medical Image Computing and Computer-assisted intervention – MICCAI 2015. Cham: Springer International Publishing; 2015. pp. 234–41.

Google Scholar 

Widell J, Lidén M. Interobserver variability in high-resolution CT of the lungs. Eur J Radiol Open. 2020;7:100228.

Article  PubMed  PubMed Central  Google Scholar 

Watadani T, Sakai F, Johkoh T, Noma S, Akira M, Fujimoto K, et al. Interobserver Variability in the CT Assessment of Honeycombing in the lungs. Radiol Radiological Soc North Am. 2013;266:936–44.

Google Scholar 

Aziz ZA, Wells AU, Hansell DM, Bain GA, Copley SJ, Desai SR, et al. HRCT diagnosis of diffuse parenchymal lung disease: inter-observer variation. Thorax. 2004;59:506–11.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Occhipinti M, Bosello S, Sisti LG, Cicchetti G, de Waure C, Pirronti T, et al. Quantitative and semi-quantitative computed tomography analysis of interstitial lung disease associated with systemic sclerosis: a longitudinal evaluation of pulmonary parenchyma and vessels. PLoS ONE. 2019;14:1–18.

Article  Google Scholar 

Axelsson GT, Gudmundsson G. Interstitial lung abnormalities - current knowledge and future directions. Eur Clin Respir J. 2021;8:1994178.

Article  PubMed  PubMed Central  Google Scholar 

Agarwala S, Kale M, Kumar D, Swaroop R, Kumar A, Kumar Dhara A et al. Deep learning for screening of interstitial lung disease patterns in high-resolution CT images. Clin Radiol. The Royal College of Radiol; 2020;75:481.e1-481.e8.

Mergen V, Kobe A, Blüthgen C, Euler A, Flohr T, Frauenfelder T et al. Deep learning for automatic quantification of lung abnormalities in COVID-19 patients: first experience and correlation with clinical parameters. Eur J Radiol Open. 2020;7.

Walsh SLF, Calandriello L, Silva M, Sverzellati N. Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study. Lancet Respir Med. Else; 2018;6:837–45.

Ho TT, Kim T, Kim WJ, Lee CH, Chae KJ, Bak SH, et al. A 3D-CNN model with CT-based Parametric response mapping for classifying COPD subjects. Sci Rep Nat Publishing Group UK. 2021;11:1–12.

Google Scholar 

Si-Mohamed SA, Nasser M, Colevray M, Nempont O, Lartaud P-J, Vlachomitrou A, et al. Automatic quantitative computed tomography measurement of longitudinal lung volume loss in interstitial lung diseases. Eur Radiol. 2022;32:4292–303.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sun H, Liu M, Kang H, Yang X, Zhang P, Zhang R, et al. Quantitative analysis of high-resolution computed tomography features of idiopathic pulmonary fibrosis: a structure-function correlation study. Quant Imaging Med Surg China. 2022;12:3655–65.

Article  Google Scholar 

Temiz Karadag D, Cakir O, San S, Yazici A, Ciftci E, Cefle A. Association of quantitative computed tomography ındices with lung function and extent of pulmonary fibrosis in patients with systemic sclerosis. Clin Rheumatol. 2022;41:513–21.

Article  PubMed  Google Scholar 

Barnes H, Humphries SM, George PM, Assayag D, Glaspole I, Mackintosh JA, et al. Machine learning in radiology: the new frontier in interstitial lung diseases. Lancet Digit Heal Engl. 2023;5:e41–50.

Article  CAS  Google Scholar 

Nathan SD, Meyer KC. IPF clinical trial design and endpoints. Curr Opin Pulm Med United States. 2014;20:463–71.

Article  Google Scholar 

Cottin V. Interstitial lung disease: new challenges and evolving phenotypes. Eur Respir Rev. 2010;19:91 LP – 93.

Goh NSL, Desai SR, Veeraraghavan S, Hansell DM, Copley SJ, Maher TM, et al. Interstitial lung disease in systemic sclerosis: a simple staging system. Am J Respir Crit Care Med United States. 2008;177:1248–54.

Article  Google Scholar 

Jeny F, Brillet P-Y, Kim Y-W, Freynet O, Nunes H, Valeyre D. The place of high-resolution computed tomography imaging in the investigation of interstitial lung disease. Expert Rev Respir Med. Taylor & Francis; 2019;13:79–94.

Nagy T, Toth NM, Palmer E, Polivka L, Csoma B, Nagy A, et al. Clinical predictors of lung-function decline in systemic-sclerosis-Associated interstitial lung disease patients with normal spirometry. Biomedicines. Switzerland; 2022. p. 10.

Li L, Gao S, Fu Q, Liu R, Zhang Y, Dong X, et al. A preliminary study of lung abnormalities on HRCT in patients of rheumatoid arthritis–associated interstitial lung disease with progressive fibrosis. Clin Rheumatol. 2019;38:3169–78.

Article  PubMed  Google Scholar 

Kazantzi A, Costaridou L, Skiadopoulos S, Korfiatis P, Karahaliou A, Daoussis D, et al. Automated 3D Ιnterstitial Lung Disease Εxtent quantification: performance evaluation and correlation to PFTs. J Digit Imaging. 2014;27:380–91.

Article  PubMed  PubMed Central  Google Scholar 

Occhipinti M, Bruni C, Camiciottoli G, Bartolucci M, Bellando-Randone S, Bassetto A et al. Quantitative analysis of pulmonary vasculature in systemic sclerosis at spirometry-gated chest CT. Ann Rheum Dis. 2020;79:1210–17.