Conclusion
To the best of our knowledge this was the first study to use software aided methods for a deeper analysis of HRCT data which is often routinely gathered in SSc patients. It was demonstrated that lung density decreased in patients with any signs of PF compared to those without PF and healthy controls. It was also shown that, even in the absence of signs of pulmonary fibrosis in HRCT, lung density was decreased in patients with dcSSc compared to both lcSSc patients and healthy controls. Decreased baseline lung density may be a predictive parameter for future PF in SSc patients although this proposal requires validation in larger patient groups. The methods described herein may contribute to the use of software-assisted machine learning techniques in assessing lung parenchyma in SSc.
Acknowledgments The authors thank all investigators for their contribution to the study. The corresponding author certifies that all authors approved the entirety of the submitted material and contributed actively to the study. We would like to thank Mr. Jeremy Jones, of the Kocaeli University Academic Writing Department, for the revision of the English in this paper.
Funding No funding.
Conflict of interest None of the authors has financial or non-financial conflicts of interest to disclose.
Ethical approval This article does not contain any studies with animals performed by any of the authors. Informed consent was obtained from all individual participants included in the study.
Author Contributions : DTK acquired the clinical data, contributed to experimental plan design, performed all the statistical analysis and drafted the manuscript. OC contributed to the acquisition of HRCT data. AK contributed to the acquisition of clinical data. AY and AC contributed to critical revision of the manuscript.