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\begin{document}

\title{Software-Based Quantitative Analysis of Lung Parenchyma in Patients with
Systemic Sclerosis May Provide New Generation Data for Pulmonary
Fibrosis}



\author[1]{Duygu Temiz Karadag}%
\author[2]{Ozgur Cakir}%
\author[2]{Andac Komac}%
\author[2]{Ayten Yazici}%
\author[2]{Ayse Cefle}%
\affil[1]{Canakkale Mehmet Akif Ersoy State Hospital}%
\affil[2]{Kocaeli University}%


\vspace{-1em}



  
  \date{June 14, 2024}


\begingroup
\let\center\flushleft
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\endgroup





\selectlanguage{english}
\begin{abstract}
Objectives: To investigate lung volume and density in patients with SSc
and changes in these parameters due to PF, using a software-aided image
quantification method, and compare this with a matched healthy control
group. Methods: Thoracic high-resolution computed tomography (HRCT)
images of patients and controls were analyzed using Myrian XP Lung 3D
software. Right, and left lung densities and volumes were calculated
separately by a blinded operator. Results were analyzed between
subgroups to investigate associations with the clinical features.
Results: A total of 135 patients with SSc and 38 healthy controls (HC)
were included. Characteristics of the SSc patients were 94 (69.6\%)
without PF, 85.4\% female, mean age 49.8 (15.4) years; 41 (30.4\%) with
PF, 88.3\% female, mean age 50.2 (11.5) years and HC group were 89.5\%
Female, mean age 52.2 (5.8) years. The right and left lung densities
were significantly higher, and right and left lung volumes were
significantly lower in the SSc patients with signs of fibrosis than
those without and HC (p\textless{}0.001 and p\textless{}0.001; p=0.006
and p=0.002, respectively). After excluding patients with PF, right and
left lung densities and volumes differed significantly between diffuse
cutaneous SSc, limited cutaneous SSc, and HC (p=0.002 and
p\textless{}0.001; p=0.045 and p=0.044, respectively). Patients who
developed PF during follow-up had significantly lower baseline right and
left lung densities than those who did not (p=0.018; p=0.014,
respectively). Forced vital capacity and carbon monoxide diffusing
capacity showed weak correlation with lung densities and volumes in
patients without PF and moderate to high correlation in PF patients.
Conclusion: Lung density and volume in SSc patients changed
significantly in those with PF and those without. Quantitative
information extracted by soft-ware aided methods may contribute more to
the detection, screening, and risk prediction in SSc related PF.%
\end{abstract}%



\sloppy


\textbf{Key-Points:}

Soft-ware aided quantification of the lung in SSc patients showed
decreased lung density and volume in patients with pulmonary fibrosis.

\textbf{INTRODUCTION}

Systemic sclerosis (SSc) is a life-threatening, immune-mediated
rheumatic disease characterized by fibrosis of the skin and internal
organs and vasculopathy. SSc may affect almost every organ, but usually
the skin, lung, heart, gastrointestinal tract, and peripheral
circulation.

Lung involvement is one of the leading causes of morbidity and mortality
{[}1{]}. Postmortem examination reveals signs of pulmonary fibrosis (PF)
in approximately 80\% of SSc patients {[}2{]}. Major risk factors for
the development of PF include diffuse cutaneous SSc (dcSSc), African
American race, older age at disease onset, shorter disease duration,
presence of anti-Scl-70 antibodies, and absence of anticentromere
antibodies {[}3{]}. It has been reported that there is a close
association between thoracic computed tomography (CT) patterns and
histopathological findings in surgical lung biopsy specimens which has
resulted in the widespread use of high-resolution computed tomography
(HRCT) to identify the lung pathology due to fibrosis {[}4-5{]}.
Thoracic HRCT scanning plays a central role in detecting lung fibrosis
in SSc. Compared to HRCT, pulmonary symptoms, chest radiography,
pulmonary function tests, and bronchoalveolar lavage (BAL) have limited
diagnostic accuracy and availability, especially when lung disease is
less advanced. HRCT may identify abnormalities in up to 44\% of the
patients with apparently normal chest radiographs {[}6{]}.

Despite major advances in diagnosis, SSc-related PF is challenging for
clinical management because treatment options are still limited.
However, it has been suggested that there is a ''window of opportunity''
when patients are identified at a very early phase of the disease,
enabling physicians to prevent or at least slow disease progression with
effective medications {[}7{]}. This view has highlighted the importance
of identification of organ involvement before irreversible fibrosis and
organ damage occur. Recent technical advances in radiology and
informatics have enabled the extraction of quantitative information,
such as volumetric data, morphometric data, or textural patterns,
concerning an organ of interest or a lesion within the organ {[}8{]}.
Many algorithms and software platforms provide image segmentation
routines for quantification of lung abnormalities. Lung segmentation is
the method used to identify the boundaries of the lung from the
surrounding tissue and provide quantitative data to assess the lung
parenchyma {[}9{]}.

The aim of this study was to investigate lung volume and density in
patients with SSc and changes in these parameters due to PF, using a
software-aided image quantification method, and compare this with a
matched healthy control group.

\textbf{METHODS}

\textbf{Patient Selection}

Medical records of the patients in the SSc cohort of the Rheumatology
Department of Kocaeli University School of Medicine were reviewed. Among
164 patients who fulfilled the 2013 American College of
Rheumatology/European League Against Rheumatism (ACR/ EULAR) SSc
classification criteria between 2007 and 2019 {[}10{]}, 135 patients
with a baseline HRCT evaluation were included in the study. Thirty-eight
gender- and age-matched healthy subjects, scanned for different
indications from various outpatient clinics were enrolled. Demographic,
clinical, and laboratory data were retrieved from medical records,
including organ involvements due to SSc, autoantibody status, forced
vital capacity (FVC), and carbon monoxide diffusing capacity (DLCO). The
patients were classified into limited cutaneous SSc (lcSSc, n=85) or
diffuse cutaneous SSc (dcSSc, n=50) according to LeRoy's criteria
{[}11{]}.

PF was defined based on a range of morphological HRCT findings,
including any one of the following: evident ground glass; honey combing;
interlobular septal thickening; or traction bronchiectasis. The
restrictive pulmonary disease pattern was characterized by a pulmonary
function test showing a normal FEV1/FVC value ([?]70\%) and at least one
of the following: FVC of \textless{}70\% of predicted normal or DLCO of
\textless{}70\% of predicted normal {[}12{]}. In case of doubt about the
findings (pneumonia, etc.), pulmonary involvement was confirmed with
supportive findings in follow-up HRCTs. Patients with a diagnosis of
other respiratory disorders such as asthma or chronic obstructive
pulmonary disease, malignancy and significant pulmonary hypertension
defined by previous clinical or echocardiographic evidence of
significant right heart failure, low cardiac index ([?]2 L/min/m\selectlanguage{ngerman}² with
right heart catheterization) or requiring parenteral therapy with
epoprostenol were excluded.

The study was approved by Kocaeli University School of Medicine Ethics
Committee, Kocaeli, Turkey, study number GOKAEK-2020/7.24.2020/118.

\textbf{Image Quantification}

Baseline HRCT scans of the patients and controls obtained between 2007
and 2019 were retrieved from the radiology archive. All CT scans were
performed non-contrast and volumetric thin-section using a 64-slice
multiple-detector CT scanner (Aquilion, Toshiba, Tokyo, Japan).
Participants were in the supine position and held a full inspiration
during acquisition. Images of 1 mm thickness were presented at window
settings designed for lung parenchyma evaluation (width 1,500 Hounsfield
units (HU); level -- 500 HU). Scans were analyzed by Myrian XP Lung 3D
software (Intrasense SA, Montpellier, France) for lung volume and lobar
density quantification. One operator (OC) blinded to the patients'
clinical features performed lung volume and density quantification using
the Myrian toolbox. Myrian is a medical imaging and computer-aided
diagnostic software program used to view, store, reproduce, and export
medical images. It is used for 3D imaging involving maximum intensity
projection and extremely accurate volume mapping, which utilizes
dedicated segmentation algorithms to separate entire vascular pulmonary
structures and normal parenchyma and quantify high precision lung
parenchyma volume. The software is compliant with DICOM images and shows
the 3D images in three planes (axial, coronal, and sagittal). All CT
scans were pre-processed using Gaussian smoothing for noise reduction
and histogram equalization for contrast enhancement. CT data of the lung
was saved as DICOM format, and subsequently imported into Myrian-Xp-Live
software. The system automatically identified and extracted image
information of the airways, vessels and lung parenchyma, and then
generated reconstructed 3D images of the lung. The right and left lungs,
as well as the trachea and adjacent vasculature could be clearly
observed in all directions. Segmentation of lung volume with an adaptive
area was used to divide the left and right lungs. The option of manual
correction was used to solve problems caused by individual anatomical
variability at each segmentation step. Based on their high density, the
pulmonary vessels were extracted from the segmented lung volumes. For
the right and left lungs, the densities were measured automatically,
with a choice of -- 700 or -- 900 HU as the characteristic density value
of healthy lungs (Figure 1).

\textbf{Statistical Analysis}

Descriptive statistics for clinical and demographic characteristics of
the patients are presented as frequency and percentage (\%) for
categorical variables and mean with standard deviation (mean ± SD) or
median with interquartile range (median {[}Q3--Q1{]}) according to the
distribution of the continuous variables. The normality was assessed
both visually and through the Shapiro--Wilk test. The independent
samples t-test was used to evaluate the intergroup differences for
normally distributed data; lung volumes and densities. For the
parameters which were not normally distributed, Mann--Whitney U test
(Wilcoxon rank-sum test) or Kruskal--Wallis test was used as
appropriate. Pearson Chi-square test was applied to analyze the
categorical variables. The Spearman's rank correlation coefficients were
used to examine the degree of associations between the right and left
lung densities, and right and left lung volumes and FVC and DLCO
{[}13{]}. The test-retest reliability was assessed by the intraclass
correlation coefficient (ICC). The strength of agreement between
repeated measures was determined by an ICC of 0.7 or greater,
representing a high agreement {[}14{]}. Statistical analyses were
performed using the SPSS, version 20.0, software (IBM Inc., Chicago, IL,
USA) and RStudio, Version 1.2.1335© 2009-2019 (PBC, Boston, MA).
Two-sided p values less than 0.05 were considered statistically
significant (\emph{p} \textless{}0.05).

\textbf{RESULTS}

Patients were divided into two groups; SSc patients with any typical
morphological findings of PF on HRCT (n=41) and patients without any
typical morphological findings of PF on HRCT (n=94). These were compared
with healthy controls (n=38). These three groups were comparable in age
and gender distribution (49.8±15.4, 50.2±11.5, 52.2±5.80,\emph{p}
=0.563; female 85.4\%, 88.3\%, 89.5\%, \emph{p} =0.840, respectively).
Baseline demographic and clinical characteristics and laboratory results
of the SSc patients with and without PF are shown in Table 1. The
patients with PF had more diffuse cutaneous involvement, lower FVC and
DLCO, and were more likely to be Scl-70 autoantibody positive. Pulmonary
artery diameter was higher in the patients with PF, and more patients
had pulmonary artery dilatation when a cut-off value of 30 mm was used
(\emph{p} \textless{}0.001 and \emph{p} =0.002, respectively). Synovitis
was more common in patients without PF.

The soft-ware analysis was performed on HRCT images of the 135 patients
with SSc and 38 controls (Figure 2). Analysis of the right and left lung
density, right and left lung volume derived from the baseline HRCT
scanning in SSc patients with PF, SSc patients without PF, and control
group was performed (Table 2, Figure 2). Comparison of the lung
measurements showed the right and left lung densities were significantly
increased, and the right and left lung volumes were significantly
decreased in SSc patients with PF than SSc without PF and controls. The
pairwise comparisons between the groups revealed there were no
differences between the SSc patients without PF and controls in terms of
right and left lung densities (\emph{p} =1.000 and \emph{p} =1.000,
respectively); there was a significant difference between SSc patients
with and without PF in terms of right and left lung volumes (\emph{p}
=0.005 and \emph{p} =0.003, respectively).

Patients without evidence of PF were sub-grouped into limited (n=71) and
diffuse (n=23) cutaneous involvement. This showed that the right and
left lung densities (\emph{p} =0.002 and \emph{p} \textless{}0.001,
respectively), and the right and left lung volumes differed
significantly between the groups (\emph{p} =0.045 and \emph{p} =0.044,
respectively) (Table 3).

Sixteen out of 56 (28.6\%) patients without any signs of PF at initial
evaluation and who had an annual follow-up CT scans developed abnormal
imaging after a mean duration of 10±5 years. Baseline lung densities and
volumes were compared between these sub-groups. This revealed that the
baseline right and left lung densities were significantly lower in the
patients who developed signs of PF during follow-up than those who did
not (\emph{p} =0.016 and \emph{p} =0.014, respectively) (Table-4).

The correlation between FVC and DLCO and lung densities and volumes in
patients with and without PF was analyzed separately. Spearman rank
correlation demonstrated negative correlation between the FVC and left
lung density (\emph{r} =-0.230, \emph{p} =0.043) and positive
correlation between FVC and right and left lung volumes (\emph{r}
=0.331,\emph{p} =0.003; \emph{r} =0.299, \emph{p} =0.008, respectively)
in the patients without PF signs while DLCO correlated only with left
lung volume in these patients (\emph{r} = 0.248, \emph{p} =0.032). In
the patients with PF, FVC correlated negatively with right and left lung
densities (\emph{r} = -0.601, \emph{p} =0.001; \emph{r} =
-0.507,\emph{p} =0.005, respectively) and positively with left lung
volume (\emph{r} = 0.500, \emph{p} =0.006). Similarly, DLCO correlated
negatively with right and left lung densities (\emph{r} =-0.487,
\emph{p} =0.012;\emph{r} = -0.393, \emph{p} =0.047, respectively) and
positively with left lung volume in these patients (\emph{r} = 0.402,
\emph{p} =0.042) (Table 5).

\textbf{Intra-observer variability}

All HRCT aided measurements were performed by an experienced radiologist
(O.C.) who was blinded to the patients' clinical features. In our study,
the intraobserver variability was as follows: ICC= 0.877 (95\% CI
0.741-0.941) for right lung density, ICC=0.887 (95\% CI 0.743-0.948) for
left lung density; ICC=0.940 (95\% CI 0.870-0.972) for right lung volume
and ICC=0.922 (95\% CI 0.838-0.963) for left lung volume. The
interobserver variability was calculated by making the measurements by a
scond operator (D.T.K.) and as follows: ICC= 0.857 (95\% CI 0.700-0.932)
for right lung density, ICC=0.872 (95\% CI 0.648-0.946) for left lung
density; ICC=0.940 (95\% CI 0.870-0.972) for right lung volume and
ICC=0.928 (95\% CI 0.828-0.967) for left lung volume.

\textbf{DISCUSSION}

In this study the change in lung density and volume due to pulmonary
fibrosis in patients with SSc evaluated by a soft-ware aided image
segmentation method was investigated. According to the study hypothesis,
fibrosis of lung tissue should lead to increased lung density and
reduced lung volume due to decreased ventilation {[}15{]}. Consistent
with this hypothesis, it was shown that right and left lung densities
were higher in SSc patients with signs of fibrosis than SSc patients
without pulmonary fibrosis and healthy controls.

Although parenchymal changes related to fibrosis were detected in
approximately 80-100\% of SSc patients in early autopsy studies
{[}2,16{]}, it is known that lung involvement in SSc varies between
40-60\% when assessed by HRCT studies {[}17-19{]}. In line with the
literature, it was expected that lung densities would be decreased in
the patients without fibrosis-related morphological changes on HRCT
compared to healthy subjects indicating sub-clinical and
sub-radiological lung involvement. However, lung densities showed no
difference between the patients without signs of fibrosis and controls.
It hs previously been shown that PF is closely related to
anti-topoisomerase antibody positivity and diffuse cutaneous
involvement, and thus patients without fibrosis-related morphological
changes on HRCT were analyzed after subgrouping them into limited and
diffuse cutaneous sub-types {[}15,18,20{]}. Our results showed that in
dcSSc patients, even if there were no signs of interstitial lung disease
(ILD) in HRCT, lung densities were higher than those of lcSSc and
control patients. This result suggested that early changes in lung
density may appear before the architectural distortion on HRCT in dcSSc
compared to lcSSc patients. Numerous studies have confirmed that
patients with lcSSc or dcSSc have distinct autoantibody profiles,
patterns of organ involvements, progression, and outcomes {[}21-22{]}.
There is still controversy whether the two subtypes of SSc represent
truly distinct diseases or merely different extremes of one disease
spectrum {[}23{]}. We suggest that the two subtypes differ in terms of
lung density in radiologically non-apparent disease, although the
clinical significance of this is unknown.

The annual follow-up HRCT scans of the 56 patients without any
morphological signs of fibrosis on HRCT were evaluated and it was found
that 16 (28.6\%) of the patients developed abnormal imaging after a mean
of ten years from baseline scans. It was shown that the baseline right
and left lung densities were significantly lower in the future PF
developers than the non-developers. However, there is little evidence to
support the predictive value of decreased lung density for PF
development in the future. Therefore its role as a parameter in
assessing and monitoring newly diagnosed patients should be investigated
in larger cohorts. Considering the heterogeneity of systemic
sclerosis-associated interstitial lung disease (SSc-ILD), the potential
absence of symptoms in early or mild disease, and the potential impact
of ILD on outcomes in patients with SSc, our results may contribute to
prediction models in SSc-ILD {[}24{]}.

There was a weak correlation between lung volumes and left lung density
and both FVC and DLCO in patients without PF. However, the association
became stronger in PF between the densities and left lung volume and
both FVC and DLCO. Based on our results, the gradual development of a
relationship with the effect of fibrosis-related changes in the lung
parenchyma was evident.

Interestingly, the expected decrease in lung volumes due to fibrosis
were inconsistent in some subgroup analyses. We believe that this
variability may be due to a change in compensatory capacity of the lungs
in response to any volume-decreasing pathology. Thus, due to this
possible confounder, the main focus of the study was lung density and we
believe that use of lung volume as a measurement parameter may be
misleading.

One of the most important limitations of our study was that follow-up
HRCT scans were not performed in all patients at regular intervals due
to the retrospective design. Thus, we refrain from making precise
comments about the time to the onset of PF, especially in patients with
dcSSc. The small sample size was another limitation, leading to
unreliable statistical analysis in some of the subgroup comparisons.

\textbf{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.

\textbf{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.

\textbf{Funding} No funding.

\textbf{Conflict of interest} None of the authors has financial or
non-financial conflicts of interest to disclose.

\textbf{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.

\textbf{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.

\textbf{References}

\begin{enumerate}
\tightlist
\item
  Elhai M, Meune C, Boubaya M, et al. Mapping and predicting mortality
  from systemic sclerosis. Ann Rheum Dis 2017; 76:1897--1905..
\item
  D'Angelo WA, Fries JF, Masi AT, Shulman LE. Pathologic observations in
  systemic sclerosis (scleroderma). A study of fifty-eight autopsy cases
  and fifty-eight matched controls. Am J Med. 1969;46(3):428-440.
  doi:10.1016/0002-9343(69)90044-8
\item
  Jaeger VK, Wirz EG, Allanore Y, et al. Incidences and risk factors of
  organ manifestations in the early course of systemic sclerosis: a
  longitudinal EUSTAR Study. Assassi S, editor. PLoS ONE 2016;
  11:e0163894
\item
  Wells AU, Hansel1 DM, Corrin B, et al. High resolution computed
  tomography as a predictor of lung histology in systemic sclerosis.
  Thorax 1992; 47:738-742
\item
  Wells AU. High-resolution Computed Tomography and Scleroderma Lung
  Disease. Rheumatology (Oxford). 2008 Oct;47 Suppl 5:v59-61. DOI:
  10.1093/rheumatology/ken271.
\item
  Harrison NK, Glanville AR, Strickland B, et al. Pulmonary involvement
  in systemic sclerosis: the detection of early changes by thin section
  CT scan, bronchoalveolar lavage and 99mTc-DTPA clearance. Respir Med.
  1989;83(5):403-414. doi:10.1016/s0954-6111(89)80072-1
\item
  Bellando-Randone S, Matucci-Cerinic M. Very early systemic sclerosis.
  Best Pract Res Clin Rheumatol. 2019;33(4):101428.
  doi:10.1016/j.berh.2019.101428
\item
  Yin, Nan et al. 'Computer-aided Identification of Interstitial Lung
  Disease Based on Computed Tomography'. 1 Jan. 2019: 591 -- 603.
\item
  Mansoor A, Bagci U, Foster B, et al. Segmentation and Image Analysis
  of Abnormal Lungs at CT: Current Approaches, Challenges, and Future
  Trends. Radiographics. 2015;35(4):1056-1076. doi:10.1148/rg.2015140232
\item
  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 72(11):1747--1755.
  https://doi.org/10.1136/annrheumdis-2013-204424
\item
  LeRoy EC, Black C, Fleischmajer R, Jablonska S, Krieg T, Medsger TA Jr
  et al (1988) Scleroderma (systemic sclerosis): classification, subsets
  and pathogenesis. J Rheumatol 15(2):202--205
\item
  Suliman YA, Dobrota R, Huscher D, et al. Brief Report: Pulmonary
  Function Tests: High Rate of False-Negative Results in the Early
  Detection and Screening of Scleroderma-Related Interstitial Lung
  Disease. Arthritis Rheumatol. 2015;67(12):3256-3261.
  doi:10.1002/art.39405
\item
  Cohen J (2003) Applied multiple regression/correlation analysis for
  the behavioral sciences, 3rd ed. Erlbaum L Associates, Mahwah, p 703
\item
  Fayers PM, Machin D (2007) Scores and measurements: validity,
  reliability, sensitivity. In: Quality of life: the assessment,
  analysis, and interpretation of patient-reported outcomes, 2nd edn.
  John Wiley \& Sons, Chichester, pp 77--107
\item
  Markstaller K, Kauczor HU, Weiler N, et al. Lung density distribution
  in dynamic CT correlates with oxygenation in ventilated pigs with
  lavage ARDS. Br J Anaesth. 2003;91(5):699--708. DOI:10.1093/bja/aeg246
\item
  Weaver AL, Divertie MB, Titus JL. Pul- monary scleroderma. Dis Chest
  1968; 54:490-498.
\item
  Solomon JJ, Olson a. L, Fischer a., Bull T, Brown KK, Raghu G.
  Scleroderma lung disease. Eur Respir Rev 2013;22:6--19.
  DOI:10.1183/09059180.00005512.
\item
  Jung E, Suh CH, Kim HA, Jung JY. Clinical characteristics of systemic
  sclerosis with interstitial lung disease. Arch Rheumatol 2018; 33:
  322--27.
\item
  Schurawitzki H, Stiglbauer R, Graninger W, et al. Interstitial lung
  disease in progressive systemic sclerosis: high-resolution CT versus
  radiography. Radiology 1990; 176: 755--759
\item
  Perelas A, Silver RM, Arrossi AV, Highland KB. Systemic
  sclerosis-associated interstitial lung disease. Lancet Respir Med.
  2020;8(3):304--320. DOI:10.1016/S2213-2600(19)30480-1
\item
  Gliddon AE, et al. Antinuclear antibodies and clinical associations in
  a British cohort with limited cutaneous systemic sclerosis. J
  Rheumatol. 2011; 38:702--705.
\item
  Meyer OC, Fertig N, Lucas M, Somogyi N, Medsger TA Jr. Disease
  subsets, antinuclear antibody profile, and clinical features in 127
  French and 247 US adult patients with systemic sclerosis. J Rheumatol.
  2007; 34:104--109.
\item
  Varga J, Hinchcliff M. Connective tissue diseases: systemic sclerosis:
  beyond limited and diffuse subsets?. Nat Rev Rheumatol.
  2014;10(4):200-202. doi:10.1038/nrrheum.2014.22
\item
  Wu W, Jordan S, Becker MO, Dobrota R, Maurer B, Fretheim H, Ye S,
  Siegert E, Allanore Y, Hoffmann-Vold AM, Distler O. Prediction of
  progression of interstitial lung disease in patients with systemic
  sclerosis: the SPAR model. Ann Rheum Dis. 2018 Sep; 77(9):1326-1332
\end{enumerate}

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