SARCOIDOSIS VASCULITIS AND DIFFUSE LUNG DISEASES 2017; 34; Mattioli 1885

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1 Original article: Clinical research SARCOIDOSIS VASCULITIS AND DIFFUSE LUNG DISEASES 2017; 34; Mattioli 1885 Internal and external responsiveness of computer-aided quantification of interstitial lung disease from high resolution computed tomography images in systemic sclerosis: a comparison with visual Reader-Based score Fausto Salaffi 1, Paolo Fraticelli 2, Marina Carotti 3, Colomba Fischetti 2, Marco Di Carlo 1, Gian Marco Giuseppetti 3, Armando Gabrielli 2 1 Rheumatology Departement, Ospedale Carlo Urbani ( Jesi), Università Politecnica delle Marche, Ancona, Italy; 2 Internal Medicine Department, Ospedali Riuniti, Università Politecnica delle Marche, Ancona, Italy; 3 Radiology Department, Ospedali Riuniti, Università Politecnica delle Marche, Ancona, Italy Abstract. Background: The aim of this study was to evaluate and compare the internal and external responsiveness of a computer-aided method (CaM) with a conventional visual reader-based score (CoVR) for measuring interstitial lung disease (ILD) in patients with systemic sclerosis (SSc) on high resolution computed tomography (HRCT). Methods: Thirty-one patients were included in this retrospective cohort. HRCTs were collected at baseline and after 1-year. The HRCT abnormalities were scored according to a CoVR (Warrick method) and a quantitative CaM. Internal responsiveness over 1-year was evaluated with standardized response mean (SRM). Receiver operating characteristic (ROC) curves analyses assessed the sensitivity and specificity of the two methods to discriminate between clinically relevant and no (relevant) progression, using judgement of the experts as gold standard (external responsiveness). Results: During 1-year, lung involvement was stable/improved in 17 of 31 patients (54.8%), and worsened in 14 patients (45.2%). The HRCT scores changed moderately over the follow-up period. Using SRM, the CaM was significantly more responsive to detect the changes due to treatment than the CoVR. Similarly, on ROC curve analysis, the CaM scores confirmed the highest performance (AUC ROC CaM vs. CoVR, vs ; p = 0.011). Conclusion: The quantitative CaM analysis was more responsive than the CoVR method for accurately assessing and monitoring the SSc-ILD progression or response to therapy. (Sarcoidosis Vasc Diffuse Lung Dis 2017; 34: 18-25) Key words: interstitial lung disease, systemic sclerosis, scoring methods Introduction Received: 30 August 2016 Accepted after revision: 6 October 2016 Correspondence: Marco Di Carlo, MD Rheumatology Departement Università Politecnica delle Marche, Ancona Italy Ospedale Carlo Urbani Via Aldo Moro, Jesi (Ancona) Tel 0731/ Fax 0731/ dica.marco@yahoo.it Systemic sclerosis (SSc) is a chronic, inflammatory, connective tissue disease characterized by extensive fibrosis, as well as by abnormalities in small vessels and in the musculature. Although cutaneous manifestations are the most pronounced feature, the disease can involve the internal organs, particularly the lungs, that have a severe impact on the prognosis (1). The European Scleroderma Trials and Research group (EUSTAR) analysis revealed that interstitial lung disease (ILD) is present in 53% of cases with diffuse cutaneous SSc and in 35% of cases with limited cutaneous SSc (2). High resolution computed tomography (HRCT) has now become an important tool for the detection and the evaluation of SSc-ILD

2 Comparison between two scoring methods of SSc-ILD 19 in daily clinical practice (3-8). It has been shown to be more accurate than chest radiography in detecting and characterizing diffuse lung diseases, and abnormalities on HRCT correlate more closely with pulmonary function test (PFT) (9, 10). Several HRCT semi-quantitative scoring methods have been described and used to characterize and quantify the disease (11). The implementation of these scoring systems is limited by intra- and inter-reader variations and requires an advanced knowledge of pulmonary anatomy and proficiency in identifying bronchopulmonary segments. This may impair the ability of these methods to assess disease progression over time. With respect to the traditional visual interpretation of HRCT findings, the automatic computerbased assessment may improve the objectivity, sensitivity, and repeatability of quantitative changes in the lung features (12, 13). Previously, we showed a high agreement concerning the semi-quantitative HRCT analysis performed by experienced radiologists, and a significant association between the descriptive parameters by both the quantitative assessment and the HRCT semi-quantitative analysis (14). The results indicate that a computer-aided method (CaM) provides a good concurrent validity, reliability and feasibility for the assessment of SSc-ILD (15, 16). Although HRCT scanning has been used to characterize the features and the extent of SSc-ILD, and even to predict results, there is limited experience with its use as an outcome measure in therapeutic trials (17-19). In the present study, we examined data from serial HRCTs performed in a cohort of SSc patients treated with different protocols. We focused the analyses on the comparison of the property of responsiveness of a CaM and a conventionale visual reader-based (CoVR) score for measuring SSc-ILD and assessing the score changes. We chose a 1-year period on the basis of SSc-ILD trials expert consensus (20). Patients and methods Study population Patients with SSc, defined by the 2013 American College of Rheumatology/European League against Rheumatism classification criteria (21), were included in this study. SSc patients were classified in limited and diffuse cutaneous involvement (lcssc and dcssc, respectively) (22). Thirty-one patients were treated according to different protocols. Fifteen patients were treated with rituximab (RTX) in two different regimens (1000 mg fortnightly x 2 or 375 mg/ m²/week for 4 consecutive weeks) at baseline and after 6 months, associated with mycophenylate mofetil (MMF) 2000 mg/day, 3 patients received a combination of oral imatinib (200 mg/day) and cyclophosphamide (CYC) (titrated as tolerated to 2 mg/kg one daily), 6 patients received MMF (2000 mg/day) monotheraphy and 7 patients received CYC (titrated as tolerated to 2 mg/kg one daily), monotherapy. All patients received additional oral corticosteroids ( 10 mg/day prednisone equivalent) and standard therapy with antiplatelet agents, calcium channel blockers, proton pump inhibitors. If needed, bisphosphonates and vitamin D supplementation were given. Protocol, patient information sheet and consent form were approved by the local Ethics Committee (Comitato Unico Regionale - ASUR Marche). The study was conducted in accordance with the Declaration of Helsinki in its fifth edition (2000). All patients provided written informed consent. HRCT assessment and conventional visual reader-based score (CoVR) quantification All patients underwent volumetric thin-section CT examinations using a CT 64 GE light Speed VCT power scanner. Scans were obtained at full inspiration from the apex to the lung base with the patients in the supine position. Scanning parameters were: 120 kv, and 300 mas, acquisition time 0.8 s, slice thickness 1 mm with 0.6 mm reconstructions and the smallest possible field of view (FOV) covering both lungs. The scans were viewed with a window level of -600 Hounsfield units (HU) and width of 1600 HU. HRCT assessment did not included the use of contrast media agents. One radiologist (MC consultant with 15 years of experience in the field of musculoskeletal radiology) and a rheumatologist (FS trained in CT interpretation), evaluated independently of each other the same HRCTs. The readers were first asked whether they observed any progression of SSc-ILD, and if they noted progression, they further had to state, during the same viewing

3 20 F. Salaffi, P. Fraticelli, M. Carotti, et al. session, whether they classified it as important progression (i.e., that they would record it as substantial progression in their report). The readers knew the chronological order of the HRCTs. No clinical information on disease duration and disease severity was provided. After the completion of the independent scoring phase, scans that has been reported in a discordant manner were jointly reviewed with a third reader (GMG experienced radiologist) to obtain a final consensus decision. The unanimous judgment of the three readers on the existence of substantial progression was used for the primary analysis. To estimate intraobserver reliability, each reader viewed all HRCTs twice, with an interval of at least four weeks. The pulmonary pathological findings on HRCT were coded and scored according to the Warrick et al. method (11). The mean values of the two independent readers were used as a final control group. The intraclass correlation coefficient (ICC) of the level of agreement between the radiologists on the total HRCT scores was 0.80 (15, 16). Computer-aided method (CaM) quantification process HRCT images were reconstructed and analysed by OsiriX MD 7, a DICOM software viewer (OsiriX MD version 7, 64-bit format), on a Mac Mini (2.8 GHz Intel Core 2 Duo Desktop Computer, 16 GB random-access memory; Apple Computer, Cupertino, CA, USA) running Mac Operating System OSX The program uses a semi-automated thresholding technique to close off lungs from other tissues. For each section, a semi-automatic lung parenchymal segmentation was performed in order to obtain analysis of all images. According to a previous proposal, the computed analysis of the HRCT lung mapping was done using a radiodensity between -200 and HU for the lung parenchyma (isolated from the mediastinum and the thoracic wall) and from -700 to -500 HU for ILD (23). A minimal user intervention was required to leave out blood vessels and large bronchi at the hilum. CT attenuation of normal lung parenchyma is reported to range from -800 to -900 HU, depending on ispiration or expiration, on the level of inspiration achieved for the scan and on anatomical location, that is ventral or dorsal portion. The area with attenuation between -500 and -700 was defined as the value of radiodensiy for ILD, including both ground-glass opacity and reticular opacity. The radiodensity of -500 HU was choosed as the threshold between consolidation and ground glass opacity. Hence, as suggested by Shin et al. (4), -700 HU was recognized as the predefined threshold value for portions of normal lung. For each HRCT were performed two CaM measurements with a total concordance between the first and second calculations of the scores (95% limits of agreement = 0 to 0, ICC = 1) (14). Statistical analysis The data were inserted into a Microsoft Excel database and analysed using the MedCalc version 16.0 (MedCalc Software, Ostend, Belgium). Values were expressed both as mean±standard deviation (SD) and as median (interquartile range [IQR]). The Student s paired-t-test evaluated whether the scores of the CoVR method and the CaM changed over 1-year. Responsiveness was assessed in the 1-year longitudinal HRCT follow-up. Based on the suggestion of Husted et al. (24), internal and external responsiveness were measured. To analyze internal responsiveness over 1-year, the standardized response mean (SRM) was used. The SRM is calculated as the mean change score between baseline and 1-year divided by the SD of this difference (25). Ninety-five percent confidence intervals (95% CIs) for the SRM were estimated through bootstrap resampling. Bootstrapping consists of resampling with replacement. We selected 1000 samples with replacement and calculated the SRM for each sample (26). Receiver operating characteristic (ROC) curve analyses assessed the sensitivity and specificity of the two scoring methods to discriminate between clinically relevant and no (relevant) progression, using judgment of the experts as gold standard. For the ROC curves analyses, the external criterion has to be dichotomized. Patients were classified as stable/ improved or worsened on the HRCT evaluation. The external criterion should be a well-accepted indication of change in the condition of the patient, clinicians should regard change in this standard as clinically meaningful (24). The areas under the ROC curves (AUC) were calculated to determine the accuracy to discriminate stable/improved from worsened patients. A non discriminating test has an AUC of 0.5 and a perfect discriminating test has

4 Comparison between two scoring methods of SSc-ILD 21 an AUC of 1.0. The threshold level with the most discriminative utility is the best combination of sensitivity and specificity. ROC curves allow to choose the threshold that is the best compromise between sensitivity and specificity for each CaM and CoVR criterion. The comparative accuracy of CoVR and CaM was determined by comparing the AUC using the Wilcoxon signed-rank test (27). Results Thirty-seven SSc patients were enrolled (male/ female: 8/10, median age 51 years, median disease 1 year, diffuse SSc 12, limited SSc 25). Six patients withdrew from the study: three were lost during follow-up, one had a minor infusion reaction which led to RTX discontinuation, and one died for different causes. Thus, the analysis included all 31 patients who completed 12 months of follow-up. After 1-year, lung involvement improved or remained stable in 17 of 31 patients (54.8% stable/improved ), and advanced in 14 patients (45.2% worsened ). Comparison of HRCT score changes by longitudinal analysis of the differences between baseline and after 1-year in the two groups of patients, using the CoVR method, showed no significant differences (paired samples t-test) (Table 1). On the other hand, the CaM allowed for both groups to detect a statistically significant difference (Table 2). To examine internal responsiveness over 1-year, the SRM has been used. The CaM was significantly more responsive to detect the changes due to treatment than the CoVR method (Table 3). ROC curve analyses assess the sensitivity and specificity of the two methods to discriminate between clinically relevant and no (relevant) progression using the judgment of the experts as standard (external responsiveness) (Fig. 1). The AUC ROC can be interpreted as the probability of correctly identifying patients stable/improved form those worsened. As shown in Fig. 1, the CaM has higher accuracy than the CoVR to distinguish between these two categories. The AUC of CaM was (standard error = ), with 95% C.I. from to 0.991) whereas of the CoVR was (standard error = ) with 95% C.I. from to The difference between the changes in the scores Table 1. Statistics for the conventional visual reader-based score (CoVR) in the sample of stable/improved and worsened patients of the two methods was significant (differences between areas = 0.130, 95% CI , p <0.05). Discussion Baseline 1-year changes Stable/improved patients Sample size Arithmetic mean % CI for the mean to to Variance Standard deviation Standard error of the mean Paired samples t-test Mean difference Standard deviation 0.56 Pooled Standard Deviation % CI to Test statistic t Two-tailed probability P = Worsened patients Sample size Arithmetic mean % CI for the mean to to Variance Standard deviation Standard error of the mean Paired samples t-test Mean difference 0.35 Standard deviation 0.74 Pooled Standard Deviation % CI to 0.78 Test statistic t 1.74 Two-tailed probability P = Although HRCT scanning is routinely used to diagnose, to characterize the hallmarks and the extent of SSc-ILD (4, 5, 7-9), and even to predict outcomes (28, 29), rarely it has been employed in a prospective and systematic manner as a parameter to assess therapeutic responses in patients with SSc- ILD (17, 30-33). Several clinical trials provided evidences to support the use of immunosuppressive agents in the management of SSc-ILD (17, 18, 34, 35). Other therapies currently under evaluation include MMF, imatinib and RTX (36-39). SSc-ILD is a hard to treat feature of the disease. The overall magnitude of

5 22 F. Salaffi, P. Fraticelli, M. Carotti, et al. Table 2. Statistics for the computer-aided method (CaM) in the sample of stable-improved and worsened patients Baseline 1-year changes Stable/improved patients Sample size Arithmetic mean % CI for the mean to to Variance Standard deviation Standard error of the mean Paired samples t-test Mean difference Standard deviation 3.02 Pooled Standard Deviation % CI to Test statistic t Two-tailed probability P = Worsened patients Sample size Arithmetic mean % CI for the mean to to Variance Standard deviation Standard error of the mean Paired samples t-test Mean difference 2.34 Standard deviation 2.71 Pooled Standard Deviation 8,42 95% CI 0.77 to 3.91 Test statistic t 3.23 Two-tailed probability P = Table 3. Standardized response mean (SRM) (with 95% CI) for the computer-aided method (CaM) and conventional visual reader-based score (CoVR) in both groups Value 95% CI a CaM stable/improved to CaM worsened to CoVR stable/improved to CoVR worsened to a BC a bootstrap confidence interval (1000 iterations; random number seed: 978) Fig. 1. ROC curves illustrating the relationship between sensi- ROC curves illustrating the relationship between sensitivity and complement of specificity (100 - specificity) for differences in computer-aided method (CaM) and conventional visual reader-based score (CoVR) between baseline and 1-year. The line that runs diagonally across the Figure from lower left upper right will defines an area of 0.5 and represents an instrument not able to discriminate different status of disease activity. The CaM shows a higher accuracy than the CoVR method to distinguish stable/ improved from worsened (difference in AUC = 0.130, 95% CI , p <0.05). The experts opinion is used as external criterion the therapeutic response in the studies was modest and the beneficial effects appeared to fade within a year after stopping treatment (35). In order to evaluate the benefits of treatment and to reproduce the findings in the clinical practice, different systems for evaluating SSc-ILD have been developed over the past 20 years (6, 11, 29, 40, 41). Among the different visual scoring systems there is a substantial variability. Moreover, sometimes could appear difficult to ascertain whether minor CT changes should be considered a disease evolution or if they are due to reader variation inherent in visual scoring systems. For the visual evaluation of SSc-ILD, the inter and intra-reader agreement (k statistic) ranges between 0.60 and 0.80, respectively (20). Goldin et al. (33) reported influential interreader differences in the side-by-side interpretations of the baseline and 1-year HRCTs visually scored. This inter-reader variability for changes over time in pulmonary fibrosis was reflected by a κ statistic of 0.51 for agreement among trained readers. This moderate degree of concordance could compromise the clinical utility of HRCT scans as an objective marker of treatment related changes in fibrosis. The implications of these findings are that scoring requires a good expertise and that using HRCT scores for longitudinal multi-center trials may be limited by considerable error. Improvements in imaging technique and technological advances in the development of software

6 Comparison between two scoring methods of SSc-ILD 23 for computer-assisted quantitative assessments of the distribution and extent of pulmonary fibrosis should help to solve some of these difficulties (19, 43). Recently, we showed a high agreement concerning the semi-quantitative HRCT analysis performed by experienced radiologists, and a significant association between the descriptive parameters by both the quantitative CaM assessment and the HRCT semi-quantitative analysis (14). Further, we investigated the performance of a CaM for the quantification of SSc-ILD (in terms of correlation both to CoVR method and the pulmonary function tests findings), its feasibility and its inter-reader reliability (15, 16). The results indicate that the CaM analysis is potentially useful for reproducible objective measurements of pulmonary fibrosis, since it uses a continuous percentage scale, rather than a categorical Likert scale. In addition, the CaM system can provide higher statistical power for detecting the extent of pulmonary fibrosis on the HRCT. To date, several computer-aided tools to segment automatically the lung, using HRCT images, have been developed. They include image display (e.g., multiplanar reformations and surface shading for three-dimensional and volume rendering), anatomic image quantification (e.g., area and volume of airways and lungs) and regional characterization of lung tissue (analysing attenuation, changes in attenuation, and texture patterns in the imaged lung) (12, 13, 19). With respect to the traditional visual interpretation of HRCT lung findings, the automatic computer-based assessment may improve the objectivity, sensitivity, and repeatability of quantitative changes in the lung features (12, 23). The aims of our study were to evaluate and to compare the internal and external responsiveness of a CaM with a CoVR method for measuring SSc-ILD on HRCT and to assess the change-score with the highest accuracy. Our data shown that, after 1-year, lung involvement improved or remained stable in 54.8% of the patients and worsened in 45.2%. The scores of the HRCT changed moderately over 12 months. The CaM demonstrates significantly better internal responsiveness than the CoVR (SRMs to and to , respectively). On ROC curve analysis the CaM scores showed the highest performance (AUC ROC CaM vs. CoVR, vs ; p = 0.011). Some potential limitations of this study have to be mentioned. First, a larger sample of patients is necessary to verify whether the CaM and the CoVR method differ in accordance with the changes of impairment. Second, the ROC approach requires an anchor that serves as a gold standard for the construct to be measured, that is, change in the HRCT severity. Unfortunately, the anchors used in literature, such as the expert panels methods to estimate HRCT changes have often not been extensively validated, and considerable criticism about their reliability and validity has been raised (44). Conclusions Our study confirms greater sensitivity in detecting HRCT differences by a CaM than a CoVR. These differences were defined as clinically relevant by an expert panel. Further studies are required to evaluate how sensitive it is to detect change over time, and therefore how it can be used in assessing response to therapy. Authors contributions: FS, PF, AG, GMG drafted the study design and the protocol, data interpretation and analysis, and in writing the final version of the manuscript. FS, MC, GMG were involved in the HRCTs scoring and in data collection. CF and PF contributed to data collection and provided clinical support. MDC contributed to data interpretation, in writing the manuscript and in its revision. All authors approved the final version. References 1. Ferri C, Sebastiani M, Lo Monaco A, Iudici M, Giuggioli D, Furini F, et al. Systemic sclerosis evolution of disease pathomorphosis and survival. Our experience on Italian patients population and review of the literature. Autoimmun Rev 2014; 13: Walker UA, Tyndall A, Czirják L, Denton C, Farge-Bancel D, Kowal- Bielecka O, et al. Clinical risk assessment of organ manifestations in systemic sclerosis: a report from the EULAR Scleroderma Trials And Research group database. Ann Rheum Dis 2007; 66: Cappelli S, Bellando Randone S, Camiciottoli G, De Paulis A, Guiducci S, Matucci-Cerinic M. Interstitial lung disease in systemic sclerosis: where do we stand? Eur Respir Rev 2015; 24: Launay D, Remy-Jardin M, Michon-Pasturel U, Mastora I, Hachulla E, Lambert M, et al. High resolution computed tomography in fibrosing alveolitis associated with systemic sclerosis. J Rheumatol 2006; 33: Patiwetwitoon S, Wangkaew S, Euathrongchit J, Kasitanon N, Louthrenoo W. High-resolution computed tomographic findings in systemic sclerosis-associated interstitial lung disease: comparison between diffuse and limited systemic sclerosis. J Clin Rheumatol 2012; 18:

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