Quantitative Evaluation of Thoracic Skeletal Muscle Mass and Normal/Fibrotic Lung Volumes on CT in Idiopathic Pulmonary Fibrosis Patients: Prognostic Significance

Quantitative Evaluation of Thoracic Skeletal Muscle Mass and Normal/Fibrotic Lung Volumes on CT in Idiopathic Pulmonary Fibrosis Patients: Prognostic Significance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Quantitative Evaluation of Thoracic Skeletal Muscle Mass and Normal/Fibrotic Lung Volumes on CT in Idiopathic Pulmonary Fibrosis Patients: Prognostic Significance Mustafa Abdulgani Kurt, Murathan Köksal, Ebru Şengül, Şeref Parlak This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7436178/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective: The primary aim of this study is to evaluate the prognostic significance and impact on mortality of sarcopenia—assessed through thoracic skeletal muscles—and threshold-based quantitative lung volumetric analysis in patients with idiopathic pulmonary fibrosis (IPF). Materials and Methods: Patients who underwent non-contrast thoracic CT between January 2019 and August 2023 at Ankara Bilkent City Hospital and exhibited a usual interstitial pneumonia (UIP) pattern in the lung parenchyma were retrospectively reviewed. A total of 106 IPF patients meeting the inclusion criteria and 106 age- and sex-matched control subjects without any chronic lung disease were included in the study. To assess sarcopenia, the cross-sectional area (PMA) and density (PMD) of the pectoralis muscles were measured from the first axial slice above the aortic arch, while the cross-sectional area (ESA) and density (ESD) of the erector spinae muscles were measured from a single axial slice at the lower margin of the 12th thoracic vertebra. Using an artificial intelligence-based analysis software package (Thoracic VCAR, GE Healthcare), normal and fibrotic lung volumes were quantitatively measured. All CT measurements were compared between the patient and control groups. Results: The mean body mass index (BMI) of the patient group was significantly lower than that of the control group (p < 0.05). Additionally, quantitative lung volume measurements (Normal Attenuation Lung Volume - NALV [L and %], Low Attenuation Lung Volume - LALV [%], High Attenuation Lung Volume - HALV [L and %], total lung volume) and thoracic skeletal muscle measurements (ESA, ESI, PMA, PMI) were significantly lower in the patient group (p < 0.05). Patients were grouped as sarcopenic or non-sarcopenic based on the distribution of their Erector Spinae Index (ESI) and Pectoralis Muscle Index (PMI). According to ESI, sarcopenic patients had significantly lower two-year survival rates, follow-up durations, normal attenuation lung volume (NALV in L and %), total lung volume, FVC (L), and FEV1 (L) compared to non-sarcopenic patients (p < 0.05). However, when classified by PMI, no statistically significant differences were observed between sarcopenic and non-sarcopenic groups in terms of two-year survival rates or quantitative lung volumes (p > 0.05). Conclusion: ESI and NAAV (%) obtained through quantitative CT analysis are significant prognostic indicators for predicting two-year mortality in IPF patients. Idiopathic pulmonary fibrosis Sarcopenia Lung volume Quantitative analysis Computed tomography Mortality Figures Figure 1 Figure 2 Figure 3 Figure 20 Figure 21 Figure 22 1. Introduction Idiopathic Pulmonary Fibrosis (IPF) is the most common chronic and progressive fibrotic lung disease of unknown etiology [ 1 ]. IPF is characterized histologically and radiologically by the usual interstitial pneumonia (UIP) pattern [ 2 ]. On thoracic computed tomography (CT), irregular reticulations predominantly in the subpleural and lower lobe zones, traction bronchiectasis, volume loss, and honeycombing are typically observed [ 3 ]. Sarcopenia is a syndrome characterized by progressive loss of muscle mass and decreased muscle strength [ 4 ]. It has been associated with poor prognosis in many medical conditions. Although several diagnostic methods are used to assess sarcopenia, the measurement of cross-sectional muscle area (SMA) at the level of the third lumbar vertebra (L3) by CT and the derived index obtained by dividing this value by the square of the patient’s height (SMI) are widely accepted in sarcopenia assessment [ 5 ]. In addition, previous studies have employed measurements of the pectoral muscles and the erector spinae muscles at the T12 vertebral level for sarcopenia evaluation across different patient populations [ 6 ]. The main objective of this study is to investigate the relationship between IPF and sarcopenia, and to determine the potential prognostic significance and impact on mortality. Furthermore, we aim to identify a linear relationship between muscle loss, lung volumes, and IPF severity through quantitative area-based and volumetric measurements. 2. Materials and Methods 2.1. Ethics Statement This retrospective study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Clinical Research Ethics Committee of Ankara Bilkent City Hospital (Date: 11.10.2023, Decision No: E2-23-5201). 2.2. Study Design Patients who underwent non-contrast thoracic computed tomography (CT) between January 2019 and August 2023 and demonstrated a usual interstitial pneumonia (UIP) pattern in the lung parenchyma were retrospectively reviewed (n = 197). Among these patients, those with a known connective tissue disease (n = 47), a history of occupational or environmental exposure (n = 9), malignancy (n = 12), coexisting chronic obstructive pulmonary disease (COPD) (n = 11), or significant CT artifacts (n = 12) were excluded. A total of 106 IPF patients who met the inclusion criteria were included in the study. In addition, a control group consisting of 106 age- and sex-matched individuals without any chronic lung disease or malignancy, who had undergone non-contrast thoracic CT for other reasons, was also included. The following data were recorded for all participants: age, sex, height, weight, body mass index (BMI), pulmonary function test (PFT) results, GAP index score and stage, survival status, and follow-up duration (in months). All CT scans were performed with the patient in the supine position following full inspiration and without intravenous contrast administration. Thoracic CT was performed using a 128-slice multidetector CT scanner (Revolution EVO, General Electric Medical Systems, Milwaukee, Wisconsin, USA). CT images were analyzed using the Thoracic VCAR software package on the Advantage Workstation 4.7 (GE Healthcare, USA), which enables quantitative analysis in the mediastinal window. The artificial intelligence–based Thoracic VCAR software performed automatic segmentation of the lungs and calculated the volumes and percentage distributions of hyperinflated, non-aerated, and poorly aerated parenchymal areas based on attenuation values. While performing these measurements, the software included segmental bronchi, vessels, and interstitial structures of both lungs but excluded the main pulmonary arteries, bronchi, mediastinal structures, and pleural effusions. According to previously published threshold-based studies, lung attenuation ranges were categorized as (a.) areas with attenuation values below − 950 Hounsfield units (HU) were classified as low-attenuation areas (e.g., emphysema), (b.) areas between − 950 and − 705 HU were considered normally aerated lung parenchyma, (c.) areas above − 705 HU were defined as high-attenuation areas, representing ground-glass opacities, fibrotic changes, or consolidations [ 7 ]. Cross-sectional area (CSA) and mean attenuation values of the pectoral muscles (pectoralis major and minor) were measured on the first axial CT slice immediately above the aortic arch. CSA and mean attenuation of the erector spinae muscles were measured on a single axial CT slice taken at the lower margin of the 12th thoracic vertebra. Muscle boundaries were manually delineated using an attenuation range of − 29 to + 150 HU. Cross-sectional areas were measured in square centimeters (cm²). All measurements were manually performed by two radiologists with at least four years of experience. For normalization and sarcopenia evaluation, pectoral muscle area (PMA) and erector spinae muscle area (ESA) were divided by the square of the patient’s height (m²) to obtain the Pectoralis Muscle Index (PMI) and Erector Spinae Index (ESI), respectively, expressed in cm²/m². 2.5. Statistical Analysis All statistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA). To examine the association between sarcopenia status (defined based on erector spinae and pectoralis muscle indices) and patient survival or two-year mortality, chi-square tests were used. For significant chi-square results, the Bonferroni correction was applied to determine between which groups the differences existed. Due to insufficient sample sizes in subgroups, the GAP stage variable (with three categories) was analyzed using the Kruskal–Wallis test. In cases where the Kruskal–Wallis test showed statistical significance, pairwise comparisons were conducted using the Mann–Whitney U test. The Pearson correlation coefficient was used to evaluate the relationship between continuous variables within the patient group. Survival analyses were performed using the Kaplan–Meier method, and differences in survival curves were assessed using the log-rank test. Univariate and multivariate analyses for two-year survival outcomes were conducted using the Cox proportional hazards regression model. Interobserver agreement for continuous variables such as pectoralis muscle area (PMA), erector spinae area (ESA), and their mean attenuations (PM-density and ESM-density) was assessed using the intraclass correlation coefficient (ICC) with corresponding 95% confidence intervals. A p-value of less than 0.05 was considered statistically significant. 3. Results A subset of 25 patients from the IPF group was randomly selected, and measurements of ESA, ESD, PMA, and PMD were performed independently by two radiologists, each with five years of experience. As presented in Table 8 , the interobserver reliability of these measurements was assessed using intraclass correlation coefficients (ICC). The ICC values for ESA, ESD, PMA, and PMD were all found to be ≥ 0.90, indicating a high level of measurement agreement between observers. Table 3 Comparison of Current Vital Status and Two-Year Mortality in Patients with and Without Sarcopenia Based on Erector Spinae Muscle Index (ESI) Variable Group Sarcopenia Present (n = 27) Sarcopenia Absent (n = 79) p-value Vital Status Deceased 15 (55.6%) 27 (34.2%) 0.051 Alive 12 (44.4%) 52 (65.8%) Two-Year Mortality Deceased 13 (72.2%)ᵃ 21 (38.9%)ᵇ 0.014 Alive 5 (27.8%)ᵃ 33 (61.1%)ᵇ Note : p-values calculated using chi-square test. Superscript letters (ᵃ, ᵇ) indicate statistically significant differences between groups based on Bonferroni-adjusted post-hoc comparisons. Different letters represent a significant difference between the corresponding proportions. Table 4 Comparison of Current Vital Status and Two-Year Mortality between Patients with and Without Sarcopenia Based on Pectoralis Muscle Index (PMI) Variable Group Sarcopenia Present (n = 27) Sarcopenia Absent (n = 79) p-value Vital Status Deceased 10 (37.0%) 32 (40.5%) 0.750 Alive 17 (63.0%) 47 (59.5%) Two-Year Mortality Deceased 7 (43.8%) 27 (48.2%) 0.752 Alive 9 (56.3%) 29 (51.8%) Note : p-values were calculated using the chi-square test. Table 6 Comparison of CT Measurements According to GAP Stage in Patients Variable GAP Stage 1 (n = 41) GAP Stage 2 (n = 38) GAP Stage 3 (n = 6) p-value Pairwise Difference Mean ± SD (Min–Max) Mean ± SD (Min–Max) Mean ± SD (Min–Max) ESA (cm²) 37.52 ± 9.87 (8.11–53.24) 35.00 ± 6.64 (20.81–47.53) 31.17 ± 6.04 (23.98–39.19) 0.085 – ESI (cm²/m²) 13.37 ± 2.86 (3.75–17.65) 12.33 ± 2.20 (8.13–17.31) 11.11 ± 2.69 (8.11–15.75) 0.027 1 > 2,3 ESD (HU) 35.04 ± 11.60 (3.6–52.2) 39.55 ± 9.02 (18–53.9) 39.22 ± 10.15 (26.8–48.7) 0.302 – PMA (cm²) 32.87 ± 9.55 (15.69–61.67) 32.62 ± 8.36 (12.51–47.15) 36.10 ± 9.63 (25.4–51.18) 0.770 – PMI (cm²/m²) 11.77 ± 2.91 (5.83–21.34) 11.47 ± 2.84 (5.01–16.91) 12.84 ± 3.70 (8.39–18.41) 0.795 – PMD (HU) 33.15 ± 7.79 (7–44) 35.05 ± 9.73 (10.6–50.6) 39.92 ± 7.32 (30–47.4) 0.155 – Table 7 Relationship between Thoracic Skeletal Muscle Measurements and Pulmonary Function Test (PFT) Results in IPF Patients PFT Parameter ESA ESI ESD PMA PMI PMD FVC (L) 0.521** 0.348** 0.053 0.305** 0.131 -0.007 FVC (% predicted) 0.117 0.184 -0.366** -0.027 0.042 -0.269** FEV1 (L) 0.552** 0.383** 0.110 0.351** 0.177 0.042 FEV1 (% predicted) 0.149 0.231* -0.331** 0.019 0.098 -0.234* FEV1/FVC (%) -0.005 0.048 0.251* 0.081 0.117 0.195 PEF (L/s) 0.564** 0.438** 0.176 0.414** 0.271** 0.050 PEF (% predicted) 0.379** 0.399** -0.026 0.220* 0.208* -0.117 FEF25–75 (L/s) 0.332** 0.242* 0.197 0.336** 0.239* 0.202* FEF25–75 (% predicted) 0.191 0.213* -0.014 0.197 0.206* 0.051 DLCO (ml/min/mmHg) 0.273* 0.178 -0.096 0.163 0.097 -0.189 DLCO (% predicted) 0.056 0.096 -0.309** 0.026 0.089 -0.305** TLC (L) 0.377** 0.215 0.044 0.202 0.068 -0.051 TLC (% predicted) 0.136 0.140 -0.182 0.011 0.025 -0.186 VC (L) 0.541** 0.302* -0.002 0.231 0.030 -0.089 VC (% predicted) 0.061 0.158 -0.442** -0.135 -0.057 -0.407** Abbreviations : ESA: Erector Spinae Muscle Area; ESI: Erector Spinae Index (cm²/m²) ESD: Erector Spinae Muscle Density; PMA: Pectoralis Muscle Area; PMI: Pectoralis Muscle Index (cm²/m²) PMD: Pectoralis Muscle Density; Pearson correlation coefficient * p < .05 , ** p < .01 Table 8 Multivariate Cox Regression Analysis for Predictors of Two-Year Mortality in Patients with IPF (Model 1) Variables HR 95% CI p-value Sex 0.420 0.155–1.138 0.420 Age 0.991 0.959–1.024 0.578 BMI 0.978 0.877–1.091 0.687 ESI (cm²/m²) 0.807 0.692–0.942 0.006 CT-based measurements in the patient and control groups were compared using the independent samples t-test. Results of lung volume and thoracic skeletal muscle measurements are summarized in Tables 1 and 2 . Table 1 Comparison of Lung Volume Measurements between IPF Patient Group and Control Group Parameter IPF Patients (n = 106) Control Group (n = 106) p-value Mean ± SD Min–Max Mean ± SD Min–Max NALV (L) – Normal Attenuation Lung Volume 2.06 ± 1.32 0.27–5.54 3.99 ± 1.44 1.33–7.74 < 0.001 NALV (%) 56.93 ± 18.26 17.52–87.2 84.75 ± 8.11 51.3–93.05 < 0.001 LALV (L) – Low Attenuation Volume 0.04 ± 0.07 0–0.33 0.03 ± 0.06 0–0.47 0.240 LALV (%) 1.14 ± 1.55 0.01–6.51 0.59 ± 0.94 0–7.35 0.002 HALV (L) – High Attenuation Volume 1.20 ± 0.34 0.60–2.07 0.60 ± 0.21 0.35–1.30 < 0.001 HALV (%) 41.90 ± 18.33 12.69–82.05 14.66 ± 8.32 6.22–48.70 < 0.001 Total Lung Volume (L) 3.30 ± 1.30 1.09–7.70 4.62 ± 1.40 2.28–8.40 < 0.001 Abbreviations : NALV = Normal Attenuation Lung Volume; LALV = Low Attenuation Lung Volume; HALV = High Attenuation Lung Volume; SD = Standard Deviation. p-values were calculated using independent samples t-test. Table 2 Comparison of Thoracic Skeletal Muscle Measurements between IPF Patient Group and Control Group Parameter IPF Patients (n = 106) Control Group (n = 106) p-value Mean ± SD Min–Max Mean ± SD Min–Max ESA (cm²) – Erector Spinae Muscle Area 35.54 ± 9.44 8.11–59.91 40.64 ± 8.78 23.26–65.32 < 0.001 ESI (cm²/m²) – Erector Spinae Index 12.61 ± 2.94 3.75–19.34 14.25 ± 3.27 8.34–33.06 < 0.001 ESD (HU) – Erector Spinae Density 37.73 ± 9.92 3.6–53.9 39.81 ± 7.58 18.3–57.1 0.087 PMA (cm²) – Pectoralis Muscle Area 32.46 ± 8.83 12.51–61.67 36.74 ± 9.70 17.25–61.11 0.001 PMI (cm²/m²) – Pectoralis Muscle Index 11.54 ± 2.87 5.01–21.34 12.72 ± 3.09 6.35–22.45 0.004 PMD (HU) – Pectoralis Muscle Density 34.57 ± 8.77 7–59.9 34.16 ± 8.75 8.1–48.4 0.733 Abbreviations : ESA = Erector Spinae Area; ESI = Erector Spinae Index; ESD = Erector Spinae Muscle Density; PMA = Pectoralis Muscle Area; PMI = Pectoralis Muscle Index; PMD = Pectoralis Muscle Density; SD = Standard Deviation. p-values were calculated using the independent samples t-test. 3.1. Sarcopenia Classification and Mortality Association Since there are no universally accepted threshold values for sarcopenia at the thoracic level in IPF patients, male and female patients were stratified into quartiles based on the distribution of their Erector Spinae Index (ESI) and Pectoralis Muscle Index (PMI) values. Patients in the lowest quartile were classified as sarcopenic (ES-sarcopenia and PM-sarcopenia). The ESI cut-off values for identifying sarcopenia were calculated as 10.8288 cm²/m² for men and 8.9623 cm²/m² for women. The PMI cut-off values were 10.1055 cm²/m² for men and 8.4636 cm²/m² for women. A statistically significant difference in two-year mortality was observed between patients with and without sarcopenia, as defined by ESI (p < 0.05). According to the Bonferroni-adjusted post-hoc analysis, among patients who died within two years, the proportion of sarcopenic individuals was significantly higher than that of non-sarcopenic individuals. No statistically significant difference was observed in two-year mortality between patients with and without sarcopenia as defined by the pectoralis muscle index (p > 0.05). The relationship between lung volume parameters and thoracic skeletal muscle measurements in IPF patients was evaluated using the Pearson correlation method. As the erector spinae area (ESA) increased, normal attenuation lung volume (NALV in litres), total lung volume, and NALV percentage (%) also increased, while high attenuation lung volume percentage (HALV %) decreased. The detailed findings are presented in Table 5 . Table 5 Correlation between Lung Volume Parameters and Thoracic Skeletal Muscle Measurements in IPF Patients Measurement NALV (L) NALV (%) LALV (L) LALV (%) HALV (L) HALV (%) Total Lung Volume (L) ESA 0.520** 0.391** 0.072 –0.070 0.138 –0.383** 0.566** ESI 0.410** 0.376** –0.075 –0.203* –0.029 –0.356** 0.404** ESD 0.060 –0.004 0.019 0.045 0.232* 0.000 0.122 PMA 0.183 0.193* 0.094 0.091 0.200* –0.200* 0.241* PMI 0.058 0.167 –0.039 –0.025 0.041 –0.164 0.066 PMD –0.114 –0.130 0.056 0.122 0.274** 0.119 –0.041 Abbreviations : ESA = Erector Spinae Area; ESI = Erector Spinae Index; ESD = Erector Spinae Muscle Density; PMA = Pectoralis Muscle Area; PMI = Pectoralis Muscle Index; PMD = Pectoralis Muscle Density; r = Pearson correlation coefficient. * p < 0.05 , ** p < 0.01 The mean ESI values of patients in GAP Stage 1 were higher than those in GAP Stages 2 and 3. However, the mean ESI values of patients in GAP Stages 2 and 3 were similar to each other. As the ESA measurements increased, FVC (L), FEV1 (L), PEF (L/s), and VC (L) values also increased. Additionally, TLC (L), PEF (% predicted), FEF25–75 (L/s), DLCO (ml/min/mmHg), and DLCO (% predicted) values showed a positive association with ESA. As the ESI values increased, there was a corresponding increase in FVC (L), FEV1 (L), PEF (% predicted), FEF25–75 (L/s), FEF25–75 (% predicted), and VC (L) measurements. There was a positive and low-level correlation between PMA and FVC (L), FEV1 (L), and FEF25–75 (L/s) (r = 0.336, p < .05), while the correlation between PMA and PEF (L/s) was positive and moderate in strength. To evaluate the independent contribution of sarcopenia to mortality, a multivariate Cox regression analysis (Model 1) was performed. In Model 1, after adjusting for age, sex, and body mass index (BMI), the Erector Spinae Index (ESI, cm²/m²) was identified as a statistically significant independent predictor of all-cause mortality in patients with IPF. (HR: 0.807; 95% CI: 0.69–0.94; p = 0.006). 4. Discussion In the comparative analysis of demographic profiles between the patient and control cohorts, statistically significant reductions were observed in both mean body weight (p = 0.007) and BMI (p = 0.044) among patients with idiopathic pulmonary fibrosis (IPF). These findings are in concordance with existing literature and reinforce the presence of cachexia, a known systemic manifestation in IPF. Further comparative evaluation of CT-derived thoracic parameters revealed marked disparities in muscle mass, with the IPF cohort demonstrating substantial reductions in both erector spinae and pectoralis muscle areas. This muscular atrophy was accompanied by significantly diminished normal aerated lung volume (NALV) and total lung volume (TLV), likely attributable to progressive fibrotic parenchymal remodeling. Subsequent stratification of IPF patients into sarcopenic and non-sarcopenic subgroups, based on both the Erector Spinae Index (ESI) and Pectoralis Muscle Index (PMI), yielded insightful prognostic correlations. When classified according to ESI, patients identified as sarcopenic exhibited significantly elevated values of low attenuation lung volume (LALV%) and high attenuation lung volume (HALV%), while concurrently demonstrating lower values of NALV (L and %), TLV, forced vital capacity (FVC, L), and forced expiratory volume in one second (FEV1, L). These associations suggest that sarcopenia in IPF is strongly linked to reduced pulmonary reserve and more extensive parenchymal disease burden. Furthermore, the duration of follow-up was significantly longer in non-sarcopenic individuals, which may reflect both slower disease progression and enhanced survival. A focused survival analysis involving 72 patients with adequate follow-up data further emphasized the prognostic utility of ESI. Sarcopenic patients, as defined by this index, exhibited significantly lower two-year survival rates compared to their non-sarcopenic counterparts. Conversely, when PMI-based classification was applied, the only clinical metric showing a statistically significant intergroup difference was forced expiratory flow at 25–75% (FEF25–75, L/s), with no meaningful differences observed in other pulmonary function or volumetric parameters, nor in survival outcomes. This highlights the potential limitations of PMI as a prognostic indicator in IPF. Numerous studies have leveraged computer-aided chest CT analysis to quantitatively assess disease extent and progression in IPF [ 8 ]. Threshold-based segmentation techniques—such as those defining − 700 Hounsfield units (HU) as the delineation between normal lung and fibrotic parenchyma—have demonstrated significant correlations with pulmonary function test (PFT) results, validating their utility in clinical stratification [ 9 , 10 ]. One such study by Moon et al. evaluated the prognostic relevance of thoracic muscle mass, quantified via CT at the T4 vertebral level, in patients with IPF. Their findings indicated that sarcopenic individuals had significantly lower FVC (%) values, elevated GAP (Gender-Age-Physiology) scores, and worse two-year survival rates [ 11 ]. In the present study, although pectoralis muscle measurements were also obtained from the T4 level, our analysis did not reveal significant differences in lung function, volumetric parameters, or survival outcomes when stratified by PMI. However, substantial prognostic differences emerged when utilizing T12-level erector spinae muscle measurements, underscoring the relevance of measurement site and muscle group selection in assessing sarcopenia in IPF. Our study also incorporated GAP staging in a subset of 85 patients, which allowed for further validation of the ESI’s prognostic value. Patients classified within GAP Stage 1 demonstrated significantly higher ESI values than those in Stages 2 and 3, reaffirming the association between preserved muscle mass and milder disease severity. The findings from Awano et al. further corroborate our observations. In their study involving 199 IPF patients, muscle assessments were performed at analogous anatomical levels—erector spinae at the lower margin of T12 and pectoralis at the axial slice above the aortic arch—mirroring our methodology [ 12 ]. Both cross-sectional area (CSA) and mean attenuation (MA) were evaluated; however, muscle area values were not normalized to height or BMI, in contrast to our approach. Despite this, their results echoed ours: patients in the lowest quartile for erector spinae muscle CSA experienced significantly worse survival outcomes, whereas no such association was found for pectoralis muscle CSA. Notably, survival outcomes also did not differ among groups categorized by muscle attenuation, suggesting that muscle bulk may hold greater prognostic relevance than density. Both univariate and multivariate analyses in their study affirmed low erector spinae muscle CSA as an independent predictor of all-cause mortality, aligning closely with our findings. Similarly, Suzuki et al. explored the prognostic significance of erector spinae muscle metrics in a cohort comprising 131 IPF and 43 idiopathic pleuroparenchymal fibroelastosis (IPPFE) patients. Thoracic CT at the T12 level was employed to measure CSA and muscle density (ESD), with results benchmarked against a control population [ 13 ]. Congruent with our data, CSA values were significantly reduced in patients with interstitial lung disease. However, no significant differences were observed in muscle density (ESD) between patients and controls. Multivariate analysis in their study identified both ESA and FVC (%) as independent predictors of mortality, reinforcing the clinical importance of skeletal muscle evaluation in IPF. Parallel findings in our dataset demonstrated a positive correlation between ESA and several key functional indices, including FVC (L), FEV1 (L), diffusing capacity for carbon monoxide (DLCO), and total lung capacity (TLC, L). Further support is found in the work of Yannick et al., who assessed 164 patients with various forms of interstitial lung disease (including 76 IPF, 28 hypersensitivity pneumonitis, and 60 unclassifiable ILD) for sarcopenia using pectoralis muscle area (PMA) on thoracic CT [ 14 ]. Their longitudinal analysis aimed to determine whether PMA correlated with disease severity and long-term outcomes. They reported positive associations between PMA and FVC, DLCO, and oxygen saturation—both at rest and during exercise. Correspondingly, our study also found a low but statistically significant positive correlation between PMA and both FVC and FEV1, thereby lending further credence to PMA as a general indicator of functional status. However, this correlation was less robust than those observed with erector spinae metrics. Distinctively, our investigation is one of the few to systematically examine the relationship between CT-derived thoracic skeletal muscle indices and quantitative lung volume parameters. Our findings suggest that patients identified as sarcopenic by ESI exhibit significantly lower NALV (L), NAAV (%), and TLV compared to non-sarcopenic individuals. Conversely, sarcopenic patients showed significantly higher LALV (%) and HALV (%) values—indicative of more extensive fibrotic transformation and reduced functional lung parenchyma. These findings underscore a compelling inverse relationship between the extent of lung involvement and skeletal muscle mass at the T12 level, suggesting that declining respiratory muscle integrity may parallel worsening disease. By contrast, PMI-based stratification failed to show statistically meaningful differences in lung volume measurements between sarcopenic and non-sarcopenic groups. This discrepancy prompts consideration of potential confounding variables. Technical factors such as variability in pectoralis muscle measurement level, inconsistencies due to shoulder and arm positioning, and the intrinsic function of pectoralis muscles as active respiratory components may contribute to their preserved mass even amidst progressive disease. These nuances may account for the lower discriminatory power of PMI relative to ESI. Taken together, our findings position the Erector Spinae Index as a more reliable and clinically informative metric for evaluating sarcopenia and predicting disease progression in patients with idiopathic pulmonary fibrosis. ESI demonstrates superior correlation with functional and volumetric lung parameters, GAP staging, and survival outcomes when compared to PMI. 5. Conclusion In conclusion, the findings of our study demonstrate that, without additional radiation exposure, routinely acquired chest CT scans can be utilized to extract valuable prognostic information in patients with idiopathic pulmonary fibrosis (IPF). Specifically, the erector spinae muscle index (ESI)—normalized to patient height—and the threshold-based quantitative lung volumes (NALV% and HALV%), calculated using artificial intelligence–based software, were found to be significant prognostic indicators of two-year survival. Among these, NALV (%) emerged as an independent predictor of all-cause mortality. Our results revealed that IPF patients with low erector spinae muscle mass had significantly lower survival rates. Given that pulmonary function tests (PFTs) and DLCO measurements require a high level of patient cooperation, the use of CT-derived quantitative parameters—such as ESI and NALV (%)—may be especially beneficial in estimating prognosis and mortality risk in patients who are unable to perform or poorly tolerate functional tests. Declarations Funding Declaration This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data Availability Statement: The datasets generated and/or analyzed during the current study are not publicly available due to institutional data protection regulations, but are available from the corresponding author on reasonable request. Ethics, Consent to Participate, and Consent to Publish Declarations Ethics approval: Ethical approval was obtained from the Clinical Research Ethics Committee of Ankara Bilkent City Hospital (Date: 11.10.2023, Decision No: E2-23-5201). Consent to participate: As this was a retrospective study using anonymized clinical and imaging data, the requirement for informed consent was waived by the ethics committee. Consent to publish: Not applicable, as no individual person’s data, images, or identifying details are published. Author Contribution Author Contributions StatementM.K. and M.A.K. designed the study and conducted the CT image analysis. M.K. performed the statistical analysis and interpreted the data. E.Ş.Ş.P. contributed to clinical data acquisition and provided pulmonary medicine expertise. M.K. and M.A.K. wrote the main manuscript text. E.Ş.Ş.P. contributed to the review and editing of the manuscript. All authors reviewed and approved the final version of the manuscript. References Martin MD, Chung JH, Kanne JP. Idiopathic pulmonary fibrosis. J Thorac Imaging. 2016;31(3):127–39. Chung JH, Cox CW, Montner SM, Adegunsoye A, Oldham JM, Husain AN, et al. CT features of the usual interstitial pneumonia pattern: differentiating connective tissue disease-associated interstitial lung disease from idiopathic pulmonary fibrosis. AJR Am J Roentgenol. 2018;210(2):307–13. Chung JH, Lynch DA. The value of a multidisciplinary approach to the diagnosis of usual interstitial pneumonitis and idiopathic pulmonary fibrosis: radiology, pathology, and clinical correlation. AJR Am J Roentgenol. 2016;206(3):463–71. Epidemiologic. and methodologic problems in determining nutritional status of older persons. Proceedings of a conference. Am J Clin Nutr. 1989;50(5 Suppl):1121–235. Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. Sarcopenia: European consensus on definition and diagnosis. Age Ageing. 2010;39(4):412–23. Asakura T, Yamada Y, Suzuki S, Namkoong H, Okamori S, Kusumoto T, et al. Quantitative assessment of erector spinae muscles in patients with Mycobacterium avium complex lung disease. Respir Med. 2018;145:66–72. Ohkubo H, Taniguchi H, Kondoh Y, Yagi M, Furukawa T, Johkoh T, et al. A volumetric computed tomography analysis of the normal lung in idiopathic pulmonary fibrosis: the relationship with the survival. Intern Med. 2018;57(7):929–37. Bak SH, Kwon SO, Han SS, Kim WJ. Computed tomography-derived area and density of pectoralis muscle associated with disease severity and longitudinal changes in chronic obstructive pulmonary disease: a case control study. Respir Res. 2019;20(1):226. Shin KE, Chung MJ, Jung MP, Choe BK, Lee KS. Quantitative computed tomographic indexes in diffuse interstitial lung disease: correlation with physiologic tests and computed tomography visual scores. J Comput Assist Tomogr. 2011;35(2):266–71. Ohkubo H, Kanemitsu Y, Uemura T, Takakuwa O, Takemura M, Maeno K, et al. Normal lung quantification in usual interstitial pneumonia pattern: the impact of threshold-based volumetric CT analysis for the staging of idiopathic pulmonary fibrosis. PLoS ONE. 2016;11(3):e0152505. Moon SW, Choi JS, Lee SH, Jung KS, Jung JY, Kang YA, et al. Thoracic skeletal muscle quantification: low muscle mass is related with worse prognosis in idiopathic pulmonary fibrosis patients. Respir Res. 2019;20(1):35. Awano N, Inomata M, Kuse N, Tone M, Yoshimura H, Jo T, et al. Quantitative computed tomography measures of skeletal muscle mass in patients with idiopathic pulmonary fibrosis according to a multidisciplinary discussion diagnosis: a retrospective nationwide study in Japan. Respir Investig. 2020;58(2):91–101. Suzuki Y, Yoshimura K, Enomoto Y, Yasui H, Hozumi H, Karayama M, et al. Distinct profile and prognostic impact of body composition changes in idiopathic pulmonary fibrosis and idiopathic pleuroparenchymal fibroelastosis. Sci Rep. 2018;8(1):14074. Molgat-Seon Y, Guler SA, Peters CM, Vasilescu DM, Puyat JH, Coxson HO, et al. Pectoralis muscle area and its association with indices of disease severity in interstitial lung disease. Respir Med. 2021;186:106539. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7436178","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":532269283,"identity":"6ce77322-bd7e-46bd-8995-f8572e8c801a","order_by":0,"name":"Mustafa Abdulgani Kurt","email":"","orcid":"","institution":"Ankara City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mustafa","middleName":"Abdulgani","lastName":"Kurt","suffix":""},{"id":532269284,"identity":"27174485-7891-4c68-8e90-9d834fad797c","order_by":1,"name":"Murathan 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14:04:25","extension":"html","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165267,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7436178/v1/c3901edf619e3af5c4ed17bf.html"},{"id":94410239,"identity":"544fe9b5-1ab1-4794-adeb-6b6243c692fa","added_by":"auto","created_at":"2025-10-27 14:04:35","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":99757,"visible":true,"origin":"","legend":"\u003cp\u003eCT-based segmentation of the paraspinal muscles in the same patient. Muscle tissues were isolated using a Hounsfield Unit (HU) range of −29 to +150, and both cross-sectional area and mean attenuation values were measured. Color-coded visualization of the segmented region is displayed.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7436178/v1/96c177f00e1441ccf32f0f91.jpeg"},{"id":94410164,"identity":"17b0329b-fe7f-4797-8b25-9f34d5bb3b0f","added_by":"auto","created_at":"2025-10-27 14:04:33","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78791,"visible":true,"origin":"","legend":"\u003cp\u003eSegmented and color-coded axial CT image from a 58-year-old male patient with idiopathic pulmonary fibrosis (IPF). The image shows the pectoralis major and minor muscles at the first axial slice above the aortic arch, with segmentation performed for quantitative assessment.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7436178/v1/45d3ab277ba8505c326d8672.jpeg"},{"id":94410140,"identity":"9eeff08d-f5aa-41e6-9f17-a5f40695f1e8","added_by":"auto","created_at":"2025-10-27 14:04:32","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":433873,"visible":true,"origin":"","legend":"\u003cp\u003eAutomated lung segmentation and quantitative lung volume analysis performed using the Thoracic VCAR software (GE Healthcare). The software differentiates normal, low, and high attenuation regions based on Hounsfield Unit thresholds.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7436178/v1/5b7f8f093e9a21809965de89.jpeg"},{"id":94410142,"identity":"74c62db2-16d2-44b5-bd2d-b59ea2d000a2","added_by":"auto","created_at":"2025-10-27 14:04:32","extension":"jpeg","order_by":20,"title":"Figure 20","display":"","copyAsset":false,"role":"figure","size":99757,"visible":true,"origin":"","legend":"\u003cp\u003eCT-based segmentation of the paraspinal muscles in the same patient. Muscle tissues were isolated using a Hounsfield Unit (HU) range of −29 to +150, and both cross-sectional area and mean attenuation values were measured. Color-coded visualization of the segmented region is displayed.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7436178/v1/5c9f72ea83fd3e5aaed30816.jpeg"},{"id":94409914,"identity":"bf819e3c-71bf-48ee-9eb6-fe0ac1a551f0","added_by":"auto","created_at":"2025-10-27 14:04:21","extension":"jpeg","order_by":21,"title":"Figure 21","display":"","copyAsset":false,"role":"figure","size":78791,"visible":true,"origin":"","legend":"\u003cp\u003eSegmented and color-coded axial CT image from a 58-year-old male patient with idiopathic pulmonary fibrosis (IPF). The image shows the pectoralis major and minor muscles at the first axial slice above the aortic arch, with segmentation performed for quantitative assessment.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7436178/v1/ae13eb6197d0f23498d0cf38.jpeg"},{"id":94410148,"identity":"fbb0acb9-fae8-4275-81c0-d5ab95a4b33e","added_by":"auto","created_at":"2025-10-27 14:04:32","extension":"jpeg","order_by":22,"title":"Figure 22","display":"","copyAsset":false,"role":"figure","size":433873,"visible":true,"origin":"","legend":"\u003cp\u003eAutomated lung segmentation and quantitative lung volume analysis performed using the Thoracic VCAR software (GE Healthcare). The software differentiates normal, low, and high attenuation regions based on Hounsfield Unit thresholds.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7436178/v1/18fadc5e9a7a52f62d8757ee.jpeg"},{"id":105073587,"identity":"d721bd07-2cbc-438a-82a3-826e74d9ff4a","added_by":"auto","created_at":"2026-03-20 15:41:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2349551,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7436178/v1/28d110ea-6860-479e-9ca4-eef4e84ed2a9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Quantitative Evaluation of Thoracic Skeletal Muscle Mass and Normal/Fibrotic Lung Volumes on CT in Idiopathic Pulmonary Fibrosis Patients: Prognostic Significance","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIdiopathic Pulmonary Fibrosis (IPF) is the most common chronic and progressive fibrotic lung disease of unknown etiology [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. IPF is characterized histologically and radiologically by the usual interstitial pneumonia (UIP) pattern [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. On thoracic computed tomography (CT), irregular reticulations predominantly in the subpleural and lower lobe zones, traction bronchiectasis, volume loss, and honeycombing are typically observed [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSarcopenia is a syndrome characterized by progressive loss of muscle mass and decreased muscle strength [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It has been associated with poor prognosis in many medical conditions. Although several diagnostic methods are used to assess sarcopenia, the measurement of cross-sectional muscle area (SMA) at the level of the third lumbar vertebra (L3) by CT and the derived index obtained by dividing this value by the square of the patient\u0026rsquo;s height (SMI) are widely accepted in sarcopenia assessment [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn addition, previous studies have employed measurements of the pectoral muscles and the erector spinae muscles at the T12 vertebral level for sarcopenia evaluation across different patient populations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe main objective of this study is to investigate the relationship between IPF and sarcopenia, and to determine the potential prognostic significance and impact on mortality. Furthermore, we aim to identify a linear relationship between muscle loss, lung volumes, and IPF severity through quantitative area-based and volumetric measurements.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Ethics Statement\u003c/h2\u003e\u003cp\u003e This retrospective study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Clinical Research Ethics Committee of Ankara Bilkent City Hospital (Date: 11.10.2023, Decision No: E2-23-5201).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Study Design\u003c/h2\u003e\u003cp\u003ePatients who underwent non-contrast thoracic computed tomography (CT) between January 2019 and August 2023 and demonstrated a usual interstitial pneumonia (UIP) pattern in the lung parenchyma were retrospectively reviewed (n\u0026thinsp;=\u0026thinsp;197). Among these patients, those with a known connective tissue disease (n\u0026thinsp;=\u0026thinsp;47), a history of occupational or environmental exposure (n\u0026thinsp;=\u0026thinsp;9), malignancy (n\u0026thinsp;=\u0026thinsp;12), coexisting chronic obstructive pulmonary disease (COPD) (n\u0026thinsp;=\u0026thinsp;11), or significant CT artifacts (n\u0026thinsp;=\u0026thinsp;12) were excluded. A total of 106 IPF patients who met the inclusion criteria were included in the study. In addition, a control group consisting of 106 age- and sex-matched individuals without any chronic lung disease or malignancy, who had undergone non-contrast thoracic CT for other reasons, was also included.\u003c/p\u003e\u003cp\u003eThe following data were recorded for all participants: age, sex, height, weight, body mass index (BMI), pulmonary function test (PFT) results, GAP index score and stage, survival status, and follow-up duration (in months).\u003c/p\u003e\u003cp\u003eAll CT scans were performed with the patient in the supine position following full inspiration and without intravenous contrast administration. Thoracic CT was performed using a 128-slice multidetector CT scanner (Revolution EVO, General Electric Medical Systems, Milwaukee, Wisconsin, USA).\u003c/p\u003e\u003cp\u003eCT images were analyzed using the Thoracic VCAR software package on the Advantage Workstation 4.7 (GE Healthcare, USA), which enables quantitative analysis in the mediastinal window. The artificial intelligence\u0026ndash;based Thoracic VCAR software performed automatic segmentation of the lungs and calculated the volumes and percentage distributions of hyperinflated, non-aerated, and poorly aerated parenchymal areas based on attenuation values. While performing these measurements, the software included segmental bronchi, vessels, and interstitial structures of both lungs but excluded the main pulmonary arteries, bronchi, mediastinal structures, and pleural effusions.\u003c/p\u003e\u003cp\u003eAccording to previously published threshold-based studies, lung attenuation ranges were categorized as (a.) areas with attenuation values below \u0026minus;\u0026thinsp;950 Hounsfield units (HU) were classified as low-attenuation areas (e.g., emphysema), (b.) areas between \u0026minus;\u0026thinsp;950 and \u0026minus;\u0026thinsp;705 HU were considered normally aerated lung parenchyma, (c.) areas above \u0026minus;\u0026thinsp;705 HU were defined as high-attenuation areas, representing ground-glass opacities, fibrotic changes, or consolidations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCross-sectional area (CSA) and mean attenuation values of the pectoral muscles (pectoralis major and minor) were measured on the first axial CT slice immediately above the aortic arch. CSA and mean attenuation of the erector spinae muscles were measured on a single axial CT slice taken at the lower margin of the 12th thoracic vertebra. Muscle boundaries were manually delineated using an attenuation range of \u0026minus;\u0026thinsp;29 to +\u0026thinsp;150 HU. Cross-sectional areas were measured in square centimeters (cm\u0026sup2;).\u003c/p\u003e\u003cp\u003eAll measurements were manually performed by two radiologists with at least four years of experience. For normalization and sarcopenia evaluation, pectoral muscle area (PMA) and erector spinae muscle area (ESA) were divided by the square of the patient\u0026rsquo;s height (m\u0026sup2;) to obtain the Pectoralis Muscle Index (PMI) and Erector Spinae Index (ESI), respectively, expressed in cm\u0026sup2;/m\u0026sup2;.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using SPSS version 26.0 (IBM Corp., Armonk, NY, USA). To examine the association between sarcopenia status (defined based on erector spinae and pectoralis muscle indices) and patient survival or two-year mortality, chi-square tests were used. For significant chi-square results, the Bonferroni correction was applied to determine between which groups the differences existed. Due to insufficient sample sizes in subgroups, the GAP stage variable (with three categories) was analyzed using the Kruskal\u0026ndash;Wallis test. In cases where the Kruskal\u0026ndash;Wallis test showed statistical significance, pairwise comparisons were conducted using the Mann\u0026ndash;Whitney U test.\u003c/p\u003e\u003cp\u003eThe Pearson correlation coefficient was used to evaluate the relationship between continuous variables within the patient group. Survival analyses were performed using the Kaplan\u0026ndash;Meier method, and differences in survival curves were assessed using the log-rank test. Univariate and multivariate analyses for two-year survival outcomes were conducted using the Cox proportional hazards regression model. Interobserver agreement for continuous variables such as pectoralis muscle area (PMA), erector spinae area (ESA), and their mean attenuations (PM-density and ESM-density) was assessed using the intraclass correlation coefficient (ICC) with corresponding 95% confidence intervals. A p-value of less than 0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eA subset of 25 patients from the IPF group was randomly selected, and measurements of ESA, ESD, PMA, and PMD were performed independently by two radiologists, each with five years of experience. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the interobserver reliability of these measurements was assessed using intraclass correlation coefficients (ICC). The ICC values for ESA, ESD, PMA, and PMD were all found to be \u0026ge;\u0026thinsp;0.90, indicating a high level of measurement agreement between observers.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Current Vital Status and Two-Year Mortality in Patients with and Without Sarcopenia Based on Erector Spinae Muscle Index (ESI)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSarcopenia Present (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSarcopenia Absent (n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVital Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeceased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15 (55.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27 (34.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12 (44.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52 (65.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTwo-Year Mortality\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeceased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13 (72.2%)ᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e21 (38.9%)ᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5 (27.8%)ᵃ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33 (61.1%)ᵇ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e:\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003ep-values calculated using chi-square test. Superscript letters (ᵃ, ᵇ) indicate statistically significant differences between groups based on Bonferroni-adjusted post-hoc comparisons. Different letters represent a significant difference between the corresponding proportions.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Current Vital Status and Two-Year Mortality between Patients with and Without Sarcopenia Based on Pectoralis Muscle Index (PMI)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSarcopenia Present (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSarcopenia Absent (n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVital Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeceased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10 (37.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32 (40.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17 (63.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47 (59.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTwo-Year Mortality\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDeceased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7 (43.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27 (48.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.752\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9 (56.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29 (51.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e:\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003ep-values were calculated using the chi-square test.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of CT Measurements According to GAP Stage in Patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGAP Stage 1 (n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGAP Stage 2 (n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGAP Stage 3 (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePairwise Difference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Min\u0026ndash;Max)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Min\u0026ndash;Max)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Min\u0026ndash;Max)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eESA (cm\u0026sup2;)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e37.52\u0026thinsp;\u0026plusmn;\u0026thinsp;9.87 (8.11\u0026ndash;53.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e35.00\u0026thinsp;\u0026plusmn;\u0026thinsp;6.64 (20.81\u0026ndash;47.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e31.17\u0026thinsp;\u0026plusmn;\u0026thinsp;6.04 (23.98\u0026ndash;39.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eESI (cm\u0026sup2;/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e13.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86 (3.75\u0026ndash;17.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e12.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.20 (8.13\u0026ndash;17.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e11.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.69 (8.11\u0026ndash;15.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u0026thinsp;\u0026gt;\u0026thinsp;2,3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eESD (HU)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e35.04\u0026thinsp;\u0026plusmn;\u0026thinsp;11.60 (3.6\u0026ndash;52.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e39.55\u0026thinsp;\u0026plusmn;\u0026thinsp;9.02 (18\u0026ndash;53.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e39.22\u0026thinsp;\u0026plusmn;\u0026thinsp;10.15 (26.8\u0026ndash;48.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePMA (cm\u0026sup2;)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e32.87\u0026thinsp;\u0026plusmn;\u0026thinsp;9.55 (15.69\u0026ndash;61.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e32.62\u0026thinsp;\u0026plusmn;\u0026thinsp;8.36 (12.51\u0026ndash;47.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e36.10\u0026thinsp;\u0026plusmn;\u0026thinsp;9.63 (25.4\u0026ndash;51.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePMI (cm\u0026sup2;/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e11.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.91 (5.83\u0026ndash;21.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e11.47\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84 (5.01\u0026ndash;16.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e12.84\u0026thinsp;\u0026plusmn;\u0026thinsp;3.70 (8.39\u0026ndash;18.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePMD (HU)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e33.15\u0026thinsp;\u0026plusmn;\u0026thinsp;7.79 (7\u0026ndash;44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e35.05\u0026thinsp;\u0026plusmn;\u0026thinsp;9.73 (10.6\u0026ndash;50.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e\u003cp\u003e39.92\u0026thinsp;\u0026plusmn;\u0026thinsp;7.32 (30\u0026ndash;47.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelationship between Thoracic Skeletal Muscle Measurements and Pulmonary Function Test (PFT) Results in IPF Patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePFT Parameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eESA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eESI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eESD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePMA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePMI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePMD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFVC (L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.521**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.348**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.305**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFVC (% predicted)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.366**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.269**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFEV1 (L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.552**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.383**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.351**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFEV1 (% predicted)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.231*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.331**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.234*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFEV1/FVC (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.251*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePEF (L/s)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.564**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.438**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.414**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.271**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePEF (% predicted)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.379**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.399**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.220*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.208*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFEF25\u0026ndash;75 (L/s)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.332**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.242*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.336**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.239*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.202*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFEF25\u0026ndash;75 (% predicted)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.213*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.206*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDLCO (ml/min/mmHg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.273*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.189\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDLCO (% predicted)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.309**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.305**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTLC (L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.377**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.051\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTLC (% predicted)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.186\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVC (L)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.541**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.302*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.089\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVC (% predicted)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.442**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.407**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e:\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eESA: Erector Spinae Muscle Area; ESI: Erector Spinae Index (cm\u0026sup2;/m\u0026sup2;) ESD: Erector Spinae Muscle Density; PMA: Pectoralis Muscle Area; PMI: Pectoralis Muscle Index (cm\u0026sup2;/m\u0026sup2;) PMD: Pectoralis Muscle Density; Pearson correlation coefficient\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003e*\u003c/b\u003e \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;.05\u003c/em\u003e, \u003cb\u003e**\u003c/b\u003e \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;.01\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate Cox Regression Analysis for Predictors of Two-Year Mortality in Patients with IPF (Model 1)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.155\u0026ndash;1.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.420\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.959\u0026ndash;1.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.877\u0026ndash;1.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.687\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eESI (cm\u0026sup2;/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.692\u0026ndash;0.942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCT-based measurements in the patient and control groups were compared using the independent samples t-test. Results of lung volume and thoracic skeletal muscle measurements are summarized in Tables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Lung Volume Measurements between IPF Patient Group and Control Group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIPF Patients (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControl Group (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMin\u0026ndash;Max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u0026ndash;Max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNALV (L) \u0026ndash; Normal Attenuation Lung Volume\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.27\u0026ndash;5.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.33\u0026ndash;7.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNALV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56.93\u0026thinsp;\u0026plusmn;\u0026thinsp;18.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.52\u0026ndash;87.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84.75\u0026thinsp;\u0026plusmn;\u0026thinsp;8.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51.3\u0026ndash;93.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLALV (L) \u0026ndash; Low Attenuation Volume\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u0026ndash;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.240\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLALV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u0026ndash;6.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u0026ndash;7.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHALV (L) \u0026ndash; High Attenuation Volume\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.60\u0026ndash;2.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.35\u0026ndash;1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHALV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41.90\u0026thinsp;\u0026plusmn;\u0026thinsp;18.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.69\u0026ndash;82.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.66\u0026thinsp;\u0026plusmn;\u0026thinsp;8.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.22\u0026ndash;48.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Lung Volume (L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.09\u0026ndash;7.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.28\u0026ndash;8.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e:\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNALV\u0026thinsp;=\u0026thinsp;Normal Attenuation Lung Volume; LALV\u0026thinsp;=\u0026thinsp;Low Attenuation Lung Volume; HALV\u0026thinsp;=\u0026thinsp;High Attenuation Lung Volume;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eSD\u0026thinsp;=\u0026thinsp;Standard Deviation.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003ep-values were calculated using independent samples t-test.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Thoracic Skeletal Muscle Measurements between IPF Patient Group and Control Group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIPF Patients (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControl Group (n\u0026thinsp;=\u0026thinsp;106)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMin\u0026ndash;Max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u0026ndash;Max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESA (cm\u0026sup2;) \u0026ndash; Erector Spinae Muscle Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35.54\u0026thinsp;\u0026plusmn;\u0026thinsp;9.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.11\u0026ndash;59.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.64\u0026thinsp;\u0026plusmn;\u0026thinsp;8.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.26\u0026ndash;65.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESI (cm\u0026sup2;/m\u0026sup2;) \u0026ndash; Erector Spinae Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.61\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.75\u0026ndash;19.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.34\u0026ndash;33.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESD (HU) \u0026ndash; Erector Spinae Density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.73\u0026thinsp;\u0026plusmn;\u0026thinsp;9.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.6\u0026ndash;53.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.81\u0026thinsp;\u0026plusmn;\u0026thinsp;7.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.3\u0026ndash;57.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePMA (cm\u0026sup2;) \u0026ndash; Pectoralis Muscle Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.46\u0026thinsp;\u0026plusmn;\u0026thinsp;8.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.51\u0026ndash;61.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.74\u0026thinsp;\u0026plusmn;\u0026thinsp;9.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.25\u0026ndash;61.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePMI (cm\u0026sup2;/m\u0026sup2;) \u0026ndash; Pectoralis Muscle Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.01\u0026ndash;21.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.72\u0026thinsp;\u0026plusmn;\u0026thinsp;3.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.35\u0026ndash;22.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePMD (HU) \u0026ndash; Pectoralis Muscle Density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.57\u0026thinsp;\u0026plusmn;\u0026thinsp;8.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u0026ndash;59.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.16\u0026thinsp;\u0026plusmn;\u0026thinsp;8.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.1\u0026ndash;48.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.733\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e:\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eESA\u0026thinsp;=\u0026thinsp;Erector Spinae Area; ESI\u0026thinsp;=\u0026thinsp;Erector Spinae Index; ESD\u0026thinsp;=\u0026thinsp;Erector Spinae Muscle Density; PMA\u0026thinsp;=\u0026thinsp;Pectoralis Muscle Area; PMI\u0026thinsp;=\u0026thinsp;Pectoralis Muscle Index; PMD\u0026thinsp;=\u0026thinsp;Pectoralis Muscle Density; SD\u0026thinsp;=\u0026thinsp;Standard Deviation.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003ep-values were calculated using the independent samples t-test.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Sarcopenia Classification and Mortality Association\u003c/h2\u003e\u003cp\u003eSince there are no universally accepted threshold values for sarcopenia at the thoracic level in IPF patients, male and female patients were stratified into quartiles based on the distribution of their Erector Spinae Index (ESI) and Pectoralis Muscle Index (PMI) values. Patients in the lowest quartile were classified as sarcopenic (ES-sarcopenia and PM-sarcopenia).\u003c/p\u003e\u003cp\u003eThe ESI cut-off values for identifying sarcopenia were calculated as 10.8288 cm\u0026sup2;/m\u0026sup2; for men and 8.9623 cm\u0026sup2;/m\u0026sup2; for women. The PMI cut-off values were 10.1055 cm\u0026sup2;/m\u0026sup2; for men and 8.4636 cm\u0026sup2;/m\u0026sup2; for women.\u003c/p\u003e\u003cp\u003eA statistically significant difference in two-year mortality was observed between patients with and without sarcopenia, as defined by ESI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). According to the Bonferroni-adjusted post-hoc analysis, among patients who died within two years, the proportion of sarcopenic individuals was significantly higher than that of non-sarcopenic individuals. No statistically significant difference was observed in two-year mortality between patients with and without sarcopenia as defined by the pectoralis muscle index (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eThe relationship between lung volume parameters and thoracic skeletal muscle measurements in IPF patients was evaluated using the Pearson correlation method. As the erector spinae area (ESA) increased, normal attenuation lung volume (NALV in litres), total lung volume, and NALV percentage (%) also increased, while high attenuation lung volume percentage (HALV %) decreased. The detailed findings are presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation between Lung Volume Parameters and Thoracic Skeletal Muscle Measurements in IPF Patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasurement\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNALV (L)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNALV (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLALV (L)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLALV (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHALV (L)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHALV (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTotal Lung Volume (L)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eESA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.520**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.391**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;0.383**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.566**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eESI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.410**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.376**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;0.203*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026ndash;0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;0.356**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.404**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eESD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.232*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePMA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.193*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.200*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;0.200*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.241*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;0.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePMD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.274**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u0026ndash;0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e:\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eESA\u0026thinsp;=\u0026thinsp;Erector Spinae Area; ESI\u0026thinsp;=\u0026thinsp;Erector Spinae Index; ESD\u0026thinsp;=\u0026thinsp;Erector Spinae Muscle Density; PMA\u0026thinsp;=\u0026thinsp;Pectoralis Muscle Area;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003ePMI\u0026thinsp;=\u0026thinsp;Pectoralis Muscle Index; PMD\u0026thinsp;=\u0026thinsp;Pectoralis Muscle Density; \u003cb\u003er\u0026thinsp;=\u003c/b\u003e\u0026thinsp;Pearson correlation coefficient.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003e*\u003c/b\u003e \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e, \u003cb\u003e**\u003c/b\u003e \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe mean ESI values of patients in GAP Stage 1 were higher than those in GAP Stages 2 and 3. However, the mean ESI values of patients in GAP Stages 2 and 3 were similar to each other.\u003c/p\u003e\u003cp\u003eAs the ESA measurements increased, FVC (L), FEV1 (L), PEF (L/s), and VC (L) values also increased. Additionally, TLC (L), PEF (% predicted), FEF25\u0026ndash;75 (L/s), DLCO (ml/min/mmHg), and DLCO (% predicted) values showed a positive association with ESA. As the ESI values increased, there was a corresponding increase in FVC (L), FEV1 (L), PEF (% predicted), FEF25\u0026ndash;75 (L/s), FEF25\u0026ndash;75 (% predicted), and VC (L) measurements. There was a positive and low-level correlation between PMA and FVC (L), FEV1 (L), and FEF25\u0026ndash;75 (L/s) (r\u0026thinsp;=\u0026thinsp;0.336, p\u0026thinsp;\u0026lt;\u0026thinsp;.05), while the correlation between PMA and PEF (L/s) was positive and moderate in strength. To evaluate the independent contribution of sarcopenia to mortality, a multivariate Cox regression analysis (Model 1) was performed. In Model 1, after adjusting for age, sex, and body mass index (BMI), the Erector Spinae Index (ESI, cm\u0026sup2;/m\u0026sup2;) was identified as a statistically significant independent predictor of all-cause mortality in patients with IPF. (HR: 0.807; 95% CI: 0.69\u0026ndash;0.94; p\u0026thinsp;=\u0026thinsp;0.006).\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn the comparative analysis of demographic profiles between the patient and control cohorts, statistically significant reductions were observed in both mean body weight (p\u0026thinsp;=\u0026thinsp;0.007) and BMI (p\u0026thinsp;=\u0026thinsp;0.044) among patients with idiopathic pulmonary fibrosis (IPF). These findings are in concordance with existing literature and reinforce the presence of cachexia, a known systemic manifestation in IPF. Further comparative evaluation of CT-derived thoracic parameters revealed marked disparities in muscle mass, with the IPF cohort demonstrating substantial reductions in both erector spinae and pectoralis muscle areas. This muscular atrophy was accompanied by significantly diminished normal aerated lung volume (NALV) and total lung volume (TLV), likely attributable to progressive fibrotic parenchymal remodeling.\u003c/p\u003e\u003cp\u003eSubsequent stratification of IPF patients into sarcopenic and non-sarcopenic subgroups, based on both the Erector Spinae Index (ESI) and Pectoralis Muscle Index (PMI), yielded insightful prognostic correlations. When classified according to ESI, patients identified as sarcopenic exhibited significantly elevated values of low attenuation lung volume (LALV%) and high attenuation lung volume (HALV%), while concurrently demonstrating lower values of NALV (L and %), TLV, forced vital capacity (FVC, L), and forced expiratory volume in one second (FEV1, L). These associations suggest that sarcopenia in IPF is strongly linked to reduced pulmonary reserve and more extensive parenchymal disease burden. Furthermore, the duration of follow-up was significantly longer in non-sarcopenic individuals, which may reflect both slower disease progression and enhanced survival.\u003c/p\u003e\u003cp\u003eA focused survival analysis involving 72 patients with adequate follow-up data further emphasized the prognostic utility of ESI. Sarcopenic patients, as defined by this index, exhibited significantly lower two-year survival rates compared to their non-sarcopenic counterparts. Conversely, when PMI-based classification was applied, the only clinical metric showing a statistically significant intergroup difference was forced expiratory flow at 25\u0026ndash;75% (FEF25\u0026ndash;75, L/s), with no meaningful differences observed in other pulmonary function or volumetric parameters, nor in survival outcomes. This highlights the potential limitations of PMI as a prognostic indicator in IPF.\u003c/p\u003e\u003cp\u003eNumerous studies have leveraged computer-aided chest CT analysis to quantitatively assess disease extent and progression in IPF [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Threshold-based segmentation techniques\u0026mdash;such as those defining \u0026minus;\u0026thinsp;700 Hounsfield units (HU) as the delineation between normal lung and fibrotic parenchyma\u0026mdash;have demonstrated significant correlations with pulmonary function test (PFT) results, validating their utility in clinical stratification [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. One such study by Moon et al. evaluated the prognostic relevance of thoracic muscle mass, quantified via CT at the T4 vertebral level, in patients with IPF. Their findings indicated that sarcopenic individuals had significantly lower FVC (%) values, elevated GAP (Gender-Age-Physiology) scores, and worse two-year survival rates [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In the present study, although pectoralis muscle measurements were also obtained from the T4 level, our analysis did not reveal significant differences in lung function, volumetric parameters, or survival outcomes when stratified by PMI. However, substantial prognostic differences emerged when utilizing T12-level erector spinae muscle measurements, underscoring the relevance of measurement site and muscle group selection in assessing sarcopenia in IPF.\u003c/p\u003e\u003cp\u003eOur study also incorporated GAP staging in a subset of 85 patients, which allowed for further validation of the ESI\u0026rsquo;s prognostic value. Patients classified within GAP Stage 1 demonstrated significantly higher ESI values than those in Stages 2 and 3, reaffirming the association between preserved muscle mass and milder disease severity.\u003c/p\u003e\u003cp\u003eThe findings from Awano et al. further corroborate our observations. In their study involving 199 IPF patients, muscle assessments were performed at analogous anatomical levels\u0026mdash;erector spinae at the lower margin of T12 and pectoralis at the axial slice above the aortic arch\u0026mdash;mirroring our methodology [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Both cross-sectional area (CSA) and mean attenuation (MA) were evaluated; however, muscle area values were not normalized to height or BMI, in contrast to our approach. Despite this, their results echoed ours: patients in the lowest quartile for erector spinae muscle CSA experienced significantly worse survival outcomes, whereas no such association was found for pectoralis muscle CSA. Notably, survival outcomes also did not differ among groups categorized by muscle attenuation, suggesting that muscle bulk may hold greater prognostic relevance than density. Both univariate and multivariate analyses in their study affirmed low erector spinae muscle CSA as an independent predictor of all-cause mortality, aligning closely with our findings.\u003c/p\u003e\u003cp\u003eSimilarly, Suzuki et al. explored the prognostic significance of erector spinae muscle metrics in a cohort comprising 131 IPF and 43 idiopathic pleuroparenchymal fibroelastosis (IPPFE) patients. Thoracic CT at the T12 level was employed to measure CSA and muscle density (ESD), with results benchmarked against a control population [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Congruent with our data, CSA values were significantly reduced in patients with interstitial lung disease. However, no significant differences were observed in muscle density (ESD) between patients and controls. Multivariate analysis in their study identified both ESA and FVC (%) as independent predictors of mortality, reinforcing the clinical importance of skeletal muscle evaluation in IPF. Parallel findings in our dataset demonstrated a positive correlation between ESA and several key functional indices, including FVC (L), FEV1 (L), diffusing capacity for carbon monoxide (DLCO), and total lung capacity (TLC, L).\u003c/p\u003e\u003cp\u003eFurther support is found in the work of Yannick et al., who assessed 164 patients with various forms of interstitial lung disease (including 76 IPF, 28 hypersensitivity pneumonitis, and 60 unclassifiable ILD) for sarcopenia using pectoralis muscle area (PMA) on thoracic CT [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Their longitudinal analysis aimed to determine whether PMA correlated with disease severity and long-term outcomes. They reported positive associations between PMA and FVC, DLCO, and oxygen saturation\u0026mdash;both at rest and during exercise. Correspondingly, our study also found a low but statistically significant positive correlation between PMA and both FVC and FEV1, thereby lending further credence to PMA as a general indicator of functional status. However, this correlation was less robust than those observed with erector spinae metrics.\u003c/p\u003e\u003cp\u003eDistinctively, our investigation is one of the few to systematically examine the relationship between CT-derived thoracic skeletal muscle indices and quantitative lung volume parameters. Our findings suggest that patients identified as sarcopenic by ESI exhibit significantly lower NALV (L), NAAV (%), and TLV compared to non-sarcopenic individuals. Conversely, sarcopenic patients showed significantly higher LALV (%) and HALV (%) values\u0026mdash;indicative of more extensive fibrotic transformation and reduced functional lung parenchyma. These findings underscore a compelling inverse relationship between the extent of lung involvement and skeletal muscle mass at the T12 level, suggesting that declining respiratory muscle integrity may parallel worsening disease.\u003c/p\u003e\u003cp\u003eBy contrast, PMI-based stratification failed to show statistically meaningful differences in lung volume measurements between sarcopenic and non-sarcopenic groups. This discrepancy prompts consideration of potential confounding variables. Technical factors such as variability in pectoralis muscle measurement level, inconsistencies due to shoulder and arm positioning, and the intrinsic function of pectoralis muscles as active respiratory components may contribute to their preserved mass even amidst progressive disease. These nuances may account for the lower discriminatory power of PMI relative to ESI.\u003c/p\u003e\u003cp\u003eTaken together, our findings position the Erector Spinae Index as a more reliable and clinically informative metric for evaluating sarcopenia and predicting disease progression in patients with idiopathic pulmonary fibrosis. ESI demonstrates superior correlation with functional and volumetric lung parameters, GAP staging, and survival outcomes when compared to PMI.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, the findings of our study demonstrate that, without additional radiation exposure, routinely acquired chest CT scans can be utilized to extract valuable prognostic information in patients with idiopathic pulmonary fibrosis (IPF). Specifically, the erector spinae muscle index (ESI)\u0026mdash;normalized to patient height\u0026mdash;and the threshold-based quantitative lung volumes (NALV% and HALV%), calculated using artificial intelligence\u0026ndash;based software, were found to be significant prognostic indicators of two-year survival. Among these, NALV (%) emerged as an independent predictor of all-cause mortality.\u003c/p\u003e\u003cp\u003eOur results revealed that IPF patients with low erector spinae muscle mass had significantly lower survival rates. Given that pulmonary function tests (PFTs) and DLCO measurements require a high level of patient cooperation, the use of CT-derived quantitative parameters\u0026mdash;such as ESI and NALV (%)\u0026mdash;may be especially beneficial in estimating prognosis and mortality risk in patients who are unable to perform or poorly tolerate functional tests.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to institutional data protection regulations, but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eEthics approval:\u003c/strong\u003e Ethical approval was obtained from the Clinical Research Ethics Committee of Ankara Bilkent City Hospital (Date: 11.10.2023, Decision No: E2-23-5201).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e As this was a retrospective study using anonymized clinical and imaging data, the requirement for informed consent was waived by the ethics committee.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConsent to publish:\u003c/strong\u003e Not applicable, as no individual person\u0026rsquo;s data, images, or identifying details are published.\u003c/li\u003e\n\u003c/ul\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions StatementM.K. and M.A.K. designed the study and conducted the CT image analysis. M.K. performed the statistical analysis and interpreted the data. E.Ş.Ş.P. contributed to clinical data acquisition and provided pulmonary medicine expertise. M.K. and M.A.K. wrote the main manuscript text. E.Ş.Ş.P. contributed to the review and editing of the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMartin MD, Chung JH, Kanne JP. Idiopathic pulmonary fibrosis. J Thorac Imaging. 2016;31(3):127\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChung JH, Cox CW, Montner SM, Adegunsoye A, Oldham JM, Husain AN, et al. CT features of the usual interstitial pneumonia pattern: differentiating connective tissue disease-associated interstitial lung disease from idiopathic pulmonary fibrosis. AJR Am J Roentgenol. 2018;210(2):307\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChung JH, Lynch DA. The value of a multidisciplinary approach to the diagnosis of usual interstitial pneumonitis and idiopathic pulmonary fibrosis: radiology, pathology, and clinical correlation. AJR Am J Roentgenol. 2016;206(3):463\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEpidemiologic. and methodologic problems in determining nutritional status of older persons. Proceedings of a conference. \u003cem\u003eAm J Clin Nutr.\u003c/em\u003e 1989;50(5 Suppl):1121\u0026ndash;235.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F, et al. Sarcopenia: European consensus on definition and diagnosis. Age Ageing. 2010;39(4):412\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAsakura T, Yamada Y, Suzuki S, Namkoong H, Okamori S, Kusumoto T, et al. Quantitative assessment of erector spinae muscles in patients with Mycobacterium avium complex lung disease. Respir Med. 2018;145:66\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOhkubo H, Taniguchi H, Kondoh Y, Yagi M, Furukawa T, Johkoh T, et al. A volumetric computed tomography analysis of the normal lung in idiopathic pulmonary fibrosis: the relationship with the survival. Intern Med. 2018;57(7):929\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBak SH, Kwon SO, Han SS, Kim WJ. Computed tomography-derived area and density of pectoralis muscle associated with disease severity and longitudinal changes in chronic obstructive pulmonary disease: a case control study. Respir Res. 2019;20(1):226.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShin KE, Chung MJ, Jung MP, Choe BK, Lee KS. Quantitative computed tomographic indexes in diffuse interstitial lung disease: correlation with physiologic tests and computed tomography visual scores. J Comput Assist Tomogr. 2011;35(2):266\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOhkubo H, Kanemitsu Y, Uemura T, Takakuwa O, Takemura M, Maeno K, et al. Normal lung quantification in usual interstitial pneumonia pattern: the impact of threshold-based volumetric CT analysis for the staging of idiopathic pulmonary fibrosis. PLoS ONE. 2016;11(3):e0152505.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoon SW, Choi JS, Lee SH, Jung KS, Jung JY, Kang YA, et al. Thoracic skeletal muscle quantification: low muscle mass is related with worse prognosis in idiopathic pulmonary fibrosis patients. Respir Res. 2019;20(1):35.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAwano N, Inomata M, Kuse N, Tone M, Yoshimura H, Jo T, et al. Quantitative computed tomography measures of skeletal muscle mass in patients with idiopathic pulmonary fibrosis according to a multidisciplinary discussion diagnosis: a retrospective nationwide study in Japan. Respir Investig. 2020;58(2):91\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSuzuki Y, Yoshimura K, Enomoto Y, Yasui H, Hozumi H, Karayama M, et al. Distinct profile and prognostic impact of body composition changes in idiopathic pulmonary fibrosis and idiopathic pleuroparenchymal fibroelastosis. Sci Rep. 2018;8(1):14074.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMolgat-Seon Y, Guler SA, Peters CM, Vasilescu DM, Puyat JH, Coxson HO, et al. Pectoralis muscle area and its association with indices of disease severity in interstitial lung disease. Respir Med. 2021;186:106539.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Idiopathic pulmonary fibrosis, Sarcopenia, Lung volume, Quantitative analysis, Computed tomography, Mortality","lastPublishedDoi":"10.21203/rs.3.rs-7436178/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7436178/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThe primary aim of this study is to evaluate the prognostic significance and impact on mortality of sarcopenia—assessed through thoracic skeletal muscles—and threshold-based quantitative lung volumetric analysis in patients with idiopathic pulmonary fibrosis (IPF).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods: \u003c/strong\u003ePatients who underwent non-contrast thoracic CT between January 2019 and August 2023 at Ankara Bilkent City Hospital and exhibited a usual interstitial pneumonia (UIP) pattern in the lung parenchyma were retrospectively reviewed. A total of 106 IPF patients meeting the inclusion criteria and 106 age- and sex-matched control subjects without any chronic lung disease were included in the study.\u003c/p\u003e\n\u003cp\u003eTo assess sarcopenia, the cross-sectional area (PMA) and density (PMD) of the pectoralis muscles were measured from the first axial slice above the aortic arch, while the cross-sectional area (ESA) and density (ESD) of the erector spinae muscles were measured from a single axial slice at the lower margin of the 12th thoracic vertebra. Using an artificial intelligence-based analysis software package (Thoracic VCAR, GE Healthcare), normal and fibrotic lung volumes were quantitatively measured. All CT measurements were compared between the patient and control groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe mean body mass index (BMI) of the patient group was significantly lower than that of the control group (p \u0026lt; 0.05). Additionally, quantitative lung volume measurements (Normal Attenuation Lung Volume - NALV [L and %], Low Attenuation Lung Volume - LALV [%], High Attenuation Lung Volume - HALV [L and %], total lung volume) and thoracic skeletal muscle measurements (ESA, ESI, PMA, PMI) were significantly lower in the patient group (p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003ePatients were grouped as sarcopenic or non-sarcopenic based on the distribution of their Erector Spinae Index (ESI) and Pectoralis Muscle Index (PMI). According to ESI, sarcopenic patients had significantly lower two-year survival rates, follow-up durations, normal attenuation lung volume (NALV in L and %), total lung volume, FVC (L), and FEV1 (L) compared to non-sarcopenic patients (p \u0026lt; 0.05). However, when classified by PMI, no statistically significant differences were observed between sarcopenic and non-sarcopenic groups in terms of two-year survival rates or quantitative lung volumes (p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eESI and NAAV (%) obtained through quantitative CT analysis are significant prognostic indicators for predicting two-year mortality in IPF patients.\u003c/p\u003e","manuscriptTitle":"Quantitative Evaluation of Thoracic Skeletal Muscle Mass and Normal/Fibrotic Lung Volumes on CT in Idiopathic Pulmonary Fibrosis Patients: Prognostic Significance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-26 13:25:29","doi":"10.21203/rs.3.rs-7436178/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"64e3b5e0-86ff-4ff5-a745-e1980ae1e120","owner":[],"postedDate":"October 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-20T15:40:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-26 13:25:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7436178","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7436178","identity":"rs-7436178","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}