Prognostic Impact of Preoperative Osteopenia in Elderly Patients with Lung Cancer

Prognostic Impact of Preoperative Osteopenia in Elderly Patients with Lung Cancer | 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 Prognostic Impact of Preoperative Osteopenia in Elderly Patients with Lung Cancer Shoji Kuriyama, Motoko Konno, Naoko Mori, Shinogu Takashima, Tsubasa Matsuo, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7742554/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Dec, 2025 Read the published version in World Journal of Surgical Oncology → Version 1 posted 16 You are reading this latest preprint version Abstract Background Osteopenia was recently reported to be a factor contributing to a poorer prognosis in various cancers. However, its prognostic impact on non-small cell lung cancer (NSCLC) patients remains unclear. In the present study, we focused on osteopenia in elderly NSCLC patients and investigated survival outcomes. Methods This study included 315 NSCLC patients aged 75 years or older who had undergone radical lobectomy or segmentectomy at our institution between 2010 and 2023. Osteopenia was evaluated based on the average pixel density within a circle in the mid-vertebral core at the 11th thoracic vertebra on preoperative computed tomography. Results Osteopenia was identified in 126 patients (40%). This osteopenia group had significantly poorer overall survival (OS) than the non-osteopenia group (5-year OS: 81.0% vs 71.4%, p = 0.026). Multivariable analysis revealed that male (p = 0.035), pathological Stage ≥ II (p < 0.001), and osteopenia (p = 0.011) were independent factors affecting OS. The cumulative incidence of non-lung cancer mortality was significantly higher in the osteopenia group than non-osteopenia group (5-year mortality rate: 16.9% vs 6.4%, p = 0.005). In multivariable analysis, the Charlson Comorbidity Index (CCI) ≥ 2 (p = 0.006), sarcopenia (p = 0.044), and osteopenia (p = 0.008) were independent factors affecting non-lung cancer mortality. Conclusions Elderly patients with osteopenia have significantly poorer OS and greater non-lung cancer mortality. Screening for osteopenia may assist in developing appropriate treatment strategies for high-risk patients. Non-small cell lung cancer Computed tomography Osteopenia Overall survival Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Lung cancer remains a leading cause of cancer-related mortality ( 1 ). The most curative treatment for early-stage non-small cell lung cancer (NSCLC) is surgical resection ( 2 ), and there is an increasing number of elderly patients undergoing surgery for NSCLC ( 3 , 4 ). This places these patients at a high risk of decreased physical capacity and increased comorbidity. For that reason, thoracic surgeons must evaluate potential postoperative risk when determining appropriate treatment strategies for elderly patients. Previous studies have shown that for cancer patients who have undergone a resection, malnutrition significantly increases the incidence of postoperative complications, adversely affects the efficacy of anticancer treatments, leads to longer hospital stays, impairs quality of life, accelerates tumor progression, and reduces survival rates ( 5 – 7 ). Indicators of immune-nutrition status such as the prognostic nutritional index (PNI) ( 5 ), controlling nutritional status (CONUT) score ( 6 ), and the presence of sarcopenia ( 7 ) have been shown to be predictive of prognosis after lung cancer surgery. Osteopenia, defined as a decrease in bone mineral density (BMD), is a common disorder in the elderly ( 8 ). The World Health Organization (WHO) primarily defines osteopenia using the T-score, which quantifies BMD relative to a young adult reference population, and a T-score between − 1.0 and − 2.5 is defined as osteopenia ( 9 ). Osteopenia is associated with physical inactivity and nutritional deficiencies, particularly a lack of vitamin D and calcium ( 10 ). BMD is typically assessed using dual-energy X-ray absorptiometry (DXA) ( 9 ). However, BMD derived from the mean pixel density of vertebral trabeculae using computed tomography (CT) was recently shown to correlate with DXA ( 11 ). Osteopenia is reportedly a prognostic factor in various cancers ( 12 – 19 ), though the impact of preoperative osteopenia on the prognosis of lung cancer patients remains unclear. In the present study, we aimed to investigate the relationship between osteopenia assessed using preoperative CT scans and postoperative prognosis in elderly lung cancer patients undergoing radical surgery, to clarify the impact of osteopenia on postoperative outcome and to develop a novel criterion to assist in determining appropriate treatment strategy. Materials and Methods Patients and surgical procedures This retrospective study was reviewed and approved by the institutional review board (IRB) at Akita University Hospital [approval number: 2679, date of June 28, 2021], and all medical records were collected under the IRB-approved protocol. The need for individual patient consent was waived because all patients’ data were anonymized. Among 331 patients aged 75 years or older who had undergone radical lobectomy or segmentectomy for NSCLC between February 2010 and March 2023 at Akita University Hospital, 315 were enrolled after excluding two patients without preoperative CT images, one patient who underwent lumbar fusion surgery for lumbar spondylolisthesis, and 13 patients who received additional resection of other lobes or segments. A diagram illustrating the process by which patients were selected for study is shown in Fig. 1 . All patients received standard preoperative and intraoperative care. The surgical procedure was determined based on the patients’ tolerance and tumor characteristics. Postoperative complications were defined as all complications of Clavien-Dindo grade II or higher that occurred within 30 days after surgery, including respiratory and cardiovascular events ( 20 ). Definition of osteopenia and sarcopenia Before surgery, all enrolled patients received preoperative chest CT scans (Discovery CT750 HD or Revolution CT, GE Healthcare) while they were in a supine position at end-inspiration. In the axial CT images, BMD was calculated as the average pixel density within a circle at the midvertebral core at the bottom of the 11th thoracic vertebra (Fig. 2 ). Osteopenia was defined based on CT attenuation below standard values calculated using the following previously described formulas: male = [308.82–2.49 × age]; female = [311.84–2.41 × age] ( 21 ). Sarcopenia was defined as a decrease in the psoas muscle index, calculated as the cross-sectional area of the psoas muscle at the third lumbar level divided by the square of the height. Cutoff values were established as gender-specific values (6.36 cm 2 /m 2 for males and 3.92 cm 2 /m 2 for females), as described previously ( 22 ). Immune-nutrition markers The PNI and neutrophil-to-lymphocyte ratio (NLR) served as immune-nutrition markers. The PNI was calculated as 10 × albumin (g/dL) + 0.005 × lymphocyte count /µl ( 23 ). NLR was calculated as the number of neutrophils divided by the number of lymphocytes ( 24 ). Statistical analysis Group data are expressed as the medians with ranges (continuous variables) or the number of patients with percentages (categorical variables). Missing data, if present, were handled using complete case analysis. Kaplan-Meier analysis was used to estimate 5-year overall survival (OS) and relapse-free survival (RFS) with survival time measured from the date of treatment initiation to the event or last follow-up. Censoring was applied for patients lost to follow-up. Survival curves were compared using the log-rank test. Univariable and multivariable analyses were performed using the Cox proportional hazards regression model for OS. Variables in the Cox proportional hazards regression model were selected using a stepwise method. Cumulative incidence curves for lung cancer-specific deaths and non-lung cancer deaths were compared using the Gray test, considering competing risks. Univariable and multivariable analyses for non-lung cancer deaths were performed using the Fine-Gray competing risk model. Variables in the Fine-Gray model were selected using a stepwise method. The proportional subdistribution hazards assumption was verified. All p-values were two-sided, with statistical significance defined as p < 0.05. Statistical analyses were performed using EZR (version 1.68), a graphical user interface for R (The R Foundation for Statistical Computing) ( 25 ), with additional analyses conducted using the survival and cmprsk R packages. Results The patients were initially divided into an osteopenia group (n = 126) and a non-osteopenia group (n = 189). The patients’ characteristics are listed in Table 1 . The osteopenia group exhibited significantly lower BMD values than the non-osteopenia group, and there was a significantly higher proportion of males in the non-osteopenia group (p = 0.007). No other significant differences in baseline characteristics were observed between the two groups. Figure 2 illustrates Kaplan-Meier curves comparing OS (Fig. 3 A) and RFS (Fig. 3 B) between the two groups. OS was significantly shorter in the osteopenia group (5-year OS: 81.0% vs 71.4%, p = 0.026). The 5-year RFS rate was lower in the osteopenia group (71.8% vs 60.7%, p = 0.052). Table 2 presents the prognostic factors for OS, analyzed using the Cox proportional hazards model. Univariable analysis identified several significant variables contributing to a poorer prognosis. These included gender, a Charlson Comorbidity Index (CCI) ≥ 2, PNI < 45, sarcopenia, pathological Stage ≥ II, and osteopenia. The multivariable analysis revealed that male (p = 0.035), pathological Stage ≥ II (p < 0.001), and osteopenia (p = 0.011) were independent predictors of a poor prognosis. Table 3 summarizes the causes of death among all patients. More patients in the osteopenia group died from non-malignant diseases than in the non-osteopenia group (11.1% vs 1.6%). Figure 3 shows the cumulative incidence curves for non-lung cancer mortality (Fig. 4 A) and lung cancer-specific deaths (Fig. 4 B). While there was no significant difference in lung cancer deaths between the two groups (p = 0.917), the incidence of non-lung cancer deaths was significantly higher in the osteopenia group than in the non-osteopenia group (p = 0.005). Table 4 summarizes the prognostic factors for non-lung cancer death, analyzed using the Fine-Gray competing risk model. Both univariable and multivariable analyses identified CCI ≥ 2 (p = 0.006), sarcopenia (p = 0.044), and osteopenia (p = 0.008) as independent factors. Table 1 Patient characteristics Clinical characteristics Osteopenia (N = 126) Non-Osteopenia (N = 189) p Age (years) 78 (75–86) 78 (75–87) 0.716 Sex (Male / Female) 64 / 62 125 / 64 0.007* PS (0 / 1 / 2) 108 / 18 / 0 145 / 42 / 2 0.101 BMI (kg/m 2 ) 22.6 (15.7–32.7) 22.3 (11.5–32.5) 0.953 Serum albumin (g/dl) 4.2 (3.3–4.9) 4.2 (3.0-6.7) 0.783 PNI 49.3 (38.5–62.6) 49.6 (37.5–70.5) 0.785 NLR 2.3 (0.8–49.1) 2.4 (0.5–14.7) 0.502 BI 125 (0-3000) 400 (0-4000) 0.698 CCI 1 (0–7) 1 (0–8) 0.304 Sarcopenia 102 (81.0%) 142 (75.1%) 0.226 BMD (HU) 93 (-54-127) 155 (109–414) < 0.001* Tumor size (cm) 2.5 (0.6–10.0) 2.4 (0.3–12.0) 0.876 Tumor subtype (Ad. / Sq. / others) 93 / 31 / 1 145 / 37 / 6 0.235 pStage (0 / I / II / III) 4 / 99 / 21 / 2 11 / 135 / 29 / 14 0.080 Procedures (Lob. / Seg.) 91 / 35 147 / 42 0.261 Postoperative complication (CD Grade ≥ 2) 35 (27.8%) 44 (23.3%) 0.367 Adjuvant chemotherapy 17 (13.5%) 33 (17.5%) 0.345 *significant difference; PS, performance status; BMI, body mass index; PNI, prognostic nutritional index; NLR, neutrophil-to-lymphocyte ratio; BI, Brinkman index; CCI, Charlson comorbidity index; BMD, bone mineral density; HU, Hounsfield units; CD, Clavien-Dindo classification Table 2 Univariable and multivariable analysis for overall survival Univariable analysis Multivariable analysis HR 95% CI p HR 95% CI p Age (> 80) 1.099 0.649–1.862 0.725 − − − Gender (male) 2.011 1.187–3.408 0.009* 1.803 1.041–3.120 0.035* CCI (≥ 2) 1.714 1.067–2.752 0.026* 1.589 0.980–2.579 0.061 PNI (< 45) 1.860 1.088–3.181 0.023* − − − Sarcopenia 2.864 1.365–6.011 0.005* − − − pStage (≥ II) 4.084 2.561–6.511 < 0.001* 3.454 2.125–5.614 < 0.001* Osteopenia 1.695 1.061–2.709 0.027* 1.871 1.156–3.031 0.011* * significant difference, CCI: Charlson comorbidity index; PNI: prognostic nutritional index Table 3 Summary of causes of death Osteopenia (N = 126) Non-Osteopenia (N = 189) Total deaths 39 (31.0%) 33 (17.5%) Lung cancer death 19 (15.1%) 23 (12.2%) Other death 20 (15.9%) 10 (5.3%) Other cancer 3 (2.4%) 2 (1.1%) Non-malignant disease Respiratory disease 8 (6.3%) 3 (1.6%) Cerebrovascular disease 2 (1.6%) 0 (0%) Cardiovascular disease 2 (1.6%) 0 (0%) Other diseases 2 (1.6%) 0 (0%) Unknown 3 (2.4%) 4 (2.1%) Table 4 Univariable and multivariable analysis for non-lung cancer death Univariable analysis Multivariable analysis HR 95% CI p HR 95% CI p Age (> 80) 1.337 0.634–2.819 0.450 − − − Gender (male) 1.743 0.820–3.706 0.150 − − − CCI (≥ 2) 2.936 1.456–5.920 0.003* 2.805 1.344–5.853 0.006* PNI (< 45) 1.408 0.615–3.225 0.420 − − − Sarcopenia 4.922 1.208–20.06 0.026* 4.067 1.035–15.980 0.044* pStage (≥ II) 1.666 0.809–3.434 0.170 − − − Osteopenia 2.692 1.300–5.576 0.008* 2.782 1.314–5.888 0.008* * significant difference, CCI: Charlson comorbidity index; PNI: prognostic nutritional index Discussion The present study shows that preoperative osteopenia on CT scans is an independent risk factor for poorer OS in elderly NSCLC patients and is also significantly associated with non-lung cancer death. These findings may provide preliminary evidence that preoperative osteopenia is predictive of prognosis for elderly NSCLC patients undergoing surgery. While several immunonutritional markers have been shown to be associated with postoperative prognosis for lung cancer patients, the most reliable prognosis marker remains unclear. The present study focused on preoperative osteopenia as an indicator that reflects nutritional status and physical activity level. However, the mechanism by which osteopenia affects prognosis are still unknown. Osteopenia is commonly seen in elderly patients, and multiple factors, including malnutrition, weight loss, frailty, and deficiency of appropriate mechanical bone loading through exercise all contribute to bone loss ( 26 , 27 ). Additionally, bones and the immune system are closely related through various regulatory molecules, including several cytokines and chemokines, which form a cooperative control system known as the osteoimmune system ( 28 ). With increasing age, the functional capacity of the bone marrow declines due to changes in the hematopoietic stem cell environment and reduced osteoblast function. Additionally, bone loss reduces the available space for hematopoiesis. The aged bone marrow microenvironment increases the risk of various age-related diseases, leading to immune dysfunction ( 29 ). In the present study, recurrence rates in the osteopenia group did not differ from the non-osteopenia group; however, both OS and non-lung cancer deaths were significantly higher in the osteopenia group. In the present study, the overall mortality rate, particularly mortality from causes other than lung cancer, was higher in elderly patients with osteopenia. Osteopenia is potentially associated with malnutrition, reduced physical activity, and decreased immune function. These factors can lead to a reduced therapeutic intervention rate and to an increase in the incidence rate and severity of infectious diseases, resulting in a poorer prognosis. Although recurrence rates showed no significant differences, the Kaplan-Meier curve was similar to OS. Osteopenia may also be involved in the recurrence and progression of lung cancer through immune dysfunction. Sarcopenia is recognized as a factor contributing to a poor prognosis in various cancers, including lung cancer. Sarcopenia and osteopenia are both commonly seen in the elderly and occasionally coexist ( 30 ). The coexistence of these conditions, termed osteosarcopenia, is associated with poorer survival than either sarcopenia or osteopenia alone ( 31 ). Pereira et al. reported that the bone loss begins before the muscle loss, suggesting that osteopenia may be an early indicator of deconditioning preceding sarcopenia ( 32 ). Our multivariable analysis revealed osteopenia to be an independent factor affecting survival. Osteopenia may be a more sensitive indicator of a poor prognosis in lung cancer patients than sarcopenia. In the present study, we adapted CT attenuation values as an alternative to DXA for diagnosing osteopenia. Thoracic CT scans are mandatory for lung cancer patients, and BMD measurement using CT scans does not put an additional burden on these patients. However, the sites for measurement and the cutoff values have not been standardized. Pickhardt et al. ( 33 ) demonstrated that CT attenuation to less than 160 Hounsfield units (HU) within a region of interest at the trabecular bone of the L1 vertebra could be used to diagnose osteopenia with 90% sensitivity and that CT attenuation to less than 110 HU showed 90% specificity. Additionally, there were no significant differences in diagnostic performance with measurement sites from T12 to L5. Moreover, administration of intravenous contrast agents had a negligible effect on attenuation values in patients over 40 years ( 34 ). Our study adopted measurement methods and cutoff values commonly used in previous research on cancer prognosis ( 13 , 15 , 18 , 21 ). Specifically, CT attenuation values were measured within a circle at the bottom of T11. T11 is generally included in both routine thoracic and abdominal imaging areas, enabling this method to be applied to numerous patients. Moreover, the cutoff values for this method are adjusted based on age and gender. Osteopenia is more common in women and the elderly, and this criterion can enable more accurate identification. However, further large-scale trials will be required to establish optimal cutoff values. Elderly lung cancer patients with osteopenia should be considered for perioperative and/or postoperative therapeutic interventions aimed at improving postoperative outcomes. Supplementation with protein and vitamin D enhances BMD and helps prevent osteoporosis and fractures in the elderly ( 35 ). A recent meta-analysis demonstrated that increasing dietary calcium or taking calcium supplements can boost BMD in key areas such as the lumbar spine, total hip, femoral neck, and overall body, with a maximum increase of 1.85% ( 36 ). This suggests nutritional therapy with protein, vitamin D, and calcium, has the potential to prevent the progression of osteopenia. Additionally, physical activity plays a crucial role in maintaining bone strength and mass. Regular exercise contributes to the preservation of BMD and bone strength, preventing osteoporotic fractures in the elderly ( 37 ). Aerobic exercise is particularly effective in increasing markers of bone formation, such as procollagen type 1 N-terminal propeptide and osteoblasts ( 38 ). Further research is expected to elucidate the potential of nutritional therapy and sustained exercise interventions to improve prognosis in lung cancer patients with osteopenia. The present study has several limitations. First, it is a small-scale, retrospective study conducted at a single institution, and selection bias cannot be entirely eliminated. Second, the measurement of BMD was performed by one operator, so the possibility of measurement bias cannot be denied. Third, this study was limited to elderly patients aged 75 and over, and further research will be required to apply our results to patients of all ages. In summary, osteopenia determined based on preoperative CT scans in elderly lung cancer patients was associated with a poorer prognosis. Screening for osteopenia may contribute to the development of appropriate treatment strategies for high-risk patients. Abbreviations BMD Bone mineral density CCI Charlson comorbidity index CI Confidence interval CONUT Controlling nutritional status CT Computed tomography DXA Dual-energy X-ray absorptiometry HU Hounsfield units IRB Institutional review board NLR Neutrophil-to-lymphocyte ratio NSCLC Non-small cell lung cancer OS Overall survival PNI Prognostic nutritional index PS Performance status RFS Recurrence-free survival ROI Region of interest Declarations Statement of Ethics An opt-out informed consent protocol was used for the participant data analyzed in this research. This consent procedure was reviewed and approved by the institutional review board (IRB) at Akita University Hospital, approval number [2679], date of decision June 28, 2021. Conflict of Interest Statement The authors have no conflict of interest. Funding Sources No specific funding was disclosed. Author Contribution Shoji Kuriyama: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft. Motoko Konno: Investigation, Supervision, Validation, Naoko Mori: Investigation, Supervision, Validation, Shinogu Takashima: Investigation, Supervision, Validation. Tsubasa Matsuo: Investigation, Supervision, Validation. Hidenobu Iwai: Investigation, Supervision, Validation. Haruka Suzuki: Investigation, Supervision, Validation. Tatsuki Fujibayashi: Investigation, Supervision, Validation. Sumire Shibano: Investigation, Supervision, Validation. Akiyuki Wakita: Supervision, Validation. Yusuke Sato: Supervision, Validation. Kyoko Nomura: Formal analysis, Supervision, Validation. Yoshihiro Minamiya: Supervision, Validation. Kazuhiro Imai: Investigation, Supervision, Validation, Writing – review & editing. Acknowledgement We thank William Goldman for assistance with the English proofreading of the manuscript. 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Cite Share Download PDF Status: Published Journal Publication published 04 Dec, 2025 Read the published version in World Journal of Surgical Oncology → Version 1 posted Editorial decision: Revision requested 01 Nov, 2025 Reviews received at journal 30 Oct, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviews received at journal 27 Oct, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviews received at journal 25 Oct, 2025 Reviewers agreed at journal 25 Oct, 2025 Reviewers agreed at journal 24 Oct, 2025 Reviewers agreed at journal 24 Oct, 2025 Reviewers agreed at journal 24 Oct, 2025 Reviews received at journal 13 Oct, 2025 Reviewers agreed at journal 13 Oct, 2025 Reviewers invited by journal 08 Oct, 2025 Editor assigned by journal 02 Oct, 2025 Submission checks completed at journal 29 Sep, 2025 First submitted to journal 29 Sep, 2025 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. 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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-7742554","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531560669,"identity":"ad3319bf-d0ef-4fa3-9e9a-0a32c35d57d3","order_by":0,"name":"Shoji Kuriyama","email":"data:image/png;base64,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","orcid":"","institution":"Akita University Graduate School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Shoji","middleName":"","lastName":"Kuriyama","suffix":""},{"id":531560670,"identity":"c58b01ea-2151-48f9-8423-5bd86c5c44de","order_by":1,"name":"Motoko Konno","email":"","orcid":"","institution":"Akita University Graduate School of 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23:16:49","extension":"xml","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":108440,"visible":true,"origin":"","legend":"","description":"","filename":"ff2d0e805a7344d4ac81e5b53abc59e51structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7742554/v1/c9b81fb2d8882e15096954f3.xml"},{"id":94046857,"identity":"c2117956-8e40-4b77-952a-7432dd4f46eb","added_by":"auto","created_at":"2025-10-21 23:08:49","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":117923,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7742554/v1/f281e65f4f4efbce446b0d3f.html"},{"id":94046835,"identity":"4342d6ac-25fd-4e55-ad99-a8324bc9d6f1","added_by":"auto","created_at":"2025-10-21 23:08:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1307880,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFlow chart illustrating the subject enrollment protocol.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7742554/v1/416c61884a0c08c836ff19f3.png"},{"id":94048776,"identity":"60d99e85-fa13-42d9-af59-3934d4adcf40","added_by":"auto","created_at":"2025-10-21 23:24:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4077421,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eBone mineral density was measured as the average pixel density within a circle at the middle of the vertebral body at the level of the 11th thoracic vertebra on preoperative CT images (A: Osteopenia; B: Non-osteopenia).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7742554/v1/620a9c2d58e0c09117331961.png"},{"id":94047658,"identity":"5116e12b-7763-43fa-8818-95be786e521b","added_by":"auto","created_at":"2025-10-21 23:16:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4150709,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eKaplan-Meier curves comparing overall survival (A) and relapse-free survival (B) between patients with and without osteopenia.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7742554/v1/c839c6f10fbcb83c6d71d5f3.png"},{"id":94046840,"identity":"db86729f-b5f0-4a36-a54c-82a9c87805d9","added_by":"auto","created_at":"2025-10-21 23:08:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1851410,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCumulative incidence curves comparing rates of lung cancer death (A) and non-lung cancer death (B) between patients with and without osteopenia.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7742554/v1/41d557e09dcf9c76d1121700.png"},{"id":97723937,"identity":"9abfebe8-e06d-4f39-9e26-abf1bf6608ef","added_by":"auto","created_at":"2025-12-08 16:09:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15124700,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7742554/v1/66db27c0-47e6-409f-a76a-378acd11caa1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Impact of Preoperative Osteopenia in Elderly Patients with Lung Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer remains a leading cause of cancer-related mortality (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The most curative treatment for early-stage non-small cell lung cancer (NSCLC) is surgical resection (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), and there is an increasing number of elderly patients undergoing surgery for NSCLC (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). This places these patients at a high risk of decreased physical capacity and increased comorbidity. For that reason, thoracic surgeons must evaluate potential postoperative risk when determining appropriate treatment strategies for elderly patients.\u003c/p\u003e\u003cp\u003ePrevious studies have shown that for cancer patients who have undergone a resection, malnutrition significantly increases the incidence of postoperative complications, adversely affects the efficacy of anticancer treatments, leads to longer hospital stays, impairs quality of life, accelerates tumor progression, and reduces survival rates (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Indicators of immune-nutrition status such as the prognostic nutritional index (PNI) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), controlling nutritional status (CONUT) score (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), and the presence of sarcopenia (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) have been shown to be predictive of prognosis after lung cancer surgery.\u003c/p\u003e\u003cp\u003eOsteopenia, defined as a decrease in bone mineral density (BMD), is a common disorder in the elderly (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The World Health Organization (WHO) primarily defines osteopenia using the T-score, which quantifies BMD relative to a young adult reference population, and a T-score between \u0026minus;\u0026thinsp;1.0 and \u0026minus;\u0026thinsp;2.5 is defined as osteopenia (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Osteopenia is associated with physical inactivity and nutritional deficiencies, particularly a lack of vitamin D and calcium (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). BMD is typically assessed using dual-energy X-ray absorptiometry (DXA) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). However, BMD derived from the mean pixel density of vertebral trabeculae using computed tomography (CT) was recently shown to correlate with DXA (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Osteopenia is reportedly a prognostic factor in various cancers (\u003cspan additionalcitationids=\"CR13 CR14 CR15 CR16 CR17 CR18\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), though the impact of preoperative osteopenia on the prognosis of lung cancer patients remains unclear.\u003c/p\u003e\u003cp\u003eIn the present study, we aimed to investigate the relationship between osteopenia assessed using preoperative CT scans and postoperative prognosis in elderly lung cancer patients undergoing radical surgery, to clarify the impact of osteopenia on postoperative outcome and to develop a novel criterion to assist in determining appropriate treatment strategy.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatients and surgical procedures\u003c/h2\u003e\u003cp\u003e This retrospective study was reviewed and approved by the institutional review board (IRB) at Akita University Hospital [approval number: 2679, date of June 28, 2021], and all medical records were collected under the IRB-approved protocol. The need for individual patient consent was waived because all patients\u0026rsquo; data were anonymized. Among 331 patients aged 75 years or older who had undergone radical lobectomy or segmentectomy for NSCLC between February 2010 and March 2023 at Akita University Hospital, 315 were enrolled after excluding two patients without preoperative CT images, one patient who underwent lumbar fusion surgery for lumbar spondylolisthesis, and 13 patients who received additional resection of other lobes or segments. A diagram illustrating the process by which patients were selected for study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAll patients received standard preoperative and intraoperative care. The surgical procedure was determined based on the patients\u0026rsquo; tolerance and tumor characteristics. Postoperative complications were defined as all complications of Clavien-Dindo grade II or higher that occurred within 30 days after surgery, including respiratory and cardiovascular events (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDefinition of osteopenia and sarcopenia\u003c/h3\u003e\n\u003cp\u003eBefore surgery, all enrolled patients received preoperative chest CT scans (Discovery CT750 HD or Revolution CT, GE Healthcare) while they were in a supine position at end-inspiration. In the axial CT images, BMD was calculated as the average pixel density within a circle at the midvertebral core at the bottom of the 11th thoracic vertebra (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Osteopenia was defined based on CT attenuation below standard values calculated using the following previously described formulas: male = [308.82\u0026ndash;2.49 \u0026times; age]; female = [311.84\u0026ndash;2.41 \u0026times; age] (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSarcopenia was defined as a decrease in the psoas muscle index, calculated as the cross-sectional area of the psoas muscle at the third lumbar level divided by the square of the height. Cutoff values were established as gender-specific values (6.36 cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e for males and 3.92 cm\u003csup\u003e2\u003c/sup\u003e/m\u003csup\u003e2\u003c/sup\u003e for females), as described previously (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eImmune-nutrition markers\u003c/h3\u003e\n\u003cp\u003eThe PNI and neutrophil-to-lymphocyte ratio (NLR) served as immune-nutrition markers. The PNI was calculated as 10 \u0026times; albumin (g/dL)\u0026thinsp;+\u0026thinsp;0.005 \u0026times; lymphocyte count /\u0026micro;l (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). NLR was calculated as the number of neutrophils divided by the number of lymphocytes (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eGroup data are expressed as the medians with ranges (continuous variables) or the number of patients with percentages (categorical variables). Missing data, if present, were handled using complete case analysis. Kaplan-Meier analysis was used to estimate 5-year overall survival (OS) and relapse-free survival (RFS) with survival time measured from the date of treatment initiation to the event or last follow-up. Censoring was applied for patients lost to follow-up. Survival curves were compared using the log-rank test. Univariable and multivariable analyses were performed using the Cox proportional hazards regression model for OS. Variables in the Cox proportional hazards regression model were selected using a stepwise method. Cumulative incidence curves for lung cancer-specific deaths and non-lung cancer deaths were compared using the Gray test, considering competing risks. Univariable and multivariable analyses for non-lung cancer deaths were performed using the Fine-Gray competing risk model. Variables in the Fine-Gray model were selected using a stepwise method.\u003c/p\u003e\u003cp\u003eThe proportional subdistribution hazards assumption was verified. All p-values were two-sided, with statistical significance defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Statistical analyses were performed using EZR (version 1.68), a graphical user interface for R (The R Foundation for Statistical Computing) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), with additional analyses conducted using the survival and cmprsk R packages.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe patients were initially divided into an osteopenia group (n\u0026thinsp;=\u0026thinsp;126) and a non-osteopenia group (n\u0026thinsp;=\u0026thinsp;189). The patients\u0026rsquo; characteristics are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The osteopenia group exhibited significantly lower BMD values than the non-osteopenia group, and there was a significantly higher proportion of males in the non-osteopenia group (p\u0026thinsp;=\u0026thinsp;0.007). No other significant differences in baseline characteristics were observed between the two groups. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates Kaplan-Meier curves comparing OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and RFS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) between the two groups. OS was significantly shorter in the osteopenia group (5-year OS: 81.0% vs 71.4%, p\u0026thinsp;=\u0026thinsp;0.026). The 5-year RFS rate was lower in the osteopenia group (71.8% vs 60.7%, p\u0026thinsp;=\u0026thinsp;0.052). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the prognostic factors for OS, analyzed using the Cox proportional hazards model. Univariable analysis identified several significant variables contributing to a poorer prognosis. These included gender, a Charlson Comorbidity Index (CCI)\u0026thinsp;\u0026ge;\u0026thinsp;2, PNI\u0026thinsp;\u0026lt;\u0026thinsp;45, sarcopenia, pathological Stage\u0026thinsp;\u0026ge;\u0026thinsp;II, and osteopenia. The multivariable analysis revealed that male (p\u0026thinsp;=\u0026thinsp;0.035), pathological Stage\u0026thinsp;\u0026ge;\u0026thinsp;II (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and osteopenia (p\u0026thinsp;=\u0026thinsp;0.011) were independent predictors of a poor prognosis. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the causes of death among all patients. More patients in the osteopenia group died from non-malignant diseases than in the non-osteopenia group (11.1% vs 1.6%). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the cumulative incidence curves for non-lung cancer mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) and lung cancer-specific deaths (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). While there was no significant difference in lung cancer deaths between the two groups (p\u0026thinsp;=\u0026thinsp;0.917), the incidence of non-lung cancer deaths was significantly higher in the osteopenia group than in the non-osteopenia group (p\u0026thinsp;=\u0026thinsp;0.005). Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the prognostic factors for non-lung cancer death, analyzed using the Fine-Gray competing risk model. Both univariable and multivariable analyses identified CCI\u0026thinsp;\u0026ge;\u0026thinsp;2 (p\u0026thinsp;=\u0026thinsp;0.006), sarcopenia (p\u0026thinsp;=\u0026thinsp;0.044), and osteopenia (p\u0026thinsp;=\u0026thinsp;0.008) as independent factors.\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 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient characteristics\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinical characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOsteopenia\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;126)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Osteopenia\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;189)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\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\u003eAge (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78 (75\u0026ndash;86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78 (75\u0026ndash;87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.716\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex (Male / Female)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e64 / 62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125 / 64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePS (0 / 1 / 2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e108 / 18 / 0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e145 / 42 / 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI (kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.6 (15.7\u0026ndash;32.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.3 (11.5\u0026ndash;32.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.953\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSerum albumin (g/dl)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.2 (3.3\u0026ndash;4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.2 (3.0-6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.783\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePNI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.3 (38.5\u0026ndash;62.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.6 (37.5\u0026ndash;70.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.785\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNLR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.3 (0.8\u0026ndash;49.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4 (0.5\u0026ndash;14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125 (0-3000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e400 (0-4000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.698\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCCI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0\u0026ndash;7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0\u0026ndash;8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.304\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSarcopenia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102 (81.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e142 (75.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.226\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMD (HU)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (-54-127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e155 (109\u0026ndash;414)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\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\u003e\u003cb\u003eTumor size (cm)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.5 (0.6\u0026ndash;10.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4 (0.3\u0026ndash;12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.876\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTumor subtype (Ad. / Sq. / others)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 / 31 / 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e145 / 37 / 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003epStage (0 / I / II / III)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 / 99 / 21 / 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 / 135 / 29 / 14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProcedures (Lob. / Seg.)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91 / 35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e147 / 42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePostoperative complication\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(CD Grade\u0026thinsp;\u0026ge;\u0026thinsp;2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35 (27.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e44 (23.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.367\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAdjuvant chemotherapy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (13.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (17.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.345\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e*significant difference; PS, performance status; BMI, body mass index; PNI, prognostic nutritional index; NLR, neutrophil-to-lymphocyte ratio; BI, Brinkman index; CCI, Charlson comorbidity index; BMD, bone mineral density; HU, Hounsfield units; CD, Clavien-Dindo classification\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\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 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariable and multivariable analysis for overall survival\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnivariable analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eMultivariable analysis\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\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\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep\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\u003eAge (\u0026gt;\u0026thinsp;80)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.649\u0026ndash;1.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender (male)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.187\u0026ndash;3.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.009*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.041\u0026ndash;3.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.035*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCCI (\u0026ge;\u0026thinsp;2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.067\u0026ndash;2.752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.026*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.980\u0026ndash;2.579\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePNI (\u0026lt;\u0026thinsp;45)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.088\u0026ndash;3.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.023*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSarcopenia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.365\u0026ndash;6.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.005*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003epStage (\u0026ge;\u0026thinsp;II)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.561\u0026ndash;6.511\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.125\u0026ndash;5.614\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\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\u003e\u003cb\u003eOsteopenia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.061\u0026ndash;2.709\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.027*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.871\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.156\u0026ndash;3.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.011*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e* significant difference, CCI: Charlson comorbidity index; PNI: prognostic nutritional index\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 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of causes of death\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOsteopenia\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;126)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-Osteopenia (N\u0026thinsp;=\u0026thinsp;189)\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\u003eTotal deaths\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39 (31.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (17.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLung cancer death\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19 (15.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (12.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOther death\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20 (15.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (5.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOther cancer\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNon-malignant disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRespiratory disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8 (6.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCerebrovascular disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCardiovascular disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOther diseases\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (2.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (2.1%)\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\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 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariable and multivariable analysis for non-lung cancer death\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnivariable analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eMultivariable analysis\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\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\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ep\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\u003eAge (\u0026gt;\u0026thinsp;80)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.634\u0026ndash;2.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender (male)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.820\u0026ndash;3.706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCCI (\u0026ge;\u0026thinsp;2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.456\u0026ndash;5.920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.344\u0026ndash;5.853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.006*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePNI (\u0026lt;\u0026thinsp;45)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.615\u0026ndash;3.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSarcopenia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.208\u0026ndash;20.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.026*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.035\u0026ndash;15.980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.044*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003epStage (\u0026ge;\u0026thinsp;II)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.809\u0026ndash;3.434\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOsteopenia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.300\u0026ndash;5.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.008*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.782\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.314\u0026ndash;5.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.008*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e* significant difference, CCI: Charlson comorbidity index; PNI: prognostic nutritional index\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study shows that preoperative osteopenia on CT scans is an independent risk factor for poorer OS in elderly NSCLC patients and is also significantly associated with non-lung cancer death. These findings may provide preliminary evidence that preoperative osteopenia is predictive of prognosis for elderly NSCLC patients undergoing surgery.\u003c/p\u003e\u003cp\u003eWhile several immunonutritional markers have been shown to be associated with postoperative prognosis for lung cancer patients, the most reliable prognosis marker remains unclear. The present study focused on preoperative osteopenia as an indicator that reflects nutritional status and physical activity level. However, the mechanism by which osteopenia affects prognosis are still unknown. Osteopenia is commonly seen in elderly patients, and multiple factors, including malnutrition, weight loss, frailty, and deficiency of appropriate mechanical bone loading through exercise all contribute to bone loss (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Additionally, bones and the immune system are closely related through various regulatory molecules, including several cytokines and chemokines, which form a cooperative control system known as the osteoimmune system (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). With increasing age, the functional capacity of the bone marrow declines due to changes in the hematopoietic stem cell environment and reduced osteoblast function. Additionally, bone loss reduces the available space for hematopoiesis. The aged bone marrow microenvironment increases the risk of various age-related diseases, leading to immune dysfunction (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In the present study, recurrence rates in the osteopenia group did not differ from the non-osteopenia group; however, both OS and non-lung cancer deaths were significantly higher in the osteopenia group. In the present study, the overall mortality rate, particularly mortality from causes other than lung cancer, was higher in elderly patients with osteopenia. Osteopenia is potentially associated with malnutrition, reduced physical activity, and decreased immune function. These factors can lead to a reduced therapeutic intervention rate and to an increase in the incidence rate and severity of infectious diseases, resulting in a poorer prognosis. Although recurrence rates showed no significant differences, the Kaplan-Meier curve was similar to OS. Osteopenia may also be involved in the recurrence and progression of lung cancer through immune dysfunction.\u003c/p\u003e\u003cp\u003eSarcopenia is recognized as a factor contributing to a poor prognosis in various cancers, including lung cancer. Sarcopenia and osteopenia are both commonly seen in the elderly and occasionally coexist (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The coexistence of these conditions, termed osteosarcopenia, is associated with poorer survival than either sarcopenia or osteopenia alone (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Pereira et al. reported that the bone loss begins before the muscle loss, suggesting that osteopenia may be an early indicator of deconditioning preceding sarcopenia (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Our multivariable analysis revealed osteopenia to be an independent factor affecting survival. Osteopenia may be a more sensitive indicator of a poor prognosis in lung cancer patients than sarcopenia.\u003c/p\u003e\u003cp\u003eIn the present study, we adapted CT attenuation values as an alternative to DXA for diagnosing osteopenia. Thoracic CT scans are mandatory for lung cancer patients, and BMD measurement using CT scans does not put an additional burden on these patients. However, the sites for measurement and the cutoff values have not been standardized. Pickhardt et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) demonstrated that CT attenuation to less than 160 Hounsfield units (HU) within a region of interest at the trabecular bone of the L1 vertebra could be used to diagnose osteopenia with 90% sensitivity and that CT attenuation to less than 110 HU showed 90% specificity. Additionally, there were no significant differences in diagnostic performance with measurement sites from T12 to L5. Moreover, administration of intravenous contrast agents had a negligible effect on attenuation values in patients over 40 years (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Our study adopted measurement methods and cutoff values commonly used in previous research on cancer prognosis (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Specifically, CT attenuation values were measured within a circle at the bottom of T11. T11 is generally included in both routine thoracic and abdominal imaging areas, enabling this method to be applied to numerous patients. Moreover, the cutoff values for this method are adjusted based on age and gender. Osteopenia is more common in women and the elderly, and this criterion can enable more accurate identification. However, further large-scale trials will be required to establish optimal cutoff values.\u003c/p\u003e\u003cp\u003eElderly lung cancer patients with osteopenia should be considered for perioperative and/or postoperative therapeutic interventions aimed at improving postoperative outcomes. Supplementation with protein and vitamin D enhances BMD and helps prevent osteoporosis and fractures in the elderly (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). A recent meta-analysis demonstrated that increasing dietary calcium or taking calcium supplements can boost BMD in key areas such as the lumbar spine, total hip, femoral neck, and overall body, with a maximum increase of 1.85% (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). This suggests nutritional therapy with protein, vitamin D, and calcium, has the potential to prevent the progression of osteopenia. Additionally, physical activity plays a crucial role in maintaining bone strength and mass. Regular exercise contributes to the preservation of BMD and bone strength, preventing osteoporotic fractures in the elderly (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Aerobic exercise is particularly effective in increasing markers of bone formation, such as procollagen type 1 N-terminal propeptide and osteoblasts (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Further research is expected to elucidate the potential of nutritional therapy and sustained exercise interventions to improve prognosis in lung cancer patients with osteopenia.\u003c/p\u003e\u003cp\u003eThe present study has several limitations. First, it is a small-scale, retrospective study conducted at a single institution, and selection bias cannot be entirely eliminated. Second, the measurement of BMD was performed by one operator, so the possibility of measurement bias cannot be denied. Third, this study was limited to elderly patients aged 75 and over, and further research will be required to apply our results to patients of all ages.\u003c/p\u003e\u003cp\u003eIn summary, osteopenia determined based on preoperative CT scans in elderly lung cancer patients was associated with a poorer prognosis. Screening for osteopenia may contribute to the development of appropriate treatment strategies for high-risk patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMD Bone mineral density\u003c/p\u003e\u003cp\u003eCCI Charlson comorbidity index\u003c/p\u003e\u003cp\u003eCI Confidence interval\u003c/p\u003e\u003cp\u003eCONUT Controlling nutritional status\u003c/p\u003e\u003cp\u003eCT Computed tomography\u003c/p\u003e\u003cp\u003eDXA Dual-energy X-ray absorptiometry\u003c/p\u003e\u003cp\u003eHU Hounsfield units\u003c/p\u003e\u003cp\u003eIRB Institutional review board\u003c/p\u003e\u003cp\u003eNLR Neutrophil-to-lymphocyte ratio\u003c/p\u003e\u003cp\u003eNSCLC Non-small cell lung cancer\u003c/p\u003e\u003cp\u003eOS Overall survival\u003c/p\u003e\u003cp\u003ePNI Prognostic nutritional index\u003c/p\u003e\u003cp\u003ePS Performance status\u003c/p\u003e\u003cp\u003eRFS Recurrence-free survival\u003c/p\u003e\u003cp\u003eROI Region of interest\u003c/p\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cb\u003eStatement of Ethics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAn opt-out informed consent protocol was used for the participant data analyzed in this research. This consent procedure was reviewed and approved by the institutional review board (IRB) at Akita University Hospital, approval number [2679], date of decision June 28, 2021.\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConflict of Interest Statement\u003c/h2\u003e\u003cp\u003eThe authors have no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding Sources\u003c/h2\u003e\u003cp\u003eNo specific funding was disclosed.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eShoji Kuriyama: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing \u0026ndash; original draft. Motoko Konno: Investigation, Supervision, Validation, Naoko Mori: Investigation, Supervision, Validation, Shinogu Takashima: Investigation, Supervision, Validation. Tsubasa Matsuo: Investigation, Supervision, Validation. Hidenobu Iwai: Investigation, Supervision, Validation. Haruka Suzuki: Investigation, Supervision, Validation. Tatsuki Fujibayashi: Investigation, Supervision, Validation. Sumire Shibano: Investigation, Supervision, Validation. Akiyuki Wakita: Supervision, Validation. Yusuke Sato: Supervision, Validation. Kyoko Nomura: Formal analysis, Supervision, Validation. Yoshihiro Minamiya: Supervision, Validation. Kazuhiro Imai: Investigation, Supervision, Validation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank William Goldman for assistance with the English proofreading of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting this study's findings are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGinsberg RJ, Rubinstein LV. Randomized trial of lobectomy versus limited resection for T1 N0 non-small cell lung cancer. Lung Cancer Study Group. Ann Thorac Surg. 1995 Sept;60(3):615\u0026ndash;22. discussion 622-3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCommittee for Scientific Affairs, The Japanese Association for Thoracic Surgery, Shimizu H, Okada M, Tangoku A, Doki Y, Endo S et al. Thoracic and cardiovascular surgeries in Japan during 2017: Annual report by the Japanese Association for Thoracic Surgery. Gen Thorac Cardiovasc Surg. 2020;68(4):414\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePark S, Park IK, Kim ER, Hwang Y, Lee HJ, Kang CH, et al. 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World J Surg. 2021;45(10):3119\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKamada T, Furukawa K, Takahashi J, Nakashima K, Nakaseko Y, Suzuki N, et al. Prognostic significance of osteopenia in patients with colorectal cancer: A retrospective cohort study. Ann Gastroenterol Surg. 2021;5(6):832\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWatanabe J, Saitsu A, Miki A, Kotani K, Sata N. Prognostic value of preoperative low bone mineral density in patients with digestive cancers: a systematic review and meta-analysis. Arch Osteoporos. 2022;17(1):33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWatanabe J, Miki A, Sakuma Y, Shimodaira K, Aoki Y, Meguro Y, et al. Preoperative osteopenia is associated with significantly shorter survival in patients with perihilar cholangiocarcinoma. Cancers (Basel). 2022;14(9):2213.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFukushima N, Tsuboi K, Nyumura Y, Hoshino M, Masuda T, Suzuki T, et al. Prognostic significance of preoperative osteopenia on outcomes after gastrectomy for gastric cancer. Ann Gastroenterol Surg. 2023;7(2):255\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOmori S, Ijichi H, Wakasugi A, Shigechi T, Oki E, Kubo M, et al. Association between preoperative osteopenia and prognosis in breast cancer patients. Anticancer Res. 2024 June;44(6):2671\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClavien PA, Barkun J, de Oliveira ML, Vauthey JN, Dindo D, Schulick RD, et al. The Clavien-Dindo classification of surgical complications: five-year experience. Ann Surg. 2009;250(2):187\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eToshima T, Yoshizumi T, Ikegami T, Harada N, Itoh S, Mano Y, et al. 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Ageing-related bone and immunity changes: insights into the complex interplay between the skeleton and the immune system. Bone Res. 2024;12(1):42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuo YR, Suriyaarachchi P, Gomez F, Curcio CL, Boersma D, Muir SW, et al. Phenotype of osteosarcopenia in older individuals with a history of falling. J Am Med Dir Assoc. 2015;16(4):290\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaintin J, Cooper C, Dennison E, Osteosarcopenia. Br J Hosp Med (Lond). 2018;79(5):253\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePereira FB, Leite AF, de Paula AP. Relationship between pre-sarcopenia, sarcopenia and bone mineral density in elderly men. Arch Endocrinol Metab. 2015;59(1):59\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePickhardt PJ, Pooler BD, Lauder T, del Rio AM, Bruce RJ, Binkley N. Opportunistic screening for osteoporosis using abdominal computed tomography scans obtained for other indications. Ann Intern Med. 2013;158(8):588\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJang S, Graffy PM, Ziemlewicz TJ, Lee SJ, Summers RM, Pickhardt PJ. Opportunistic osteoporosis screening at routine abdominal and thoracic CT: Normative L1 trabecular attenuation values in more than 20 000 adults. Radiology. 2019;291(2):360\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHill TR, Aspray TJ. The role of vitamin D in maintaining bone health in older people. Ther Adv Musculoskelet Dis. 2017;9(4):89\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTai V, Leung W, Grey A, Reid IR, Bolland MJ. Calcium intake and bone mineral density: systematic review and meta-analysis. BMJ. 2015 Sept;29:351:h4183.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAckerman KE, Misra M. Bone health and the female athlete triad in adolescent athletes. Phys Sportsmed. 2011;39(1):131\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePasqualini L, Ministrini S, Lombardini R, Bagaglia F, Paltriccia R, Pippi R, et al. Effects of a 3-month weight-bearing and resistance exercise training on circulating osteogenic cells and bone formation markers in postmenopausal women with low bone mass. Osteoporos Int. 2019;30(4):797\u0026ndash;806.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-surgical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjso","sideBox":"Learn more about [World Journal of Surgical Oncology](http://wjso.biomedcentral.com)","snPcode":"12957","submissionUrl":"https://submission.nature.com/new-submission/12957/3","title":"World Journal of Surgical Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Non-small cell lung cancer, Computed tomography, Osteopenia, Overall survival","lastPublishedDoi":"10.21203/rs.3.rs-7742554/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7742554/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eOsteopenia was recently reported to be a factor contributing to a poorer prognosis in various cancers. However, its prognostic impact on non-small cell lung cancer (NSCLC) patients remains unclear. In the present study, we focused on osteopenia in elderly NSCLC patients and investigated survival outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis study included 315 NSCLC patients aged 75 years or older who had undergone radical lobectomy or segmentectomy at our institution between 2010 and 2023. Osteopenia was evaluated based on the average pixel density within a circle in the mid-vertebral core at the 11th thoracic vertebra on preoperative computed tomography.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOsteopenia was identified in 126 patients (40%). This osteopenia group had significantly poorer overall survival (OS) than the non-osteopenia group (5-year OS: 81.0% vs 71.4%, p\u0026thinsp;=\u0026thinsp;0.026). Multivariable analysis revealed that male (p\u0026thinsp;=\u0026thinsp;0.035), pathological Stage\u0026thinsp;\u0026ge;\u0026thinsp;II (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and osteopenia (p\u0026thinsp;=\u0026thinsp;0.011) were independent factors affecting OS. The cumulative incidence of non-lung cancer mortality was significantly higher in the osteopenia group than non-osteopenia group (5-year mortality rate: 16.9% vs 6.4%, p\u0026thinsp;=\u0026thinsp;0.005). In multivariable analysis, the Charlson Comorbidity Index (CCI)\u0026thinsp;\u0026ge;\u0026thinsp;2 (p\u0026thinsp;=\u0026thinsp;0.006), sarcopenia (p\u0026thinsp;=\u0026thinsp;0.044), and osteopenia (p\u0026thinsp;=\u0026thinsp;0.008) were independent factors affecting non-lung cancer mortality.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eElderly patients with osteopenia have significantly poorer OS and greater non-lung cancer mortality. Screening for osteopenia may assist in developing appropriate treatment strategies for high-risk patients.\u003c/p\u003e","manuscriptTitle":"Prognostic Impact of Preoperative Osteopenia in Elderly Patients with Lung Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 23:08:43","doi":"10.21203/rs.3.rs-7742554/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-01T16:42:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-30T13:05:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62606856058455040481450136889665171008","date":"2025-10-29T00:25:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-27T22:40:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291944192274981977037079772043393302086","date":"2025-10-27T16:02:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-25T09:35:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139629948234120749478516125540334271978","date":"2025-10-25T06:58:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136338594783702743504607557941727684792","date":"2025-10-25T03:17:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112535925878800316775520379777748188983","date":"2025-10-25T00:11:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164556760785556522295701789942445305597","date":"2025-10-24T10:36:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-13T18:36:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229722887246054591892311559346937675725","date":"2025-10-13T16:27:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-08T09:17:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-02T10:37:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-29T23:13:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"World Journal of Surgical Oncology","date":"2025-09-29T13:18:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-surgical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjso","sideBox":"Learn more about [World Journal of Surgical Oncology](http://wjso.biomedcentral.com)","snPcode":"12957","submissionUrl":"https://submission.nature.com/new-submission/12957/3","title":"World Journal of Surgical Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e583457c-4bb1-4c78-b776-77aaaf1c3d57","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:03:06+00:00","versionOfRecord":{"articleIdentity":"rs-7742554","link":"https://doi.org/10.1186/s12957-025-04143-z","journal":{"identity":"world-journal-of-surgical-oncology","isVorOnly":false,"title":"World Journal of Surgical Oncology"},"publishedOn":"2025-12-04 15:58:04","publishedOnDateReadable":"December 4th, 2025"},"versionCreatedAt":"2025-10-21 23:08:43","video":"","vorDoi":"10.1186/s12957-025-04143-z","vorDoiUrl":"https://doi.org/10.1186/s12957-025-04143-z","workflowStages":[]},"version":"v1","identity":"rs-7742554","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7742554","identity":"rs-7742554","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}