Prognostic role of preoperative skeletal muscle mass index in surgical lung cancer patients: a systematic review with meta-analysis
Original Article

Prognostic role of preoperative skeletal muscle mass index in surgical lung cancer patients: a systematic review with meta-analysis

Xinqi Wei, Ying Zhang, Min Zhu, Yu Zhang, Yan Ma

Department of Cardiothoracic Surgery, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, Huai’an, China

Contributions: (I) Conception and design: X Wei, Y Ma; (II) Administrative support: All authors; (III) Provision of study materials or patients: X Wei, Ying Zhang, M Zhu; (IV) Collection and assembly of data: X Wei, Yu Zhang; (V) Data analysis and interpretation: All authors; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

Correspondence to: Yan Ma, MD. Department of Cardiothoracic Surgery, The Affiliated Huaian No. 1 People’s Hospital of Nanjing Medical University, No. 1 West Huanghe Road, Huai’an 223300, China. Email: 15061236388@163.com.

Background: The prognostic value of preoperative skeletal muscle mass index (SMI) in lung cancer patients who undergo the surgery remains unclear. This meta-analysis aimed to clarify prognostic value of preoperative SMI with long-term survival among operated lung cancer patients.

Methods: PubMed, Cochrane Library, and Web of Science databases were searched up to March 21, 2026. Endpoints for survival included the overall survival (OS), disease-free survival (DFS) and cancer-specific survival (CSS). Subgroup analyses stratified by the neoadjuvant therapy and tumor type for OS were further performed.

Results: Fourteen retrospective studies with 8,008 participants were included. After combining available data, it was identified that a lower SMI was associated with poorer OS [hazard ratio (HR) =1.35, 95% confidence interval (CI): 1.30–1.52, P<0.001] and DFS (HR =2.10, 95% CI: 1.06–4.15, P=0.03) and potentially poorer CSS (HR =1.64, 95% CI: 0.91–2.95, P=0.10). Subgroup analyses for the OS based on the neoadjuvant therapy (no: HR =1.90, P=0.003; yes: HR =2.50, P=0.03; mixed: HR =1.27, P=0.004) and tumor type (non-small cell lung cancer: HR =1.28, P<0.001; lung cancer: HR =2.03, P<0.001) manifested consistent results.

Conclusions: Lower preoperative SMI was associated with worse survival among surgical lung cancer patients and may have potential value in prognostic assessment.

Keywords: Skeletal muscle mass index (SMI); lung cancer; surgical; survival; meta-analysis

Submitted Apr 29, 2026. Accepted for publication Jun 18, 2026. Published online Jun 29, 2026.

doi: 10.21037/jtd-2026-1213

Highlight box

Key findings

• This meta-analysis included 14 retrospective studies with 8,008 surgical lung cancer patients.

• Low preoperative skeletal muscle mass index (SMI) was significantly associated with poorer overall survival (OS) [hazard ratio (HR) =1.35] and disease-free survival (DFS) (HR =2.10).

• A non-significant trend toward worse cancer-specific survival was observed (HR =1.64).

• Subgroup analyses by neoadjuvant therapy and tumor type showed consistent results.

• Sensitivity and trim-and-fill analyses supported the robustness of the findings.

What is known and what is new?

• Reduced skeletal muscle mass has been linked to poor outcomes in several malignancies, including lung cancer, but evidence in surgical populations has been inconsistent.

• This study provides an updated quantitative synthesis specifically evaluating preoperative SMI in resected lung cancer.

• Our findings confirm that low preoperative SMI is associated with inferior long-term survival, particularly OS and DFS.

What are the implications, and what should change now?

• Preoperative SMI may serve as a practical imaging-based biomarker for risk stratification before lung cancer surgery.

• Routine assessment of SMI could help identify high-risk patients.

• Patients with low SMI may benefit from targeted nutritional support and multimodal prehabilitation.

• Prospective studies are needed to validate whether improving muscle mass translates into better survival outcomes.

Introduction

Lung cancer remains the most common malignancies and the leading cause of cancer-related death worldwide. According to global cancer statistics, lung cancer accounted for nearly 2.5 million new cases and approximately 1.8 million deaths in 2022, highlighting its substantial global disease burden (1). For patients with resectable disease, surgery remains the cornerstone of curative-intent treatment and plays a central role in multimodality management, particularly in early-stage and selected locally advanced non-small cell lung cancer (NSCLC) (2,3). However, even after complete resection, the long-term prognosis of lung cancer patients is still unsatisfactory in a considerable proportion of cases because of postoperative recurrence, metastasis, and tumor heterogeneity. Recent evidence has shown that recurrence-free survival declines substantially with advancing stage even among surgically treated patients, indicating that surgery alone cannot fully eliminate the risk of disease progression (4,5). Therefore, identifying reliable preoperative prognostic indicators is of great clinical importance for risk stratification and perioperative decision-making in surgical lung cancer patients.

In recent years, increasing attention has been paid to the prognostic value of preoperative biomarkers in resected lung cancer, including systemic inflammatory, nutritional, and molecular indicators. Although several blood-based markers are convenient and easily accessible, their levels are often influenced by transient inflammation, infection, comorbidities, and laboratory variability, which may limit their stability and their ability to objectively reflect the patient’s actual physical condition (6,7). In contrast, body composition-related parameters, especially skeletal muscle mass, have been increasingly recognized as more objective indicators of host reserve and nutritional-metabolic status in patients with solid tumors. Skeletal muscle mass index (SMI), commonly calculated as appendicular skeletal muscle mass divided by height squared (kg/m2), is widely used to quantify muscle mass and evaluate sarcopenia. Accumulating evidence has demonstrated that reduced skeletal muscle mass is associated with unfavorable outcomes in several solid malignancies, and growing studies have also suggested its prognostic relevance in lung cancer (8-10). Nevertheless, the prognostic value of preoperative SMI for long-term survival in operated lung cancer has not yet been systematically clarified. Thus, we conducted this present meta-analysis to comprehensively evaluate the association between preoperative SMI and long-term survival outcomes in operated lung cancer. We present this article in accordance with the PRISMA reporting checklist (11) (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1213/rc).

Methods

Literature searching

PubMed, Cochrane Library, and Web of Science databases were searched from inception up to March 21, 2026 with following terms: skeletal muscle mass index, skeletal muscle index, SMI, lung, pulmonary, tumor, cancer, neoplasm, carcinoma, resection, surgery, surgical, operated, lobectomy, segmentectomy and wedge resection. The searching strategy for PubMed database was presented in Appendix 1.

Study selection

Studies meeting following criteria were included: (I) operated lung cancer patients; (II) SMI was calculated before the surgery according to the formula: skeletal muscle mass of the limbs (kg)/height2 (m2); (III) the association of preoperative SMI with survival including the overall survival (OS), disease-free survival (DFS), and cancer-specific survival (CSS) was explored; (IV) hazard ratios (HRs) with 95% confidence intervals (CIs) were reported; and (V) full texts were available.

Studies meeting following criteria were excluded: (I) letters, editorials, case reports, animal trials or conference abstract; and (II) insufficient, overlapped or duplicated data.

Data collection

Following data were collected from each included studies: the author, country of author, publication year, sample size, stage of tumor, neoadjuvant therapy, type of tumor, cutoff value of SMI, endpoint, HR, and 95% CI.

In this study, endpoints of survival consisted of the OS, DFS, and CSS.

Quality assessment

Quality was assessed by Newcastle-Ottawa Scale (NOS) score tool and studies with NOS score >5 were regarded as high-quality studies (12).

Statistical analysis

Statistical analyses were conducted by STATA 17.0 software. Heterogeneity between included studies was evaluated by I2 statistics. If significant heterogeneity was detected (I2>50% or P<0.10), the random-effects model was applied; otherwise, the fixed-effects model was applied. HRs and 95% CIs were combined to evaluate the association of SMI with OS, DFS, and CSS. Sensitivity analysis was conducted to detect the sources of heterogeneity and assess the stability of the overall results. Besides, Begg’s funnel plot and Egger’s test were performed to detect publication bias (13,14). If significant publication bias was detected, trim-and-fill method was further used (12).

Results

Literature searching and selection

According to the searching strategy designed, 521 publications were searched from three databases. Then 101 duplicated records were automatically excluded by the EndNote 21.0 software. After carefully reviewing the titles, abstracts and full texts of remains publications, 14 studies were eventually included in this meta-analysis (15-28) (Figure 1).

Figure 1 Flow diagram of this meta-analysis.

Basic characteristic of included literature

Fourteen studies were all retrospective involving of 8,008 participants. Sample sizes ranged from 106 to 2,712. Most studies focused on patients who did not receive the neoadjuvant therapy and NSCLC patients. All studies were high-quality studies. Detailed information was shown in Table 1.

Table 1

Basic characteristics of included studies

Author, year Country Sample size Tumor stage Neoadjuvant therapy Tumor type Cutoff value of SMI Endpoint NOS
Shoji, 2017 (15) Japan 147 TNM I No NSCLC 43.75 cm2/m2 for men, 41.10 cm2/m2 for women OS 6
Choi, 2021 (16) Korea 440 TNM I No NSCLC 55 cm2/m2 for men, 39 cm2/m2 for women OS 8
Tanaka, 2021 (17) Japan 587 TNM I–III Mixed NSCLC NR OS, DFS 8
Kaltenhauser, 2023 (18) Germany 280 TNM I–IV Mixed LC 28.8 cm2/m2 for men, 20.4 cm2/m2 for women OS, CSS 7
Ponholzer, 2023 (19) Austria 311 TNM I No LC 45.4 cm2/m2 for men, 34.4 cm2/m2 for women OS 8
Sato, 2023 (20) Japan 386 TNM I–II No NSCLC 34.6 cm2/m2 for men, 29.6 cm2/m2 for women OS, DFS 8
Tao, 2023 (21) China 750 TNM I–IV Mixed NSCLC 18.1 cm2/m2 for men, 14.7 cm2/m2 for women OS 8
Vedire, 2023 (22) USA 492 TNM I–III No NSCLC 52.8 cm2/m2 for men, 41.9 cm2/m2 for women OS, DFS 8
Watanabe, 2023 (23) Japan 106 TNM II–III Yes NSCLC NR OS, DFS 6
Huang, 2025 (24) China 2,712 TNM I–IV Mixed NSCLC 20.95 cm2/m2 OS 9
Mantz, 2025 (25) Germany 838 TNM I–IIB Mixed NSCLC Continuous OS, CSS 8
Uchibori, 2025 (26) Japan 300 TNM I–IIIA No NSCLC 43.1 cm2/m2 for men, 34.1 cm2/m2 for women OS 8
Khan, 2026 (27) USA 343 TNM I–IIB No NSCLC Continuous OS 8
Khan, 2026 (28) USA 316 TNM I No NSCLC NR DFS 8

CSS, cancer-specific survival; DFS, disease-free survival; LC, lung cancer; NOS, Newcastle-Ottawa Scale; NR, not reported; NSCLC, non-small cell lung cancer; OS, overall survival; SMI, skeletal muscle mass index; TNM, tumor-node-metastasis.

Association of preoperative SMI with OS among surgical lung cancer

Thirteen studies clarified the prognostic role of preoperative SMI for OS among lung cancer patients undergoing the surgery (15-27). Pooled results demonstrated the relationship between lower SMI and worse OS (HR =1.35, 95% CI: 1.20–1.52, P<0.001; I2=84.1%, P<0.001) (Figure 2). Then subgroup analyses based on the neoadjuvant therapy (no: HR =1.90, 95% CI: 1.25–2.88, P=0.003; yes: HR =2.50, 95% CI: 1.12–5.61, P=0.03; mixed: HR =1.27, 95% CI: 1.08–1.50, P=0.004) (Figure S1) and type of tumor (NSCLC: HR =1.28, 95% CI: 1.14–1.44, P<0.001; lung cancer: HR =2.03, 95% CI: 1.43–2.90, P<0.001) (Figure S2) clarified similar results (Table 2).

Figure 2 Association of preoperative SMI with OS among surgical lung cancer patients. CI, confidence interval; HR, hazard ratio; OS, overall survival; SMI, skeletal muscle mass index.

Table 2

Results of meta-analysis

Items Number of studies HR 95% CI P value† I2 (%) P value‡
OS 13 1.35 1.20–1.52 <0.001 84.1 <0.001
   Neoadjuvant therapy
    No 7 1.90 1.25–2.88 0.003 83.3 <0.001
    Yes 1 2.50 1.12–5.61 0.03 – –
    Mixed 5 1.27 1.08–1.50 0.004 67.1 0.02
   Tumor type
    NSCLC 11 1.28 1.14–1.44 <0.001 83.9 <0.001
    LC 2 2.03 1.43–2.90 <0.001 0.0 0.76
DFS 4 2.10 1.06–4.15 0.03 93.2 <0.001
CSS 3 1.64 0.91–2.95 0.10 79.5 0.008

†, for HR and 95% CI; ‡, for I2 (%). CI, confidence interval; CSS, cancer-specific survival; DFS, disease-free survival; HR, hazard ratio; LC, lung cancer; NSCLC, non-small cell lung cancer; OS, overall survival.

Association of preoperative SMI with DFS among surgical lung cancer

Four studies explored prognostic role of preoperative SMI for DFS (17,22,23,28). The pooled analysis manifested that lower SMI was associated with poorer DFS among surgical lung cancer (HR =2.10, 95% CI: 1.06–4.15, P=0.03; I2=93.2%, P<0.001) (Figure 3, Table 2).

Figure 3 Association of preoperative SMI with DFS among surgical lung cancer patients. CI, confidence interval; DFS, disease-free survival; HR, hazard ratio; SMI, skeletal muscle mass index.

Association of preoperative SMI with CSS among surgical lung cancer

Only three studies explored the relationship between preoperative SMI and CSS (18,20,25). According to the pooled results, an obvious statistical trend for the association of lower preoperative SMI with worse CSS was observed (HR =1.64; 95% CI: 0.91–2.95, P=0.10; I2=79.5%, P=0.008) (Figure 4, Table 2).

Figure 4 Association of preoperative SMI with CSS among surgical lung cancer patients. CI, confidence interval; CSS, cancer-specific survival; HR, hazard ratio; SMI, skeletal muscle mass index.

Sensitivity analysis

According to the sensitivity analysis, the pooled results were stable and none of included studies had an impact on the overall conclusion (Figure 5).

Figure 5 Sensitivity analysis about the association of preoperative SMI with OS among surgical lung cancer patients. CI, confidence interval; OS, overall survival; SMI, skeletal muscle mass index.

Publication bias

According to Begg’s funnel plot (Figure 6A) and Egger’s test (P<0.001), significant publication bias was detected. Therefore, the trim-and-fill method was applied and six potentially unpublished studies were detected (Figure 6B). However, these six studies did not affect the overall conclusion (fixed HR =1.05, 95% CI: 1.04–1.07, P<0.001; random HR =1.19, 95% CI: 1.06–1.34, P=0.004), which also indicated the reliability of our findings.

Figure 6 Publication bias analysis about the association of preoperative SMI with OS among surgical lung cancer patients. (A) Begg’s funnel plot about the association of preoperative SMI with OS among surgical lung cancer patients. (B) Filled funnel plot about the association of preoperative SMI with OS among surgical lung cancer patients. HR, hazard ratio; OS, overall survival; SE, standard error; SMI, skeletal muscle mass index.

Discussion

In the present meta-analysis, we synthesized the available evidence regarding the prognostic role of preoperative SMI in lung cancer patients undergoing surgery and found that a lower preoperative SMI was significantly associated with poorer OS and DFS and also showed a trend toward worse CSS. These findings suggest that preoperative SMI may serve as a meaningful indicator for identifying high-risk patients before surgery. Given that surgery remains the main curative treatment for resectable lung cancer, accurate preoperative risk stratification is clinically important for optimizing perioperative decision-making and long-term management. Our findings are also generally in line with recent studies and reviews showing that sarcopenia or reduced skeletal muscle mass is associated with inferior outcomes in surgically treated lung cancer patients, supporting the growing recognition of body composition as an important prognostic domain in thoracic oncology (10,29,30).

Several mechanisms may explain why a lower SMI is associated with poorer survival in surgical lung cancer patients. First, low SMI usually reflects impaired nutritional reserve and cancer-related catabolic status, which may reduce tolerance to surgical stress and hinder postoperative recovery. Second, skeletal muscle is increasingly regarded as an important metabolic and endocrine organ; loss of muscle mass is closely linked to chronic systemic inflammation, immune dysregulation, insulin resistance, and altered myokine signaling, all of which may contribute to tumor progression and unfavorable oncologic outcomes. Third, reduced muscle mass is often accompanied by poorer physical performance, compromised cardiopulmonary fitness, and frailty, which may limit treatment tolerance, delay adjuvant therapy, and increase vulnerability to recurrence and death. In lung cancer specifically, emerging evidence suggests that altered body composition and sarcopenia are closely intertwined with systemic inflammation, host immunity, and treatment response, providing a biologically plausible explanation for the association between low SMI and worse prognosis (31-33).

From a clinical perspective, preoperative SMI may be incorporated into the routine assessment of patients scheduled for lung cancer surgery to improve individualized management. Because SMI reflects the patient’s objective physical reserve more directly than many blood-based biomarkers, it may help clinicians identify patients who are more likely to experience unfavorable long-term outcomes despite curative-intent resection. For patients with low preoperative SMI, closer postoperative surveillance, intensified nutritional evaluation and support, and earlier implementation of exercise-based rehabilitation or multimodal prehabilitation strategies may be warranted (34). In addition, SMI may be considered together with conventional clinicopathological factors to refine perioperative counseling and postoperative treatment planning (35). Recent evidence indicates that nutritional support, pulmonary rehabilitation, and multimodal prehabilitation may improve functional capacity, reduce postoperative complications, and enhance recovery in lung cancer surgery populations, supporting the practical value of identifying low-SMI patients before treatment (36,37).

Several limitations of this meta-analysis should be acknowledged. First, all included studies were retrospective, which increases the risk of selection bias and residual confounding. In addition, limited reporting prevented more detailed subgroup analyses (e.g., by age or tumor stage), potentially affecting the robustness of our findings. Second, although all studies assessed preoperative skeletal muscle mass using CT, substantial methodological variability existed in anatomical measurement sites, muscle constructs, cutoff definitions (including sex-specific vs. non-sex-specific thresholds), and statistical modeling approaches (categorical vs. continuous). As this analysis was based on published aggregate data, we were unable to standardize exposure definitions or apply a uniform cutoff. Therefore, the pooled HRs should be interpreted as reflecting a general association between reduced CT-derived skeletal muscle status and prognosis rather than the effect of a single standardized SMI definition. Third, clinical heterogeneity was considerable with respect to tumor stage distribution and treatment background, including the inclusion of neoadjuvant-treated or mixed populations. Although most studies reported multivariable-adjusted estimates, residual confounding related to stage, treatment strategies, and host-related frailty cannot be excluded. Finally, statistical heterogeneity was substantial for several outcomes (e.g., OS, DFS, and CSS). While random-effects models and sensitivity analyses were applied, high I2 values indicate notable between-study variability. Due to the limited number of studies and incomplete reporting of potential effect modifiers, formal meta-regression analyses were not feasible. Accordingly, the pooled effect sizes should be interpreted with caution.

Conclusions

This meta-analysis indicates that lower preoperative SMI may be associated with poorer long-term survival in lung cancer patients undergoing surgery, particularly for OS and DFS. These findings suggest that preoperative SMI has potential value in prognostic assessment and risk stratification for surgical lung cancer patients. However, given that all included studies were retrospective and methodological heterogeneity existed across studies, the results should be interpreted with caution. Further well-designed prospective studies are required to confirm these findings and clarify the clinical utility of SMI in this population.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1213/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1213/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2026-1213/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

  1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024;74:229-63. [Crossref] [PubMed]
  2. Kang MW. Evolution of Lung Cancer Surgery: Historical Milestones, Current Strategy, and Future Innovations. J Chest Surg 2025;58:79-84. [Crossref] [PubMed]
  3. Zhang R, Zou C, Zeng L, et al. Perioperative immunotherapy in nonsmall cell lung cancer. Curr Opin Oncol 2025;37:40-7. [Crossref] [PubMed]
  4. Rajaram R, Huang Q, Li RZ, et al. Recurrence-Free Survival in Patients With Surgically Resected Non-Small Cell Lung Cancer: A Systematic Literature Review and Meta-Analysis. Chest 2024;165:1260-70. [Crossref] [PubMed]
  5. Bogatsa E, Lazaridis G, Stivanaki C, et al. Neoadjuvant and Adjuvant Immunotherapy in Resectable NSCLC. Cancers (Basel) 2024;16:1619. [Crossref] [PubMed]
  6. Taylor M, Evison M, Michael S, et al. Pre-Operative Measures of Systemic Inflammation Predict Survival After Surgery for Primary Lung Cancer. Clin Lung Cancer 2024;25:460-467.e7. [Crossref] [PubMed]
  7. Abbosh C, Hodgson D, Doherty GJ, et al. Implementing circulating tumor DNA as a prognostic biomarker in resectable non-small cell lung cancer. Trends Cancer 2024;10:643-54. [Crossref] [PubMed]
  8. Li S, Xie K, Xiao X, et al. Correlation between sarcopenia and esophageal cancer: a narrative review. World J Surg Oncol 2024;22:27. [Crossref] [PubMed]
  9. Trestini I, Belluomini L, Dodi A, et al. Body composition derangements in lung cancer patients treated with first-line pembrolizumab: A multicentre observational study. J Cachexia Sarcopenia Muscle 2024;15:2349-60. [Crossref] [PubMed]
  10. Uchibori A, Okada S, Shimomura M, et al. Clinical impact of preoperative sarcopenia and immunonutritional impairment on postoperative outcomes in non-small cell lung cancer surgery. Lung Cancer 2024;198:108004. [Crossref] [PubMed]
  11. Zhang X, Tan R, Lam WC, et al. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) Extension for Chinese Herbal Medicines 2020 (PRISMA-CHM 2020). Am J Chin Med 2020;48:1279-313. [Crossref] [PubMed]
  12. Wang Y, Li J, Chang S, et al. Risk and Influencing Factors for Subsequent Primary Lung Cancer After Treatment of Breast Cancer: A Systematic Review and Two Meta-Analyses Based on Four Million Cases. J Thorac Oncol 2021;16:1893-908. [Crossref] [PubMed]
  13. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics 1994;50:1088-101.
  14. Egger M, Davey Smith G, Schneider M, et al. Bias in meta-analysis detected by a simple, graphical test. BMJ 1997;315:629-34. [Crossref] [PubMed]
  15. Shoji F, Matsubara T, Kozuma Y, et al. Relationship Between Preoperative Sarcopenia Status and Immuno-nutritional Parameters in Patients with Early-stage Non-small Cell Lung Cancer. Anticancer Res 2017;37:6997-7003. [Crossref] [PubMed]
  16. Choi H, Park YS, Na KJ, et al. Association of Adipopenia at Preoperative PET/CT with Mortality in Stage I Non-Small Cell Lung Cancer. Radiology 2021;301:645-53. [Crossref] [PubMed]
  17. Tanaka S, Ozeki N, Mizuno Y, et al. Preoperative paraspinous muscle sarcopenia and physical performance as prognostic indicators in non-small-cell lung cancer. J Cachexia Sarcopenia Muscle 2021;12:646-56. [Crossref] [PubMed]
  18. Kaltenhauser S, Niessen C, Zeman F, et al. Diagnosis of sarcopenia on thoracic computed tomography and its association with postoperative survival after anatomic lung cancer resection. Sci Rep 2023;13:18450. [Crossref] [PubMed]
  19. Ponholzer F, Groemer G, Ng C, et al. Sarcopenia and Mediastinal Adipose Tissue as a Prognostic Marker for Short- and Long-Term Outcomes after Primary Surgical Treatment for Lung Cancer. Cancers (Basel) 2023;15:5666. [Crossref] [PubMed]
  20. Sato S, Sato M, Shinohara H. Significance of preoperative evaluation of skeletal muscle index and immune-nutritional status for patients with early-stage non-small cell lung cancer. Gen Thorac Cardiovasc Surg 2023;71:354-62. [Crossref] [PubMed]
  21. Tao J, Fang J, Chen L, et al. Increased adipose tissue is associated with improved overall survival, independent of skeletal muscle mass in non-small cell lung cancer. J Cachexia Sarcopenia Muscle 2023;14:2591-601. [Crossref] [PubMed]
  22. Vedire Y, Nitsche L, Tiadjeri M, et al. Skeletal muscle index is associated with long term outcomes after lobectomy for non-small cell lung cancer. BMC Cancer 2023;23:778. [Crossref] [PubMed]
  23. Watanabe K, Kinoshita F, Takenaka T, et al. Skeletal muscle area predicts the outcomes of non-small-cell lung cancer after trimodality therapy. Interdiscip Cardiovasc Thorac Surg 2023;36:ivad020. [Crossref] [PubMed]
  24. Huang Y, Cun H, Mou Z, et al. Multiparameter body composition analysis on chest CT predicts clinical outcomes in resectable non-small cell lung cancer. Insights Imaging 2025;16:32. [Crossref] [PubMed]
  25. Mantz L, Mercaldo ND, Simon J, et al. Preoperative Chest CT Myosteatosis Indicates Worse Postoperative Survival in Stage 0-IIB Non-Small Cell Lung Cancer. Radiology 2025;314:e240282. [Crossref] [PubMed]
  26. Uchibori A, Okada S, Shimomura M, et al. Clinical impact of preoperative adipopenia on postoperative outcomes in non-small cell lung cancer surgery. Lung Cancer 2025;210:108810. [Crossref] [PubMed]
  27. Khan AA, Naeem W, Ansari M, et al. Skeletal Muscle Gauge as a Prognosticator of Survival in Resected Early-Stage Non-small Cell Lung Cancer. Ann Surg Oncol 2026;33:925-34. [Crossref] [PubMed]
  28. Khan AA, Naeem W, Shah SK, et al. Association Between Postoperative Skeletal Muscle Loss and Disease-Free Survival in Node-Negative NSCLC ≤ 4 cm Without Adjuvant Therapy: A 5-Years Trend Analysis. Ann Surg Oncol 2026;33:530-8. [Crossref] [PubMed]
  29. Lading T, Media AS, Tellefsen B, et al. Sarcopenia and survival in surgically treated non-small cell lung cancer: A systematic review and meta-analysis. Eur J Surg Oncol 2025;51:110538. [Crossref] [PubMed]
  30. Huang Y, Yuan F, Yang L, et al. Computed tomography (CT)-based skeletal muscle vertebral-related index to assess low muscle mass in patients with non-small cell lung cancer. Quant Imaging Med Surg 2024;14:5737-47. [Crossref] [PubMed]
  31. Santo V, Brunetti L, Pecci F, et al. Host-related Determinants of Response to Immunotherapy in Non-small Cell Lung Cancer: The Interplay of Body Composition, Metabolism, Sex and Immune Regulation. Curr Oncol Rep 2025;27:1427-47. [Crossref] [PubMed]
  32. Giovarelli M, Mocciaro E, Carnovale C, et al. Immunosenescence in skeletal muscle: The role-play in cancer cachexia chessboard. Semin Cancer Biol 2025;111:48-59. [Crossref] [PubMed]
  33. Feng Y, Wang L, Guo F, et al. Predictive impact of sarcopenia in advanced non-small cell lung cancer patients treated with immune checkpoint inhibitors: A retrospective study. Heliyon 2024;10:e27282. [Crossref] [PubMed]
  34. Werblińska A, Zielińska D, Szlanga L, et al. The Impact of Nutritional Support on Outcomes of Lung Cancer Surgery-Narrative Review. J Clin Med 2025;14:3197. [Crossref] [PubMed]
  35. Mania K, Pieczynska A, Hojan K. Comprehensive Multimodal Prehabilitation for Lung Cancer: A Systematic Review of Randomized Controlled Trials. Ther Clin Risk Manag 2025;21:1735-45. [Crossref] [PubMed]
  36. Colaço AJ, Castro C, Hall S, et al. Prehabilitation: preoperative rehabilitation interventions for lung cancer - a scoping review. Front Aging 2025;6:1665955. [Crossref] [PubMed]
  37. Zhong J, Trinh I, Raju S, et al. Pulmonary Rehabilitation in Patients with Operable Non-Small Cell Lung Cancer. J Clin Med 2025;14:770. [Crossref] [PubMed]
Cite this article as: Wei X, Zhang Y, Zhu M, Zhang Y, Ma Y. Prognostic role of preoperative skeletal muscle mass index in surgical lung cancer patients: a systematic review with meta-analysis. J Thorac Dis 2026;18(7):784. doi: 10.21037/jtd-2026-1213

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