Does Pre-Checkpoint Inhibitor Sarcopenia, Visceral, or Subcutaneous Fat Predict Survival in Non-Small Cell Lung Cancer Patients? 

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Julia Kooser, Mellar Davis, Tian Guo, Erin Vanenkevort, Amanda Young, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5389970/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Dec, 2025 Read the published version in Supportive Care in Cancer → Version 1 posted 7 You are reading this latest preprint version Abstract Background The role of skeletal muscle area (SMA), subcutaneous, and visceral fat area (SFA and VFA) in cancer survivorship is inconsistent. We investigated the prognostic significance of the skeletal muscle index, subcutaneous and visceral fat area specifically via CT scans around the time of checkpoint inhibitor therapy in patients with non-small cell lung cancer (NSCLC). Methods CT scans of patients within 60 days of checkpoint inhibitor medication use were utilized to assess skeletal muscle area visceral fat index (VFA), subcutaneous fat area (SFA), and visceral and subcutaneous fat ratio corrected by patients’ height in meters squared. Skeletal muscle and fat areas at L3 were read by a single trained reader using TeraRecon software. Survival (in days) was calculated from the first CT scan to the death date. Survival analysis was performed using a Cox proportional hazards model to evaluate the association between body composition metrics and patient survival outcomes at one and two years. Multiple regression models were utilized with all CT parameters in a single model Results With 46 patients included in the analysis, our results did not show a significant relationship between any parameters assessed (SMA, SFA, VFA, visceral and subcutaneous fat ratio, and days from checkpoint inhibitor therapy to initial scan) and cancer survivorship in either female or male patients. Discussion Our results demonstrate no significant relationship between the parameters assessed and NSCLC survivorship in either male or female patients, which is consistent with small studies. However, meta-analyses of multiple studies support the association of pre-immunotherapy with reduced survival. Conclusion Pre-treatment Sarcopenia, SFA, and VFA do not appear to predict cancer survival on checkpoint inhibitors in small studies. Larger studies are needed to explore the utility of CT scan-derived SMI and fat area in predicting checkpoint inhibitor benefits in patients with lung cancer. sarcopenia visceral fat area subcutaneous fat area skeletal muscle area checkpoint inhibitor therapy Figures Figure 1 Figure 2 Introduction Sarcopenia (progressive loss of skeletal muscle mass) is an adverse effect associated with different treatments and cancers [ 1 ]. The pathophysiology is diverse, which contributes to sarcopenia. More specifically, mechanisms accounting for sarcopenia within cancer include systemic inflammation and metabolic derangements [ 2 ]. For example, C-reactive protein (CRP) is an acute phase reactant that rises in acute inflammation and tissue damage [ 1 ]. CRP has also been reported to be elevated with both sarcopenia and cancer [ 1 ]. Additionally, high levels of CRP have been associated with worse survival outcomes in a wide variety of cancers [ 1 ]. Specifically, systemic inflammatory states, such as those seen with cancer, are associated with depressed insulin-like growth factor1 (IGF-1) and IGF-binding protein-3 (IGFBP-3) [ 1 ]. Metabolic derangements also occur at the muscle level, such as high cortisol levels stimulating glycogenolysis and proteolysis [ 1 ]. Subcutaneous fat, found directly beneath the skin and outside the organs, has been associated with improved and worse survivorship in different cancers [ 3 , 4 ]. Different mechanisms account for potential protective effects of subcutaneous fat, such as increased adiponectin levels in subcutaneous fat, and adiponectin is associated with increased insulin sensitization and reduced inflammation [ 5 , 6 ]. However, subcutaneous fat may still be implicated in systemic inflammation, which could further exacerbate cancer. For example, in pregnant women between 24–28 weeks gestation, there was a significant association between subcutaneous abdominal fat thickness (SCFT) and glycated hemoglobin [ 7 , 8 ]. Also, higher levels of CRP were found in 47.9% of cases with SCFT over 15 mm [ 7 , 8 ]. Visceral fat, which lies within the omentum beneath subcutaneous fat and lines organs, has been associated with both worse and better survivorship outcomes in the setting of cancer [ 9 , 10 ]. Visceral fat is associated with the release of pro-inflammatory factors and high fat content, which can result in systemic inflammation and insulin that could further mediate the pathogenic effects of cancer [ 11 ]. Checkpoint inhibitors have become an important therapy in cancer and are highly dependent on a host immune response. There is growing clinical evidence of an association between muscle and immune responsiveness. Irisin and titin, which arise from muscle, have important interactions with the immune system [ 12 – 16 ]. Several reviews have explored the association of sarcopenia and checkpoint inhibitor lung cancer responses [ 17 – 22 ]. Therefore, we wished to explore the association of skeletal muscle, visceral, and subcutaneous fat areas at L3 on CT scans performed during checkpoint inhibitor therapy and survival after initiating checkpoint therapy. Methods Patient data were collected from electronic medical records. The study was reviewed by the Geisinger Medical Center Institutional Review Board and was given exemption status as a retrospective study. The study sample is 263 patients. However, patients missing key variables were removed from this analysis. Additionally, only scans within 60 days of the checkpoint inhibitor medication data were used. we used the third lumbar vertebra (L3) skeletal muscle mass index as an estimator of sarcopenia: the recommended was extracted by one operator (JK), under the supervision of (NK), using skeletal muscle area measurement on axial computed tomography (CT) sections (cm2 of muscle tissue) at the level of the upper edge of L3. The muscles in the L3 region, containing psoas, erector spinal, quadratus lumborum, transversus abdominis, external and internal obliques, and rectus abdominis. Therefore, 46 patients are included in the analysis. Demarcation by Hounsfield units was − 29 to + 150 for skeletal muscle, -150 to -50 for visceral fat, and − 190 to -30 for subcutaneous fat. Visceral and subcutaneous fat areas were measured from the same area and corrected for height. The TeraRecon (Durham, North Carolina, USA) software was used to collect muscle and fat areas. Key variables included skeletal muscle area (SMA), visceral fat area (VFA), subcutaneous fat area (SFA), and visceral and subcutaneous fat ratio. Each outcome was divided by their height (in meters) squared to standardize these measurements by the patients' size. Survival (in days) was calculated from the first CT scan to the death date or to date of last follow-up. Data was described using median and interquartile range (IQR) for continuous variables and frequencies and percentages for categorical variables. Survival analysis was performed using a Cox proportional hazards model to evaluate the association between body composition metrics and patient survival outcomes at one and two years. Multiple regression models were utilized with all CT parameters in a single model to examine the effect of each while controlling for all other parameters. The analyses were performed separately for patients with CT scans taken within 30 and 60 days of diagnosis. All analyses were performed using SAS Enterprise Guide 8.3 (SAS Institute, Inc., Cary, NC, USA). Results A total of 46 patients were included in the analysis. Patients’ demographic and clinical characteristics are described in Table 1 . Over half of the patients were male (54.3%). The median time from the initial cancer diagnosis to the first scan was 111 days. The median time from checkpoint medication to the initial scan was 14 days. The results of the scans are outlined in Table 2 . The median visceral and subcutaneous fat ratio was 0.9 (IQR: 0.6, 1.6) for all patients, for females only 0.6 (IQR: 0.5, 0.8) and males only 1.4 (IQR) 0.9, 2.0). There were 27 of 46 patients alive at one year and 13 of 46 at two years. The survival model, including the days from medication to scan and scan results, is seen in Table 3 a. There are no significant associations between parameters and survival for both males and females. The bivariate model and each adjustment for days from checkpoint inhibitor to scan results are seen in Tables 3 b and 3 c. Full survival model is shown for females and males in Figs. 1 and 2 respectively. None of the parameters have a significant relationship to survival. The one and two-year survival rates are shown in Table 4 . Females have a one-year survival rate of 59.3% and a two-year survival rate of 27.3%. Males have a slightly higher one-year survival rate at 72.0% and two-year survival rate of 33.7% compared to females. Discussion Our results show no significant relationship between the parameters (SMA, SFA, VFA, VFA/SFA ratio, days from checkpoint inhibitor therapy) in female or male patients. Female patients had somewhat lower survivorship rates than their male counterparts. Several small studies have not found that sarcopenia before checkpoint inhibitors influences survival in non-small cell lung cancer patients. In an Asian study involving 820 patients, obesity, defined as a BMI of greater than 25 kilograms/meter squared, was associated with improved survival independent of skeletal muscle index and visceral fat index as well as gender [ 23 ]. A second retrospective study of 74 patients with non-small cell lung cancer undergoing checkpoint inhibitor found no association between survival and psoas muscle index, visceral to subcutaneous fat ratio or visceral fat area before beginning therapy [ 24 ]. A third study involving 142 consecutive patients with non-small cell lung cancer who received a checkpoint inhibitor as first or second-line therapy, a multi-variant analysis, and Cox model found that sarcopenia before treatment and evolving sarcopenia was associated with reduced progression-free survival, but overall survival was not different between those with sarcopenia and those without [ 25 ]. A similar study involving 34 patients with non-small cell lung cancer undergoing checkpoint inhibitor therapy found that sarcopenia before treatment predicted reduced progression-free survival but not overall survival [ 26 ]. Three meta-analyses have demonstrated that sarcopenia, defined as a reduced skeletal muscle index before checkpoint inhibitor therapy, predicts reduced survival in patients with non-small cell lung cancer. A meta-analysis of 19 studies involving 1763 patients found that pretreatment sarcopenia was associated with reduced survival with a hazard ratio of 1.73 (95% confidence interval 1.36 to 2.19) [ 27 ]. A meta-analysis of 13 studies, 9 of which had reported the influence of pre-immunotherapy sarcopenia on survival, found that pretreatment sarcopenia was associated with a poorer 1-year survival with the odds ratio of 2.44 (95% confidence interval 1.78 to 3.35) and 2-year survival with an odds ratio of 1.6 (95% confidence interval 1.08 to 2.37) [ 28 ]. Finally, a meta-analysis of 9 studies involving 579 patients found that pre-immunotherapy sarcopenia was associated with a reduced overall survival with a hazard ratio of 1.61 (95% confidence interval 1.24 to 2.10) [ 29 ]. While our findings do not demonstrate a significant association between sarcopenia and survivorship, independent meta-analysis has demonstrated that pretreatment sarcopenia does predict a poor response. Our study is likely underpowered, as were the smaller studies discussed in the previous paragraph. There are inconsistent findings between increased subcutaneous or visceral fat area and cancer survivorship. Multiple studies suggest that increased subcutaneous and visceral fat area, as well as increased obesity, may be associated with better survivorship outcomes in lung cancer [ 9 , 30 – 31 ]. Others have demonstrated significantly reduced recurrence-free and overall survivorship with increased adiposity [ 32 , 33 ]. Our study was small, with only 46 patients in the final analysis. Also, we extensively assessed associations between parameters and survivorship. The mechanism by which SMA, SFA, and VFA would impact survivorship of NSCLC in patients receiving checkpoint inhibitor therapy is not known, and most studies were not prospective; hence, one can only say there appears to be a modest association which may be missed in small observational studies. Our patients’ parameters were also assessed near the time patients received checkpoint inhibitor therapy; changes over time may be more important as a predictor of survival rather than at the time of initiating checkpoint inhibitor therapy. As evidenced by differing results in published studies, the role of sarcopenia SFA and VFA in predicting survivorship in cancer is still not clearly understood. By following CT scans of skeletal and fat area changes of patients with NSCLC who are treated with checkpoint inhibitor therapy, we may be able to create a simple and easily usable yet highly accurate prognostic tool for better understanding the role of SMA, SFA, and VFA in survivorship of patients. Conclusion Sarcopenia, as measured by the skeletal muscle index and visceral and subcutaneous fat areas on an L3 CT scan, may become a clinical parameter to follow on checkpoint inhibitor therapy. However, in small studies like ours, with only baseline measurements, such parameters may not predict survival. Future studies will need to validate the benefits of routinely adding measurements of skeletal muscle and fat areas on diagnostic and follow-up CT scans in patients with non-small cell lung cancer on checkpoint inhibitor therapy. Declarations Funding- The authors declare that no funds, grants, or other support were received during or utilized for preparation of the manuscript. Competing Interests- the authors have no competing interests to disclose currently. Author Contributions- JK wrote the main manuscript text and collected data. MD wrote manuscript text, played an essential role in research design, and edited and reviewed manuscript. TG conducted the statistical analysis and was responsible for writing the methods and results sections. Additionally, created the accompanying tables and was involved in reviewing and editing the manuscript. EV collected and analyzed data and was involved in reviewing and editing the manuscript. AY was responsible for statistical analysis and editing of the paper. NK was responsible for collecting data and reviewed and edited the paper. BL and MG were responsible for data pull and contributed to data points in analysis. AP was responsible for brainstorming study design and editing and reviewing manuscript. MW was responsible for helping with study design and data collection and editing and reviewing manuscript. Ethics Approval- This is a retrospective study and does not include live subjects. The study was reviewed by Geisinger Medical Center’s IRB and received exemption status. Consent to participate- The study received IRB exemption status. Patient identifiers were sequestered and not provided. Consent to publish- The study received IRB exemption status. Patient identifiers were sequestered and not provided. References Prado CM, Baracos VE, McCargar LJ, et al. Sarcopenia as a determinant of chemotherapy toxicity and time to tumor progression in metastatic breast cancer patients receiving capecitabine treatment. Clin Cancer Res . Apr 15 2009;15(8):2920-6. doi:10.1158/1078-0432.CCR-08-2242 Armstrong VS, Fitzgerald LW, Bathe OF. Cancer-Associated Muscle Wasting—Candidate Mechanisms and Molecular Pathways. International Journal of Molecular Sciences . 2020;21(23):9268. Ebadi M, Martin L, Ghosh S, et al. Subcutaneous adiposity is an independent predictor of mortality in cancer patients. 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Diabetology & Metabolic Syndrome . 2011/06/22 2011;3(1):12. doi:10.1186/1758-5996-3-12 Tables Table 1 Patient demographics and characteristics (n = 46) Median (IQR) Male , n (%) 25 (54.3%) Date of initial cancer diagnosis to first scan date (days) 111 (62, 249) Date of initial cancer diagnosis to date of checkpoint medication (days) 124.5 (71.0, 259.0) Date of checkpoint medication to initial scan on (days) -14 (-47,18) Medication name , n (%) Nivolumab infusion 3 (6.5%) Pembrolizumab infusion 43 (93.5%) White blood cell 10.0 (7.9, 13.9) Albumin 3.8 (3.3, 3.9) Missing 1 Neutrophil 8.1 (5.0, 11.6) Lymphocytes 1.1 (0.7, 1.9) Neutrophil and lymphocyte ratio 6.3 (3.4, 10.6) Status of Death , n (%) 35 (76.1%) *Data is summarized by median and interquartile range (IQR) unless otherwise noted. Table 2 CT Scan Measurements All (n = 46) Female (n = 21) Male (n = 25) Skeletal muscle area 0.5 (0.3, 0.7) 0.6 (0.4, 1.1) 0.6 (0.2, 0.6) Visceral fat area 0.5 (0.2, 0.7) 0.4 (0.2, 0.6) 0.6 (0.3, 0.8) Subcutaneous fat area 0.4 (0.4, 0.5) 0.4 (0.3, 0.4) 0.5 (0.4, 0.5) Visceral and subcutaneous fat ratio 0.9 (0.6, 1.6) 0.6 (0.5, 0.8) 1.4 (0.9, 2.0) *Data is summarized by median and interquartile range (IQR) unless otherwise noted. Table 3 a. Survival Model of CT scan measurements for females and males separately Standardized Parameter Hazard Ratio (95% Confidence Interval) p-value Female Skeletal muscle area 0.09 (0.00, 111860.90) 0.74 Visceral fat area 12.78 (0.45, 362.57) 0.14 Subcutaneous fat area 0.32 (0.01, 11.58) 0.53 Days from checkpoint inhibitor to CT scan 0.49 (0.11, 2.13) 0.34 Male Skeletal muscle area 15.20 (0.05, 5015.12) 0.36 Visceral fat area 0.91 (0.18, 4.61) 0.91 Subcutaneous fat area 0.17 (0.01, 4.63) 0.30 Days from checkpoint inhibitor to CT scan 1.23 (0.43, 3.53) 0.70 *Included in the model: Skeletal muscle area, visceral fat area, subcutaneous fat area, days from checkpoint inhibitor to CT scan. Table 3 b. Survival Model of each CT scan measurement individually separately for females and males (Bivariate) Standardized Parameter Hazard Ratio (95% Confidence Interval) p-value Female Skeletal muscle area 0.81 (0.00, 1243.71) 0.95 Visceral fat area 2.31 (0.52, 10.26) 0.27 Subcutaneous fat area 1.09 (0.30, 3.92) 0.90 Visceral and subcutaneous fat ratio 1.19 (0.35, 4.09) 0.78 Male Skeletal muscle area 1.12 (0.02, 64.62) 0.96 Visceral fat area 0.54 (0.15, 1.98) 0.35 Subcutaneous fat area 0.32 (0.04, 2.57) 0.28 Visceral and subcutaneous fat ratio 0.92 (0.56, 1.50) 0.73 * Each measurement is put in the model separately, unadjusted. Table 3 c. Survival Model for each CT scan measurement adjusted for time from checkpoint inhibitor to scan separately for females and males Standardized Parameter Hazard Ratio (95% Confidence Interval) p-value Female Skeletal muscle area 0.28 (0.00, 801.74) 0.75 Visceral fat area 3.24 (0.63, 16.51) 0.16 Subcutaneous fat area 1.29 (0.32, 5.22) 0.72 Visceral and subcutaneous fat ratio 1.33 (0.37, 4.78) 0.66 Male Skeletal muscle area 1.70 (0.02, 142.54) 0.82 Visceral fat area 0.55 (0.15, 2.06) 0.38 Subcutaneous fat area 0.33 (0.04, 2.79) 0.31 Visceral and subcutaneous fat ratio 0.92 (0.56, 1.51) 0.73 * Each measurement is adjusted for days from checkpoint inhibitor to CT scan. Table 4 The one and two-year rate of survival separately for the full model for females and males Survival Rate (95% Confidence Interval) Female 1-year Survival Rate 59.3% (35.7%, 98.5%) 2-year Survival Rate 27.3% (8.5%, 88.0%) Male 1-year Survival Rate 72.0% (51.3%, 100%) 2-year Survival Rate 33.7% (12.4%, 91.9%) *Included in the model: Skeletal muscle area, visceral fat area, subcutaneous fat area, days from checkpoint inhibitor to CT scan. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Dec, 2025 Read the published version in Supportive Care in Cancer → Version 1 posted Editorial decision: Revision requested 04 Mar, 2025 Reviews received at journal 04 Mar, 2025 Reviewers agreed at journal 03 Mar, 2025 Reviewers invited by journal 23 Dec, 2024 Editor assigned by journal 23 Dec, 2024 Submission checks completed at journal 12 Nov, 2024 First submitted to journal 04 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Center","correspondingAuthor":false,"prefix":"","firstName":"Amanda","middleName":"","lastName":"Young","suffix":""},{"id":377038642,"identity":"54f4cffd-d560-4080-9d78-2e0c365ba0f0","order_by":5,"name":"Nicole Koppenhaver","email":"","orcid":"","institution":"Geisinger Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Nicole","middleName":"","lastName":"Koppenhaver","suffix":""},{"id":377038643,"identity":"38a1b912-7a34-425e-9b7f-5593052d4895","order_by":6,"name":"Braxton Lagerman","email":"","orcid":"","institution":"Geisinger Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Braxton","middleName":"","lastName":"Lagerman","suffix":""},{"id":377038644,"identity":"18f86176-47cb-4be3-b7fd-34fa02d8a166","order_by":7,"name":"Mudit Gupta","email":"","orcid":"","institution":"Geisinger Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Mudit","middleName":"","lastName":"Gupta","suffix":""},{"id":377038645,"identity":"07583ac7-7d2a-489d-b679-43adca95467b","order_by":8,"name":"Aalpen Patel","email":"","orcid":"","institution":"Geisinger Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Aalpen","middleName":"","lastName":"Patel","suffix":""},{"id":377038646,"identity":"1613f46e-e0f4-4e3b-a538-55ce5e22e3a8","order_by":9,"name":"Mark Wojtowicz","email":"","orcid":"","institution":"Geisinger Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Wojtowicz","suffix":""}],"badges":[],"createdAt":"2024-11-04 17:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5389970/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5389970/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00520-025-10243-z","type":"published","date":"2025-12-19T15:57:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71605790,"identity":"3fdafecd-e608-4cd5-b128-3a8ef79c91f4","added_by":"auto","created_at":"2024-12-17 06:10:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22450,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFull Survival Model for Females (related Tables 3a, 4)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5389970/v1/4425c2ecd45e606ce88170f9.png"},{"id":71605788,"identity":"4be65dd8-136d-423d-9010-ea312f4e9b7a","added_by":"auto","created_at":"2024-12-17 06:10:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23867,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFull Survival Model for Males (related Tables 3a, 4)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5389970/v1/2722149afd1723d2f7ca47e1.png"},{"id":98813929,"identity":"aaa6454d-846c-4890-842a-a76997b7e9ea","added_by":"auto","created_at":"2025-12-22 16:07:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":995674,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5389970/v1/b1cb91e6-5b2a-422f-aae4-99ed058753f1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Does Pre-Checkpoint Inhibitor Sarcopenia, Visceral, or Subcutaneous Fat Predict Survival in Non-Small Cell Lung Cancer Patients? ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSarcopenia (progressive loss of skeletal muscle mass) is an adverse effect associated with different treatments and cancers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The pathophysiology is diverse, which contributes to sarcopenia. More specifically, mechanisms accounting for sarcopenia within cancer include systemic inflammation and metabolic derangements [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. For example, C-reactive protein (CRP) is an acute phase reactant that rises in acute inflammation and tissue damage [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. CRP has also been reported to be elevated with both sarcopenia and cancer [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Additionally, high levels of CRP have been associated with worse survival outcomes in a wide variety of cancers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSpecifically, systemic inflammatory states, such as those seen with cancer, are associated with depressed insulin-like growth factor1 (IGF-1) and IGF-binding protein-3 (IGFBP-3) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Metabolic derangements also occur at the muscle level, such as high cortisol levels stimulating glycogenolysis and proteolysis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSubcutaneous fat, found directly beneath the skin and outside the organs, has been associated with improved and worse survivorship in different cancers [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Different mechanisms account for potential protective effects of subcutaneous fat, such as increased adiponectin levels in subcutaneous fat, and adiponectin is associated with increased insulin sensitization and reduced inflammation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, subcutaneous fat may still be implicated in systemic inflammation, which could further exacerbate cancer. For example, in pregnant women between 24\u0026ndash;28 weeks gestation, there was a significant association between subcutaneous abdominal fat thickness (SCFT) and glycated hemoglobin [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Also, higher levels of CRP were found in 47.9% of cases with SCFT over 15 mm [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVisceral fat, which lies within the omentum beneath subcutaneous fat and lines organs, has been associated with both worse and better survivorship outcomes in the setting of cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Visceral fat is associated with the release of pro-inflammatory factors and high fat content, which can result in systemic inflammation and insulin that could further mediate the pathogenic effects of cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCheckpoint inhibitors have become an important therapy in cancer and are highly dependent on a host immune response. There is growing clinical evidence of an association between muscle and immune responsiveness. Irisin and titin, which arise from muscle, have important interactions with the immune system [\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Several reviews have explored the association of sarcopenia and checkpoint inhibitor lung cancer responses [\u003cspan additionalcitationids=\"CR18 CR19 CR20 CR21\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, we wished to explore the association of skeletal muscle, visceral, and subcutaneous fat areas at L3 on CT scans performed during checkpoint inhibitor therapy and survival after initiating checkpoint therapy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003ePatient data were collected from electronic medical records. The study was reviewed by the Geisinger Medical Center Institutional Review Board and was given exemption status as a retrospective study. The study sample is 263 patients. However, patients missing key variables were removed from this analysis. Additionally, only scans within 60 days of the checkpoint inhibitor medication data were used. we used the third lumbar vertebra (L3) skeletal muscle mass index as an estimator of sarcopenia: the recommended was extracted by one operator (JK), under the supervision of (NK), using skeletal muscle area measurement on axial computed tomography (CT) sections (cm2 of muscle tissue) at the level of the upper edge of L3. The muscles in the L3 region, containing psoas, erector spinal, quadratus lumborum, transversus abdominis, external and internal obliques, and rectus abdominis. Therefore, 46 patients are included in the analysis. Demarcation by Hounsfield units was \u0026minus;\u0026thinsp;29 to +\u0026thinsp;150 for skeletal muscle, -150 to -50 for visceral fat, and \u0026minus;\u0026thinsp;190 to -30 for subcutaneous fat. Visceral and subcutaneous fat areas were measured from the same area and corrected for height. The TeraRecon (Durham, North Carolina, USA) software was used to collect muscle and fat areas.\u003c/p\u003e \u003cp\u003eKey variables included skeletal muscle area (SMA), visceral fat area (VFA), subcutaneous fat area (SFA), and visceral and subcutaneous fat ratio. Each outcome was divided by their height (in meters) squared to standardize these measurements by the patients' size. Survival (in days) was calculated from the first CT scan to the death date or to date of last follow-up. Data was described using median and interquartile range (IQR) for continuous variables and frequencies and percentages for categorical variables. Survival analysis was performed using a Cox proportional hazards model to evaluate the association between body composition metrics and patient survival outcomes at one and two years. Multiple regression models were utilized with all CT parameters in a single model to examine the effect of each while controlling for all other parameters. The analyses were performed separately for patients with CT scans taken within 30 and 60 days of diagnosis. All analyses were performed using SAS Enterprise Guide 8.3 (SAS Institute, Inc., Cary, NC, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 46 patients were included in the analysis. Patients\u0026rsquo; demographic and clinical characteristics are described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Over half of the patients were male (54.3%). The median time from the initial cancer diagnosis to the first scan was 111 days. The median time from checkpoint medication to the initial scan was 14 days. The results of the scans are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The median visceral and subcutaneous fat ratio was 0.9 (IQR: 0.6, 1.6) for all patients, for females only 0.6 (IQR: 0.5, 0.8) and males only 1.4 (IQR) 0.9, 2.0). There were 27 of 46 patients alive at one year and 13 of 46 at two years. The survival model, including the days from medication to scan and scan results, is seen in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003ea. There are no significant associations between parameters and survival for both males and females. The bivariate model and each adjustment for days from checkpoint inhibitor to scan results are seen in Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003eb and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3\u003c/span\u003ec. Full survival model is shown for females and males in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e respectively. None of the parameters have a significant relationship to survival. The one and two-year survival rates are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Females have a one-year survival rate of 59.3% and a two-year survival rate of 27.3%. Males have a slightly higher one-year survival rate at 72.0% and two-year survival rate of 33.7% compared to females.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results show no significant relationship between the parameters (SMA, SFA, VFA, VFA/SFA ratio, days from checkpoint inhibitor therapy) in female or male patients. Female patients had somewhat lower survivorship rates than their male counterparts.\u003c/p\u003e \u003cp\u003eSeveral small studies have not found that sarcopenia before checkpoint inhibitors influences survival in non-small cell lung cancer patients. In an Asian study involving 820 patients, obesity, defined as a BMI of greater than 25 kilograms/meter squared, was associated with improved survival independent of skeletal muscle index and visceral fat index as well as gender [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A second retrospective study of 74 patients with non-small cell lung cancer undergoing checkpoint inhibitor found no association between survival and psoas muscle index, visceral to subcutaneous fat ratio or visceral fat area before beginning therapy [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A third study involving 142 consecutive patients with non-small cell lung cancer who received a checkpoint inhibitor as first or second-line therapy, a multi-variant analysis, and Cox model found that sarcopenia before treatment and evolving sarcopenia was associated with reduced progression-free survival, but overall survival was not different between those with sarcopenia and those without [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A similar study involving 34 patients with non-small cell lung cancer undergoing checkpoint inhibitor therapy found that sarcopenia before treatment predicted reduced progression-free survival but not overall survival [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThree meta-analyses have demonstrated that sarcopenia, defined as a reduced skeletal muscle index before checkpoint inhibitor therapy, predicts reduced survival in patients with non-small cell lung cancer. A meta-analysis of 19 studies involving 1763 patients found that pretreatment sarcopenia was associated with reduced survival with a hazard ratio of 1.73 (95% confidence interval 1.36 to 2.19) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A meta-analysis of 13 studies, 9 of which had reported the influence of pre-immunotherapy sarcopenia on survival, found that pretreatment sarcopenia was associated with a poorer 1-year survival with the odds ratio of 2.44 (95% confidence interval 1.78 to 3.35) and 2-year survival with an odds ratio of 1.6 (95% confidence interval 1.08 to 2.37) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Finally, a meta-analysis of 9 studies involving 579 patients found that pre-immunotherapy sarcopenia was associated with a reduced overall survival with a hazard ratio of 1.61 (95% confidence interval 1.24 to 2.10) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile our findings do not demonstrate a significant association between sarcopenia and survivorship, independent meta-analysis has demonstrated that pretreatment sarcopenia does predict a poor response. Our study is likely underpowered, as were the smaller studies discussed in the previous paragraph.\u003c/p\u003e \u003cp\u003eThere are inconsistent findings between increased subcutaneous or visceral fat area and cancer survivorship. Multiple studies suggest that increased subcutaneous and visceral fat area, as well as increased obesity, may be associated with better survivorship outcomes in lung cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Others have demonstrated significantly reduced recurrence-free and overall survivorship with increased adiposity [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study was small, with only 46 patients in the final analysis. Also, we extensively assessed associations between parameters and survivorship. The mechanism by which SMA, SFA, and VFA would impact survivorship of NSCLC in patients receiving checkpoint inhibitor therapy is not known, and most studies were not prospective; hence, one can only say there appears to be a modest association which may be missed in small observational studies. Our patients\u0026rsquo; parameters were also assessed near the time patients received checkpoint inhibitor therapy; changes over time may be more important as a predictor of survival rather than at the time of initiating checkpoint inhibitor therapy.\u003c/p\u003e \u003cp\u003eAs evidenced by differing results in published studies, the role of sarcopenia SFA and VFA in predicting survivorship in cancer is still not clearly understood. By following CT scans of skeletal and fat area changes of patients with NSCLC who are treated with checkpoint inhibitor therapy, we may be able to create a simple and easily usable yet highly accurate prognostic tool for better understanding the role of SMA, SFA, and VFA in survivorship of patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eSarcopenia, as measured by the skeletal muscle index and visceral and subcutaneous fat areas on an L3 CT scan, may become a clinical parameter to follow on checkpoint inhibitor therapy. However, in small studies like ours, with only baseline measurements, such parameters may not predict survival. Future studies will need to validate the benefits of routinely adding measurements of skeletal muscle and fat areas on diagnostic and follow-up CT scans in patients with non-small cell lung cancer on checkpoint inhibitor therapy.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eFunding- The authors declare that no funds, grants, or other support were received during or utilized for preparation of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting Interests- the authors have no competing interests to disclose currently.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions-\u0026nbsp;JK wrote the main manuscript text and collected data. MD wrote manuscript text, played an essential role in research design, and edited and reviewed manuscript. TG conducted the statistical analysis and was responsible for writing the methods and results sections. Additionally, created the accompanying tables and was involved in reviewing and editing the manuscript. EV collected and analyzed data and was involved in reviewing and editing the manuscript. AY was responsible for statistical analysis and editing of the paper. NK was responsible for collecting data and reviewed and edited the paper. BL and MG were responsible for data pull and contributed to data points in analysis. AP was responsible for brainstorming study design and editing and reviewing manuscript. MW was responsible for helping with study design and data collection and editing and reviewing manuscript.\u003c/p\u003e\n\u003cp\u003eEthics Approval- This is a retrospective study and does not include live subjects. The study was reviewed by Geisinger Medical Center\u0026rsquo;s IRB and received exemption status. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent to participate- The study received IRB exemption status. Patient identifiers were sequestered and not provided.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent to publish- The study received IRB exemption status. Patient identifiers were sequestered and not provided.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePrado CM, Baracos VE, McCargar LJ, et al. Sarcopenia as a determinant of chemotherapy toxicity and time to tumor progression in metastatic breast cancer patients receiving capecitabine treatment. \u003cem\u003eClin Cancer Res\u003c/em\u003e. Apr 15 2009;15(8):2920-6. doi:10.1158/1078-0432.CCR-08-2242 \u003c/li\u003e\n\u003cli\u003eArmstrong VS, Fitzgerald LW, Bathe OF. Cancer-Associated Muscle Wasting\u0026mdash;Candidate Mechanisms and Molecular Pathways. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e. 2020;21(23):9268. \u003c/li\u003e\n\u003cli\u003eEbadi M, Martin L, Ghosh S, et al. 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Visceral Adiposity and Cancer: Role in Pathogenesis and Prognosis. \u003cem\u003eNutrients\u003c/em\u003e. Jun 19 2021;13(6)doi:10.3390/nu13062101 \u003c/li\u003e\n\u003cli\u003eBansal S, Vachher M, Arora T, Kumar B, Burman A. Visceral fat: A key mediator of NAFLD development and progression. \u003cem\u003eHuman Nutrition \u0026amp; Metabolism\u003c/em\u003e. 2023/09/01/ 2023;33:200210. doi:https://doi.org/10.1016/j.hnm.2023.200210 \u003c/li\u003e\n\u003cli\u003eZhang Z, Zhang C, Zhang S. Irisin Activates M1 Macrophage and Suppresses Th2-Type Immune Response in Rats with Pelvic Inflammatory Disease. \u003cem\u003eEvid Based Complement Alternat Med\u003c/em\u003e. 2022;2022:5215915. doi:10.1155/2022/5215915 \u003c/li\u003e\n\u003cli\u003eMazur-Bialy AI, Bilski J, Pochec E, Brzozowski T. New insight into the direct anti-inflammatory activity of a myokine irisin against proinflammatory activation of adipocytes. Implication for exercise in obesity. \u003cem\u003eJ Physiol Pharmacol\u003c/em\u003e. Apr 2017;68(2):243-251. \u003c/li\u003e\n\u003cli\u003eShao L, Li H, Chen J, et al. Irisin suppresses the migration, proliferation, and invasion of lung cancer cells via inhibition of epithelial-to-mesenchymal transition. \u003cem\u003eBiochem Biophys Res Commun\u003c/em\u003e. Apr 8 2017;485(3):598-605. doi:10.1016/j.bbrc.2016.12.084 \u003c/li\u003e\n\u003cli\u003eJia Q, Wang J, He N, He J, Zhu B. Titin mutation associated with responsiveness to checkpoint blockades in solid tumors. \u003cem\u003eJCI Insight\u003c/em\u003e. May 16 2019;4(10)doi:10.1172/jci.insight.127901 \u003c/li\u003e\n\u003cli\u003eXie X, Tang Y, Sheng J, et al. Titin Mutation Is Associated With Tumor Mutation Burden and Promotes Antitumor Immunity in Lung Squamous Cell Carcinoma. \u003cem\u003eFront Cell Dev Biol\u003c/em\u003e. 2021;9:761758. doi:10.3389/fcell.2021.761758 \u003c/li\u003e\n\u003cli\u003eRen B, Shen J, Qian Y, Zhou T. Sarcopenia as a Determinant of the Efficacy of Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer: A Meta-Analysis. \u003cem\u003eNutr Cancer\u003c/em\u003e. 2023;75(2):685-695. doi:10.1080/01635581.2022.2153879 \u003c/li\u003e\n\u003cli\u003eDeng HY, Chen ZJ, Qiu XM, Zhu DX, Tang XJ, Zhou Q. Sarcopenia and prognosis of advanced cancer patients receiving immune checkpoint inhibitors: A comprehensive systematic review and meta-analysis. \u003cem\u003eNutrition\u003c/em\u003e. Oct 2021;90:111345. doi:10.1016/j.nut.2021.111345 \u003c/li\u003e\n\u003cli\u003eWang J, Cao L, Xu S. Sarcopenia affects clinical efficacy of immune checkpoint inhibitors in non-small cell lung cancer patients: A systematic review and meta-analysis. \u003cem\u003eInt Immunopharmacol\u003c/em\u003e. Nov 2020;88:106907. doi:10.1016/j.intimp.2020.106907 \u003c/li\u003e\n\u003cli\u003eRoch B, Coffy A, Jean-Baptiste S, et al. Cachexia - sarcopenia as a determinant of disease control rate and survival in non-small lung cancer patients receiving immune-checkpoint inhibitors. \u003cem\u003eLung Cancer\u003c/em\u003e. May 2020;143:19-26. doi:10.1016/j.lungcan.2020.03.003 \u003c/li\u003e\n\u003cli\u003eFeng 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. \u003cem\u003eHeliyon\u003c/em\u003e. Mar 15 2024;10(5):e27282. doi:10.1016/j.heliyon.2024.e27282 \u003c/li\u003e\n\u003cli\u003eRoch B, Coffy A, Jean-Baptiste S, et al. Cachexia - sarcopenia as a determinant of disease control rate and survival in non-small lung cancer patients receiving immune-checkpoint inhibitors. \u003cem\u003eLung Cancer\u003c/em\u003e. 2020;143:19-26. doi:10.1016/j.lungcan.2020.03.003 \u003c/li\u003e\n\u003cli\u003eLee JH, Kang D, Ahn JS, Guallar E, Cho J, Lee HY. Obesity paradox in patients with non-small cell lung cancer undergoing immune checkpoint inhibitor therapy. \u003cem\u003eJ Cachexia Sarcopenia Muscle. \u003c/em\u003e2023;14(6):2898-2907.\u003c/li\u003e\n\u003cli\u003eMinami S, Ihara S, Tanaka T, Komuta K. Sarcopenia and Visceral Adiposity Did Not Affect Efficacy of Immune-Checkpoint Inhibitor Monotherapy for Pretreated Patients With Advanced Non-Small Cell Lung Cancer. \u003cem\u003eWorld J Oncol. \u003c/em\u003e2020;11(1):9-22.\u003c/li\u003e\n\u003cli\u003eRoch B, Coffy A, Jean-Baptiste S, et al. Cachexia - sarcopenia as a determinant of disease control rate and survival in non-small lung cancer patients receiving immune-checkpoint inhibitors. \u003cem\u003eLung Cancer\u003c/em\u003e. 2020;143:19-26. doi: 10.1016/j.lungcan.2020.03.003 \u003c/li\u003e\n\u003cli\u003eLi S, Liu Z, Ren Y, et al. Sarcopenia Was a Poor Prognostic Predictor for Patients With Advanced Lung Cancer Treated With Immune Checkpoint Inhibitors. \u003cem\u003eFront Nutr. \u003c/em\u003e2022;9:900823.\u003c/li\u003e\n\u003cli\u003eLi S, Wang T, Tong G, Li X, You D, Cong M. Prognostic Impact of Sarcopenia on Clinical Outcomes in Malignancies Treated With Immune Checkpoint Inhibitors: A Systematic Review and Meta-Analysis. \u003cem\u003eFront Oncol. \u003c/em\u003e2021;11:726257.\u003c/li\u003e\n\u003cli\u003eRen B, Shen J, Qian Y, Zhou T. Sarcopenia as a Determinant of the Efficacy of Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer: A Meta-Analysis. \u003cem\u003eNutr Cancer. \u003c/em\u003e2023;75(2):685-695.\u003c/li\u003e\n\u003cli\u003eWang J, Cao L, Xu S. Sarcopenia affects clinical efficacy of immune checkpoint inhibitors in non-small cell lung cancer patients: A systematic review and meta-analysis. \u003cem\u003eInt Immunopharmacol. \u003c/em\u003e2020;88:106907.\u003c/li\u003e\n\u003cli\u003eTao 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. \u003cem\u003eJ Cachexia Sarcopenia Muscle\u003c/em\u003e. Dec 2023;14(6):2591-2601. doi:10.1002/jcsm.13333 \u003c/li\u003e\n\u003cli\u003ePetrelli F, Cortellini A, Indini A, et al. Association of Obesity With Survival Outcomes in Patients With Cancer: A Systematic Review and Meta-analysis. \u003cem\u003eJAMA Network Open\u003c/em\u003e. 2021;4(3):e213520-e213520. doi:10.1001/jamanetworkopen.2021.3520 \u003c/li\u003e\n\u003cli\u003eBarbi J, Patnaik SK, Pabla S, et al. Visceral Obesity Promotes Lung Cancer Progression-Toward Resolution of the Obesity Paradox in Lung Cancer. \u003cem\u003eJ Thorac Oncol\u003c/em\u003e. Aug 2021;16(8):1333-1348. doi:10.1016/j.jtho.2021.04.020 \u003c/li\u003e\n\u003cli\u003eDonohoe CL, Doyle SL, Reynolds JV. Visceral adiposity, insulin resistance and cancer risk. \u003cem\u003eDiabetology \u0026amp; Metabolic Syndrome\u003c/em\u003e. 2011/06/22 2011;3(1):12. doi:10.1186/1758-5996-3-12 \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\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 demographics and characteristics (n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\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\u003eMale\u003c/b\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (54.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDate of initial cancer diagnosis to first scan date (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (62, 249)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDate of initial cancer diagnosis to date of checkpoint medication (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124.5 (71.0, 259.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDate of checkpoint medication to initial scan on (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-14 (-47,18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication name\u003c/b\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNivolumab infusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePembrolizumab infusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (93.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite blood cell\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0 (7.9, 13.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlbumin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8 (3.3, 3.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeutrophil\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.1 (5.0, 11.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymphocytes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 (0.7, 1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNeutrophil and lymphocyte ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.3 (3.4, 10.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStatus of Death\u003c/b\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (76.1%)\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*Data is summarized by median and interquartile range (IQR) unless otherwise noted.\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\u003eCT Scan Measurements\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;46)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkeletal muscle area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5 (0.3, 0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6 (0.4, 1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6 (0.2, 0.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral fat area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5 (0.2, 0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4 (0.2, 0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6 (0.3, 0.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubcutaneous fat area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.4 (0.4, 0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.4 (0.3, 0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5 (0.4, 0.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral and subcutaneous fat ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9 (0.6, 1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6 (0.5, 0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4 (0.9, 2.0)\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*Data is summarized by median and interquartile range (IQR) unless otherwise noted.\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\u003ea. Survival Model of CT scan measurements for females and males separately\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandardized Parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard Ratio\u003c/p\u003e \u003cp\u003e(95% Confidence Interval)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkeletal muscle area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09 (0.00, 111860.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral fat area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.78 (0.45, 362.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubcutaneous fat area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32 (0.01, 11.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays from checkpoint inhibitor to CT scan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49 (0.11, 2.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMale\u003c/em\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\u003eSkeletal muscle area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.20 (0.05, 5015.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral fat area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91 (0.18, 4.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubcutaneous fat area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17 (0.01, 4.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays from checkpoint inhibitor to CT scan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.23 (0.43, 3.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70\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*Included in the model: Skeletal muscle area, visceral fat area, subcutaneous fat area, days from checkpoint inhibitor to CT scan.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb. Survival Model of each CT scan measurement individually separately for females and males (Bivariate)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandardized Parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard Ratio\u003c/p\u003e \u003cp\u003e(95% Confidence Interval)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkeletal muscle area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81 (0.00, 1243.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral fat area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.31 (0.52, 10.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubcutaneous fat area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (0.30, 3.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral and subcutaneous fat ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.19 (0.35, 4.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMale\u003c/em\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\u003eSkeletal muscle area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.12 (0.02, 64.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral fat area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54 (0.15, 1.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubcutaneous fat area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32 (0.04, 2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral and subcutaneous fat ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.56, 1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73\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 \u003cb\u003e* Each measurement is put in the model separately, unadjusted.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ec. Survival Model for each CT scan measurement adjusted for time from checkpoint inhibitor to scan separately for females and males\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandardized Parameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard Ratio\u003c/p\u003e \u003cp\u003e(95% Confidence Interval)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkeletal muscle area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.28 (0.00, 801.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral fat area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.24 (0.63, 16.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubcutaneous fat area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.29 (0.32, 5.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral and subcutaneous fat ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.33 (0.37, 4.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMale\u003c/em\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\u003eSkeletal muscle area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.70 (0.02, 142.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral fat area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55 (0.15, 2.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubcutaneous fat area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.33 (0.04, 2.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral and subcutaneous fat ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.56, 1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73\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 \u003cb\u003e* Each measurement is adjusted for days from checkpoint inhibitor to CT scan.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe one and two-year rate of survival separately for the full model for females and males\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurvival Rate (95% Confidence Interval)\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\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1-year Survival Rate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e59.3% (35.7%, 98.5%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2-year Survival Rate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e27.3% (8.5%, 88.0%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1-year Survival Rate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e72.0% (51.3%, 100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2-year Survival Rate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e33.7% (12.4%, 91.9%)\u003c/b\u003e\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 \u003cb\u003e*Included in the model: Skeletal muscle area, visceral fat area, subcutaneous fat area, days from checkpoint inhibitor to CT scan.\u003c/b\u003e \u003c/p\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":"supportive-care-in-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jscc","sideBox":"Learn more about [Supportive Care in Cancer](https://www.springer.com/journal/520)","snPcode":"520","submissionUrl":"https://submission.nature.com/new-submission/520/3","title":"Supportive Care in Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"sarcopenia, visceral fat area, subcutaneous fat area, skeletal muscle area, checkpoint inhibitor therapy","lastPublishedDoi":"10.21203/rs.3.rs-5389970/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5389970/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe role of skeletal muscle area (SMA), subcutaneous, and visceral fat area (SFA and VFA) in cancer survivorship is inconsistent. We investigated the prognostic significance of the skeletal muscle index, subcutaneous and visceral fat area specifically via CT scans around the time of checkpoint inhibitor therapy in patients with non-small cell lung cancer (NSCLC).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eCT scans of patients within 60 days of checkpoint inhibitor medication use were utilized to assess skeletal muscle area visceral fat index (VFA), subcutaneous fat area (SFA), and visceral and subcutaneous fat ratio corrected by patients\u0026rsquo; height in meters squared. Skeletal muscle and fat areas at L3 were read by a single trained reader using TeraRecon software. Survival (in days) was calculated from the first CT scan to the death date. Survival analysis was performed using a Cox proportional hazards model to evaluate the association between body composition metrics and patient survival outcomes at one and two years. Multiple regression models were utilized with all CT parameters in a single model\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWith 46 patients included in the analysis, our results did not show a significant relationship between any parameters assessed (SMA, SFA, VFA, visceral and subcutaneous fat ratio, and days from checkpoint inhibitor therapy to initial scan) and cancer survivorship in either female or male patients.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eOur results demonstrate no significant relationship between the parameters assessed and NSCLC survivorship in either male or female patients, which is consistent with small studies. However, meta-analyses of multiple studies support the association of pre-immunotherapy with reduced survival.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePre-treatment Sarcopenia, SFA, and VFA do not appear to predict cancer survival on checkpoint inhibitors in small studies. Larger studies are needed to explore the utility of CT scan-derived SMI and fat area in predicting checkpoint inhibitor benefits in patients with lung cancer.\u003c/p\u003e","manuscriptTitle":"Does Pre-Checkpoint Inhibitor Sarcopenia, Visceral, or Subcutaneous Fat Predict Survival in Non-Small Cell Lung Cancer Patients? ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 06:10:25","doi":"10.21203/rs.3.rs-5389970/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-04T22:11:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-04T08:03:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"900357627197813437053250313676776189","date":"2025-03-04T01:20:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-23T15:19:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-23T15:17:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-12T08:08:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Supportive Care in Cancer","date":"2024-11-04T17:12:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"supportive-care-in-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jscc","sideBox":"Learn more about [Supportive Care in Cancer](https://www.springer.com/journal/520)","snPcode":"520","submissionUrl":"https://submission.nature.com/new-submission/520/3","title":"Supportive Care in Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b4efedf4-7e8c-493b-873f-0fcdf187213e","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T16:01:23+00:00","versionOfRecord":{"articleIdentity":"rs-5389970","link":"https://doi.org/10.1007/s00520-025-10243-z","journal":{"identity":"supportive-care-in-cancer","isVorOnly":false,"title":"Supportive Care in Cancer"},"publishedOn":"2025-12-19 15:57:43","publishedOnDateReadable":"December 19th, 2025"},"versionCreatedAt":"2024-12-17 06:10:25","video":"","vorDoi":"10.1007/s00520-025-10243-z","vorDoiUrl":"https://doi.org/10.1007/s00520-025-10243-z","workflowStages":[]},"version":"v1","identity":"rs-5389970","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5389970","identity":"rs-5389970","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}