Nutritional Risk Screening May Overlook Altered Body Composition in Hospitalized Cancer Patients: A Real-world Analysis Using Computed Tomography

Nutritional Risk Screening May Overlook Altered Body Composition in Hospitalized Cancer Patients: A Real-world Analysis Using Computed Tomography | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Nutritional Risk Screening May Overlook Altered Body Composition in Hospitalized Cancer Patients: A Real-world Analysis Using Computed Tomography Matheus de Souza Lima, Almir Galvão Vieira Bitencourt, Thais Manfrinato Miola This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8496612/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Purpose Nutritional screening tools are widely used in routine oncologic care to identify patients at nutritional risk. However, their ability to identify hospitalized patients with relevant alterations in body composition remains unclear. This study aimed to evaluate whether nutritional screening tools routinely used in clinical practice capture patients with CT-defined changes in muscle mass and quality. Methods This retrospective, cross-sectional study included hospitalized adult cancer patients treated at a tertiary cancer center in São Paulo, Brazil, between 2022 and 2023. Eligible patients underwent abdominal computed tomography (CT) and nutritional screening during hospitalization. Skeletal Muscle Index (SMI) and skeletal muscle density (SMD) were assessed at the third lumbar vertebra (L3) using CoreSlicer® software. Nutritional risk was evaluated using routinely implemented screening tools, and associations with CT-derived body composition parameters were analyzed. Results 272 patients were included. Low muscle mass was identified in 51.1% of patients and low muscle density in 52.2%, including individuals with normal or elevated body mass index. Nutritional risk identified by NRS-2002 was significantly associated with SMI but showed limited ability to identify CT-defined alterations when used in isolation. The CNS demonstrated high sensitivity but low specificity. Many patients with CT-detected alterations in muscle mass and quality were not classified as at nutritional risk by screening tools. Conclusion Widely used screening instruments may overlook clinically relevant alterations in body composition among patients. When imaging is already available as part of routine oncologic care, opportunistic CT-based assessment may complement screening and support more individualized supportive care interventions Cancer Malnutrition Sarcopenia Body composition Computed tomography Nutritional screening Figures Figure 1 INTRODUCTION Malnutrition and loss of skeletal muscle mass are highly prevalent among cancer patients and are consistently associated with poor prognosis, increased morbidity, greater treatment-related toxicity, and reduced quality of life. These alterations result from a complex interaction between tumor burden, systemic inflammation, and anticancer therapies. Up to 80% of patients experience weight loss or malnutrition during the disease course, and approximately 10–20% of cancer-related deaths are attributed to nutritional deterioration rather than tumor progression itself. Despite its clinical relevance, cancer-related malnutrition remains underdiagnosed and undertreated worldwide. 1 – 6 International guidelines recommend routine nutritional screening for all cancer patients as a strategy to identify individuals at risk and guide further assessment. The European Society for Clinical Nutrition and Metabolism (ESPEN) emphasizes that effective screening tools should be rapid, low-cost, sensitive, and specific, recommending validated instruments such as the Nutritional Risk Screening-2002 (NRS-2002). However, low muscle mass and impaired muscle quality are strongly associated with adverse outcomes and may remain undetected, particularly in patients with preserved or elevated body mass index. Computed tomography (CT) has emerged as a reference method for body composition assessment in oncology, with cross-sectional imaging at the third lumbar vertebra (L3) demonstrating strong correlation with whole-body muscle and adipose tissue. 7–12 Previous studies comparing nutritional screening tools with CT-derived muscle mass have demonstrated substantial discordance. In a cohort of 725 chemotherapy outpatients, up to 61% of patients with sarcopenia identified by CT were classified as having low nutritional risk by screening instruments. These findings underscore that while screening tools are essential to guide further evaluation, they were not designed to detect alterations in body composition, and no consensus exists regarding the optimal screening strategy in oncology. 13 In Brazil, nutritional screening tools are often the only method used to assess nutritional risk in hospitalized cancer patients, which may limit further evaluation when no risk is identified. In this context, it remains unclear whether screening instruments routinely used in clinical practice adequately identify patients with clinically relevant alterations in body composition. Therefore, this study aimed to evaluate the association between routinely implemented nutritional screening tools and CT-derived measures of muscle mass and muscle quality in hospitalized cancer patients. MATERIALS AND METHODS Clinical and clinicopathological data were obtained from the A.C. Camargo Cancer Center database for patients hospitalized between January 2022 and December 2023. Eligible participants were adults aged ≥ 18 years with a confirmed cancer diagnosis who had undergone abdominal CT within 30 days prior to admission, reflecting routine imaging intervals in oncologic care, and had documented nutritional screening during hospitalization. Patients admitted to the intensive care unit or receiving exclusive palliative care were excluded. In cases of multiple hospitalizations, only the first admission was considered. Nutritional risk was assessed using the Nutritional Risk Screening-2002 (NRS-2002) and the Nutritional Screening Tool (CNS), reflecting routine institutional practice. Body mass index (BMI) was calculated according to World Health Organization criteria for adults and Pan American Health Organization criteria for older adults. 14 Body composition was evaluated using a single axial CT slice at the inferior border of the third lumbar vertebra (L3), analyzed with CoreSlicer® software. Skeletal muscle area, including psoas, paravertebral, and abdominal wall muscles, was quantified using semi-automated segmentation and manually corrected when necessary. Skeletal muscle was identified within a range of − 29 to + 150 Hounsfield Units. Skeletal Muscle Index (SMI, cm²/m²) was calculated by normalizing muscle area to height. Muscle depletion was defined as SMI < 55 cm²/m² for men and < 39 cm²/m² for women. Mean skeletal muscle density (SMD) was measured using established sex-specific cut-offs.⁷,¹⁵ All CT analyses were performed by trained investigators following standardized protocols. STATISTICAL ANALYSIS Statistical analyses were conducted to assess associations between nutritional screening results and CT-derived body composition parameters. Continuous variables were compared using Student’s t-test or Mann–Whitney U test, as appropriate. For comparisons involving three or more groups, one-way ANOVA or Kruskal–Wallis tests were applied. Associations between categorical variables were analyzed using Pearson’s chi-square test or Fisher’s exact test. Correlations between continuous variables were evaluated using Pearson’s or Spearman’s coefficients. Statistical significance was defined as p < 0.05. RESULTS A total of 272 patients met eligibility criteria and consented to participate (Fig. 1 ). The cohort was predominantly female (59.6%), with a median age of 62 years The most common tumor sites were upper gastrointestinal (18.4%) and colorectal (12.5%). Most patients presented with non-metastatic disease (71.3%). Length of hospitalization ranged from 1 to 93 days (median: 6), and the majority of admissions were clinical (78.3%). Clinical and demographic characteristics are detailed in Table 1 . Table 1 – Clinical and demographic characteristics Variable Category N (%) Gender Male 110 (40,4) Female 162 (59,6) Age (years) Min-Max 20–93 Mean/Median 60,49 / 62 Tumor location Upper gastrointestinal 50 (18,4) Colorectal 34 (12,5) Breast 34 (12,5) Hematological 29 (10,7) Urological 28 (10,3) Gynecological 23 (8,5) Lung and chest 19 (7) Head and Neck 16 (5,9) Sarcoma and Bone Tumors 10 (3,7) Cutaneous 8 (2,9) Central nervous system 2 (0,7) Other 19 (7) Metastasis Yes 78 (28,7) No 194 (71,3) Reason for Hospitalization Clinical 213 (78,3) Surgical 59 (21,7) Length of Hospital Stay Min – Max 1–93 Mean / Median 9,58 / 6 Body weight showed wide variation, with median of 68kg. Accordingly, BMI ranged broadly, with a median of 24.9 kg/m². Skeletal muscle index (SMI) and skeletal muscle density (SMD) also varied considerably, with medians of 44.48 cm²/m² and 33.38 HU (Table 2 ). Table 2 – Values ​​of anthropometric parameters and Muscle Mass analysis by CT Variable Min-Max N (%) Weight (kg) 25–129 - BMI (kg/m2) 12,2–41,40 - BMI (Classification) Underweight/Malnutrition 58 (21,3) Normal range 111 (40,8) Overweight 53 (19,5) Obesity 50 (18,4) CT Muscle Area (cm2) 69,11–245,44 - SMI (cm2/m2) 26,01–81,45 - SMI (Classification) Low 139 (51,1) Normal 133 (48,9) SMD (HU) 1,5–56,35 33,11 / 33,38 SMD (Classification) Normal 130 (47,8) Low 142 (52,2) Based on BMI, most patients were classified as well nourished (40.8%). However, SMI assessment revealed muscle mass depletion in 51.1% of participants, and SMD analysis showed low skeletal density in 52.2%. Based on the combined NRS 2002 criteria, 55.1% of patients were classified as not at nutritional risk. According to CNS screening tool criteria, 73.2% of patients were classified as at nutritional risk. A significant association between SMI and nutritional risk was observed only with NRS 2002 (Table 3 ) Table 3 – Association between Muscle Mass Index and nutritional screening tools Variable Category SMI (N%) p Value Low Normal NRS 2002 Risk 73 (52,5%) 49 (36,8%) 0,009 No risk 66 (47,5%) 84 (63,2%) CNS Risk 106 (76,3%) 93 (69,9%) 0,239 No risk 33 (23,7%) 40 (30,1%) CNS screening demonstrated a sensitivity of 76%, specificity of 30%, positive predictive value (PPV) of 53%, negative predictive value (NPV) of 54%, and overall accuracy of 53% in relation to CT-defined low muscle mass. These findings suggest that while CNS is relatively effective in identifying patients at nutritional risk, its low specificity limits its ability to accurately rule out those not at risk, reducing its clinical utility when used in isolation. For NRS 2002, sensitivity was 52%, specificity 63%, PPV 59%, NPV 56%, and overall accuracy 57% when compared with CT-defined muscle depletion. Although moderately specific in identifying patients not at risk, its limited sensitivity and overall performance indicate that NRS 2002 should be applied with caution and preferably in combination with additional clinical or laboratory assessments. No significant association was found between skeletal muscle index (SMI) and skeletal muscle density (SMD) (p = 0.728). However, both NRS 2002 and CNS screening tools showed significant associations with SMD (p < 0.05) (Table 4 ). Table 4 – Association between DME with IMM and nutritional screening tools. Variable Category SMD (N%) p Value Low Normal SMI Low 74 (53.2) 65 (46,8) 0,728 Normal 68 (51,1) 65 (48,9) NRS 2002 Risk 80 (56,3) 42 (32,3) < 0,001 No risk 62 (43,7) 88 (67,7) CNS Risk 116 (81,7) 83 (63,8) 0,001 No risk 26 (18,3) 47 (36,2) Length of hospital stay did not differ significantly according to BMI (p 0,253), SMI (p 0,662), or SMD (0,127). In contrast, nutritional risk identified by both NRS 2002 and CNS screenings was significantly associated with longer hospitalization (p < 0.001). Patients classified as at risk by NRS 2002 had a mean stay of 13.02 days, compared to 6.69 days for those without risk. Similarly, CNS at-risk patients stayed an average of 11.11 days versus 5.44 days for non-risk patients DISCUSSION Malnutrition and muscle depletion are frequent and clinically relevant complications in cancer patients, adversely affecting treatment tolerance, functional status, and quality of life. In this real-world cohort of hospitalized oncology patients, CT-based assessment revealed a high prevalence of low muscle mass and impaired muscle quality, frequently occurring even in individuals with preserved or elevated BMI. These findings reinforce the limitations of BMI as an isolated marker of nutritional risk and highlight the need for more comprehensive assessment strategies. 7,8,16,17 . In this study, skeletal muscle depletion was observed in 51.1% of patients assessed by CT, and low skeletal muscle density (SMD) was found in 52.2%. These alterations persisted even among patients with normal or elevated BMI, highlighting the limitations of BMI as a sole marker of nutritional risk. Similarly, Miola et al. reported 73% muscle depletion in head and neck cancer patients evaluated by CT, with over half presenting normal BMI. A systematic review by Prado et al. demonstrated associations between low muscle mass and increased chemotherapy toxicity, reduced survival, and greater side effects. 7 , 18 , 19 . These findings reinforce the need to move beyond BMI as a standalone indicator, favoring more comprehensive approaches that incorporate direct measures of body composition, such as skeletal muscle mass and density CT imaging at the L3 vertebra is widely regarded as the reference method for body composition assessment in oncology research. This validated and reproducible method utilizes routinely available staging images without additional patient burden. In this study, CT was crucial for detecting alterations missed by clinical screening: 55% of patients with low muscle mass were not classified as “nutritionally at risk” by screening tools. Ní Bhuachalla et al. similarly found that 41% of sarcopenic patients identified by CT were missed by nutritional screening, and 38% had low muscle mass without significant weight loss, underscoring the difficulty in detecting these conditions via conventional measures. Our findings align with these data, as patients with impaired body composition often showed no changes in anthropometry or subjective criteria. 13,20 Regarding screening tools, NRS 2002 showed a significant association with SMI, moderate sensitivity, but only 57% overall accuracy. CNS demonstrated higher sensitivity but low specificity, suggesting risk of overestimation and false positives. Previous studies have questioned the NRS 2002’s ability to detect muscle mass alterations specifically, despite its validity for general nutritional risk identification. 21 , 22 , 23 , 24 . It is important to note that screening tools like NRS 2002 and CNS assess a combination of factors—including food intake, clinical status, and disease impact—not solely weight loss. This enables early identification of patients at risk for malnutrition or rapid nutritional decline, even before objective body composition changes occur. Overall, our results support international and national literature, indicating that tools such as NRS 2002 and CNS are valuable for flagging patients who require more detailed assessment but insufficient to detect all at-risk patients, especially those with altered body composition. CT-based body composition analysis emerges as a valuable complement to screening, enabling more precise clinical decision-making. In oncology centers where imaging is routinely available, this may represent an opportunity to enhance personalized nutritional care and improve clinical outcomes. From a supportive care perspective, failure to identify patients with altered body composition during hospitalization represents a missed opportunity for early nutritional intervention, symptom management, and functional preservation. Addressing this diagnostic gap may contribute to improved patient-centered care and more effective allocation of supportive resources in oncology settings. This study has limitations inherent to its retrospective and cross-sectional design. Multivariate analyses were not performed, and potential confounding factors such as age, tumor type, and disease stage may have influenced observed associations. Additionally, the single-center design and reliance on available CT scans may limit generalizability. Despite these limitations, the study provides clinically meaningful insights into routine nutritional assessment practices in a real-world oncology setting. CONCLUSION The findings of this study demonstrate that NRS 2002 is associated with computed tomography (CT)-based assessments of muscle mass; however, a substantial proportion of patients were classified as not being at nutritional risk despite exhibiting low muscle mass. These results suggest that, while screening instruments are valuable, they may fail to identify all individuals with clinically relevant alterations in body composition. This underscores the potential value of incorporating body composition assessment into clinical decision-making, particularly in settings where imaging is already available. Future research should investigate the application of accessible and cost-effective technologies for evaluating muscle mass in hospital settings, thereby facilitating the early detection of muscle depletion. Declarations ETHICS APPROVAL This study was conducted in accordance with the ethical guidelines for research involving human subjects established by the Brazilian National Health Council Resolution No. 466, dated December 12, 2012. All participants provided written informed consent prior to inclusion in the study, and their identities were kept confidential. The study protocol was submitted for review and approval by the Research Ethics Committee of the Fundação Antônio Prudente – AC Camargo Cancer Center. AUTHOR CONTRIBUTIONS All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Matheus de Souza Lima, Almir Galvão Vieira Bitencourt and Thais Manfrinato Miola. The first draft of the manuscript was written by Matheus de Souza Lima and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. DECLARATION OF INTEREST The authors declare that they have no conflicts of interest. DATA AVAILABILITY STATEMENT The data that support the findings of this study are available from AC Camargo Cancer Center, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of AC Camargo Cancer Center FUNDING STATEMENT This research received no financial supportfrom any funding agency in the public, commercial, or not-for-profit sectors. References Benoist S, Brouquet A. Dépistage de la dénutrition. J Chir Viscer. 2015;152(1):3–7. Muscaritoli M, Lucia S, Farcomeni A, Lorusso V, Saracino V, Barone C, et al. Prevalence of malnutrition in patients at first medical oncology visit: the PreMiO study. Oncotarget. 2017;8(45):79884–96. Arends J, Bachmann P, Baracos V, Barthelemy N, Bertz H, Bozzetti F, et al. ESPEN guidelines on nutrition in cancer patients. Clin Nutr. 2017;36(1):11–48. de Souza Gomes N, Maio R. Avaliação subjetiva global produzida pelo próprio paciente e indicadores de risco nutricional no paciente oncológico em quimioterapia. Rev Bras Cancerol. 2015;61(3):235–42. do Vale IAV, de Lima RAG, de Oliveira T, da Silva RF. Avaliação e indicação nutricional em pacientes oncológicos no início do tratamento quimioterápico. Rev Bras Cancerol. 2015;61(4):367–72. da Silva AC, da Silva Pinheiro L, Alves RC. As implicações da caquexia no câncer. e-Scientia. 2012;5(2):49–56. Miola TM. Avaliação da massa muscular pré-operatória com tomografia computadorizada em pacientes portadores de câncer de cabeça e pescoço [dissertação]. São Paulo: Universidade de São Paulo; 2021. Prado CM, Purcell SA, Laviano A. Sarcopenia and cachexia in the era of obesity: clinical and nutritional impact. Proc Nutr Soc. 2016;75(2):188–98. Horie LM, Barrére APN, de Brito D, Lima LCG, Gonzalez MC. Diretriz BRASPEN de terapia nutricional no paciente com câncer. BRASPEN J. 2019;34(2):180–210. Bardoscia, L., Besutti, G., Pellegrini, M., Pagano, M., Bonelli, C., Bonelli, E., Braglia, L., Cozzi, S., Roncali, M., Iotti, C., Pinto, C., Pattacini, P., & Ciammella, P. (2022). Impact of low skeletal muscle mass and quality on clinical outcomes in patients with head and neck cancer undergoing (chemo)radiation. Frontiers in nutrition , 9 , 994499. https://doi.org/10.3389/fnut.2022.994499 Besutti, G., Massaro, F., Bonelli, E., Braglia, L., Casali, M., Versari, A., Ligabue, G., Pattacini, P., Cavuto, S., Merlo, D. F., Luminari, S., Merli, F., Vaccaro, S., & Pellegrini, M. (2021). Prognostic Impact of Muscle Quantity and Quality and Fat Distribution in Diffuse Large B-Cell Lymphoma Patients. Frontiers in nutrition , 8 , 620696. https://doi.org/10.3389/fnut.2021.620696 Bastijns S, Alabadi B, Barbosa-Silva TG, Beaudart C, Brockhattingen K, Lamers S, Piotrowicz K, Welch C, Cock AM, Perkisas S. 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Muscaritoli M, Arends J, Bachmann P, Baracos V, Barthelemy N, Bertz H, et al. ESPEN practical guideline: Clinical Nutrition in cancer. Clin Nutr. 2021;40(5):2898–913 Prado CM, Purcell SA, Alish C, Pereira SL, Deutz NEP, Heyland DK, et al. Nutrition and exercise: potential to improve outcomes in cancer. Curr Oncol Rep. 2018;20(6):50. Carneiro IP, Mazurak VC, Prado CM. Clinical implications of sarcopenic obesity in cancer. Curr Oncol Rep. 2016;18(8):62. Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(1):16–31. Isenring E, Capra S, Bauer JD. Nutritional status and quality of life in patients receiving radiation therapy for cancers of the head and neck. Support Care Cancer. 2013;21(10):2811–8. Laky B, Janda M, Bauer J, Vavra C, Cleghorn G, Obermair A. Malnutrition among gynaecological cancer patients. Support Care Cancer. 2010;18(3):391–9. Weerink LBM, van der Hoorn A, van Leeuwen BL, de Bock GH, Witjes MJH, Ruurda JP, et al. Association between low skeletal muscle mass and postoperative mortality in cancer surgery: systematic review and meta-analysis. JAMA Surg. 2020;155(6):e196229. Achim V, Bash J, Mowery A, Guimaraes AR, Li R, Moore MS, et al. Prognostic indication of sarcopenia for wound complication and survival in laryngeal cancer. Otolaryngol Head Neck Surg. 2017;156(6):1020–6. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8496612","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":579625211,"identity":"219ea563-a6b5-4fc1-9fea-706b826a7ccc","order_by":0,"name":"Matheus de Souza Lima","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYBADOSBmg/MOgEkJnKoZGxgSGIwZGJihWtiI1JLYgKyFAZ8W/vbe4w9+/rBJX9t+/tiDj211eQb3ex8eYKixiWaQ7n2ATYvEmXOJjT0JabnbziSzG85sO1xscIzd4ADDsbTcBpnjBti0GEjkGDbwJBzO3XYgmU2at+1A4oZjbAwHGBsO5zZIpGF1GEhL45+Ew+lm5x+zSf9tqyNOSzPQlgSzG0BbGNuYCWuROHPGcLZMWprhthuPzSR7zh1OnHksjeFAAtAvbTLHcIRYj8HHNzY28mbnE59J/CirS+w7fIz5w4cam9x+6TasWnCABAbkxDAKRsEoGAWjgFQAAOzxZSETFg0CAAAAAElFTkSuQmCC","orcid":"","institution":"AC Camargo Cancer Center","correspondingAuthor":true,"prefix":"","firstName":"Matheus","middleName":"de Souza","lastName":"Lima","suffix":""},{"id":579625212,"identity":"262ea8ac-6e62-4878-a598-b0e3b2af60c3","order_by":1,"name":"Almir Galvão Vieira Bitencourt","email":"","orcid":"","institution":"AC Camargo Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Almir","middleName":"Galvão Vieira","lastName":"Bitencourt","suffix":""},{"id":579625213,"identity":"c3ac8ef2-307d-44ab-a9ec-aac374425b5f","order_by":2,"name":"Thais Manfrinato Miola","email":"","orcid":"","institution":"AC Camargo Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Thais","middleName":"Manfrinato","lastName":"Miola","suffix":""}],"badges":[],"createdAt":"2026-01-01 18:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8496612/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8496612/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101792799,"identity":"6ab197f0-dedb-46a6-ac9c-27dfedcf0ecd","added_by":"auto","created_at":"2026-02-03 16:15:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":192608,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant eligibility Flowchart\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8496612/v1/570cf186ec2345d346cb30e1.png"},{"id":101792800,"identity":"755fde21-83a7-4dfa-99d6-b7f61e8ed87f","added_by":"auto","created_at":"2026-02-03 16:15:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":646998,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8496612/v1/c3c70d9c-c257-4b0e-8277-116983ab8cc9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eNutritional Risk Screening May Overlook Altered Body Composition in Hospitalized Cancer Patients: A Real-world Analysis Using Computed Tomography\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMalnutrition and loss of skeletal muscle mass are highly prevalent among cancer patients and are consistently associated with poor prognosis, increased morbidity, greater treatment-related toxicity, and reduced quality of life. These alterations result from a complex interaction between tumor burden, systemic inflammation, and anticancer therapies. Up to 80% of patients experience weight loss or malnutrition during the disease course, and approximately 10\u0026ndash;20% of cancer-related deaths are attributed to nutritional deterioration rather than tumor progression itself. Despite its clinical relevance, cancer-related malnutrition remains underdiagnosed and undertreated worldwide.\u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e International guidelines recommend routine nutritional screening for all cancer patients as a strategy to identify individuals at risk and guide further assessment. The European Society for Clinical Nutrition and Metabolism (ESPEN) emphasizes that effective screening tools should be rapid, low-cost, sensitive, and specific, recommending validated instruments such as the Nutritional Risk Screening-2002 (NRS-2002). However, low muscle mass and impaired muscle quality are strongly associated with adverse outcomes and may remain undetected, particularly in patients with preserved or elevated body mass index. Computed tomography (CT) has emerged as a reference method for body composition assessment in oncology, with cross-sectional imaging at the third lumbar vertebra (L3) demonstrating strong correlation with whole-body muscle and adipose tissue. \u003csup\u003e7\u0026ndash;12\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePrevious studies comparing nutritional screening tools with CT-derived muscle mass have demonstrated substantial discordance. In a cohort of 725 chemotherapy outpatients, up to 61% of patients with sarcopenia identified by CT were classified as having low nutritional risk by screening instruments. These findings underscore that while screening tools are essential to guide further evaluation, they were not designed to detect alterations in body composition, and no consensus exists regarding the optimal screening strategy in oncology. \u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn Brazil, nutritional screening tools are often the only method used to assess nutritional risk in hospitalized cancer patients, which may limit further evaluation when no risk is identified. In this context, it remains unclear whether screening instruments routinely used in clinical practice adequately identify patients with clinically relevant alterations in body composition. Therefore, this study aimed to evaluate the association between routinely implemented nutritional screening tools and CT-derived measures of muscle mass and muscle quality in hospitalized cancer patients.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eClinical and clinicopathological data were obtained from the A.C. Camargo Cancer Center database for patients hospitalized between January 2022 and December 2023. Eligible participants were adults aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years with a confirmed cancer diagnosis who had undergone abdominal CT within 30 days prior to admission, reflecting routine imaging intervals in oncologic care, and had documented nutritional screening during hospitalization. Patients admitted to the intensive care unit or receiving exclusive palliative care were excluded. In cases of multiple hospitalizations, only the first admission was considered.\u003c/p\u003e \u003cp\u003eNutritional risk was assessed using the Nutritional Risk Screening-2002 (NRS-2002) and the Nutritional Screening Tool (CNS), reflecting routine institutional practice. Body mass index (BMI) was calculated according to World Health Organization criteria for adults and Pan American Health Organization criteria for older adults. \u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBody composition was evaluated using a single axial CT slice at the inferior border of the third lumbar vertebra (L3), analyzed with CoreSlicer\u0026reg; software. Skeletal muscle area, including psoas, paravertebral, and abdominal wall muscles, was quantified using semi-automated segmentation and manually corrected when necessary. Skeletal muscle was identified within a range of \u0026minus;\u0026thinsp;29 to +\u0026thinsp;150 Hounsfield Units. Skeletal Muscle Index (SMI, cm\u0026sup2;/m\u0026sup2;) was calculated by normalizing muscle area to height. Muscle depletion was defined as SMI\u0026thinsp;\u0026lt;\u0026thinsp;55 cm\u0026sup2;/m\u0026sup2; for men and \u0026lt;\u0026thinsp;39 cm\u0026sup2;/m\u0026sup2; for women. Mean skeletal muscle density (SMD) was measured using established sex-specific cut-offs.⁷,\u0026sup1;⁵ All CT analyses were performed by trained investigators following standardized protocols.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSTATISTICAL ANALYSIS\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted to assess associations between nutritional screening results and CT-derived body composition parameters. Continuous variables were compared using Student\u0026rsquo;s t-test or Mann\u0026ndash;Whitney U test, as appropriate. For comparisons involving three or more groups, one-way ANOVA or Kruskal\u0026ndash;Wallis tests were applied. Associations between categorical variables were analyzed using Pearson\u0026rsquo;s chi-square test or Fisher\u0026rsquo;s exact test. Correlations between continuous variables were evaluated using Pearson\u0026rsquo;s or Spearman\u0026rsquo;s coefficients. Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 272 patients met eligibility criteria and consented to participate (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The cohort was predominantly female (59.6%), with a median age of 62 years The most common tumor sites were upper gastrointestinal (18.4%) and colorectal (12.5%). Most patients presented with non-metastatic disease (71.3%). Length of hospitalization ranged from 1 to 93 days (median: 6), and the majority of admissions were clinical (78.3%). Clinical and demographic characteristics are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Clinical and demographic characteristics\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (40,4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162 (59,6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin-Max\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u0026ndash;93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean/Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60,49 / 62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eTumor location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpper gastrointestinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (18,4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColorectal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (12,5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (12,5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHematological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (10,7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (10,3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGynecological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (8,5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLung and chest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHead and Neck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (5,9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSarcoma and Bone Tumors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (3,7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCutaneous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (2,9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentral nervous system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0,7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMetastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (28,7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e194 (71,3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eReason for Hospitalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e213 (78,3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurgical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (21,7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLength of Hospital Stay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin \u0026ndash; Max\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u0026ndash;93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean / Median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,58 / 6\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\u003eBody weight showed wide variation, with median of 68kg. Accordingly, BMI ranged broadly, with a median of 24.9 kg/m\u0026sup2;. Skeletal muscle index (SMI) and skeletal muscle density (SMD) also varied considerably, with medians of 44.48 cm\u0026sup2;/m\u0026sup2; and 33.38 HU (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Values ​​of anthropometric parameters and Muscle Mass analysis by CT\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin-Max\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,2\u0026ndash;41,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eBMI (Classification)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnderweight/Malnutrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (21,3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111 (40,8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (19,5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (18,4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT Muscle Area (cm2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69,11\u0026ndash;245,44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMI (cm2/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26,01\u0026ndash;81,45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSMI (Classification)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139 (51,1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133 (48,9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMD (HU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,5\u0026ndash;56,35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33,11 / 33,38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSMD (Classification)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (47,8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142 (52,2)\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\u003eBased on BMI, most patients were classified as well nourished (40.8%). However, SMI assessment revealed muscle mass depletion in 51.1% of participants, and SMD analysis showed low skeletal density in 52.2%.\u003c/p\u003e \u003cp\u003eBased on the combined NRS 2002 criteria, 55.1% of patients were classified as not at nutritional risk. According to CNS screening tool criteria, 73.2% of patients were classified as at nutritional risk. A significant association between SMI and nutritional risk was observed only with NRS 2002 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Association between Muscle Mass Index and nutritional screening tools\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSMI (N%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNRS 2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (52,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (36,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (47,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (63,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (76,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93 (69,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (23,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (30,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCNS screening demonstrated a sensitivity of 76%, specificity of 30%, positive predictive value (PPV) of 53%, negative predictive value (NPV) of 54%, and overall accuracy of 53% in relation to CT-defined low muscle mass. These findings suggest that while CNS is relatively effective in identifying patients at nutritional risk, its low specificity limits its ability to accurately rule out those not at risk, reducing its clinical utility when used in isolation.\u003c/p\u003e \u003cp\u003eFor NRS 2002, sensitivity was 52%, specificity 63%, PPV 59%, NPV 56%, and overall accuracy 57% when compared with CT-defined muscle depletion. Although moderately specific in identifying patients not at risk, its limited sensitivity and overall performance indicate that NRS 2002 should be applied with caution and preferably in combination with additional clinical or laboratory assessments.\u003c/p\u003e \u003cp\u003eNo significant association was found between skeletal muscle index (SMI) and skeletal muscle density (SMD) (p\u0026thinsp;=\u0026thinsp;0.728). However, both NRS 2002 and CNS screening tools showed significant associations with SMD (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Association between DME with IMM and nutritional screening tools.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSMD (N%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (46,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (51,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (48,9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNRS 2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (56,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (32,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (43,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88 (67,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116 (81,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (63,8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (18,3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (36,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLength of hospital stay did not differ significantly according to BMI (p 0,253), SMI (p 0,662), or SMD (0,127). In contrast, nutritional risk identified by both NRS 2002 and CNS screenings was significantly associated with longer hospitalization (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients classified as at risk by NRS 2002 had a mean stay of 13.02 days, compared to 6.69 days for those without risk. Similarly, CNS at-risk patients stayed an average of 11.11 days versus 5.44 days for non-risk patients\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eMalnutrition and muscle depletion are frequent and clinically relevant complications in cancer patients, adversely affecting treatment tolerance, functional status, and quality of life. In this real-world cohort of hospitalized oncology patients, CT-based assessment revealed a high prevalence of low muscle mass and impaired muscle quality, frequently occurring even in individuals with preserved or elevated BMI. These findings reinforce the limitations of BMI as an isolated marker of nutritional risk and highlight the need for more comprehensive assessment strategies. \u003csup\u003e7,8,16,17\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, skeletal muscle depletion was observed in 51.1% of patients assessed by CT, and low skeletal muscle density (SMD) was found in 52.2%. These alterations persisted even among patients with normal or elevated BMI, highlighting the limitations of BMI as a sole marker of nutritional risk. Similarly, Miola et al. reported 73% muscle depletion in head and neck cancer patients evaluated by CT, with over half presenting normal BMI. A systematic review by Prado et al. demonstrated associations between low muscle mass and increased chemotherapy toxicity, reduced survival, and greater side effects.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. These findings reinforce the need to move beyond BMI as a standalone indicator, favoring more comprehensive approaches that incorporate direct measures of body composition, such as skeletal muscle mass and density\u003c/p\u003e \u003cp\u003eCT imaging at the L3 vertebra is widely regarded as the reference method for body composition assessment in oncology research. This validated and reproducible method utilizes routinely available staging images without additional patient burden. In this study, CT was crucial for detecting alterations missed by clinical screening: 55% of patients with low muscle mass were not classified as \u0026ldquo;nutritionally at risk\u0026rdquo; by screening tools. N\u0026iacute; Bhuachalla et al. similarly found that 41% of sarcopenic patients identified by CT were missed by nutritional screening, and 38% had low muscle mass without significant weight loss, underscoring the difficulty in detecting these conditions via conventional measures. Our findings align with these data, as patients with impaired body composition often showed no changes in anthropometry or subjective criteria. \u003csup\u003e13,20\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eRegarding screening tools, NRS 2002 showed a significant association with SMI, moderate sensitivity, but only 57% overall accuracy. CNS demonstrated higher sensitivity but low specificity, suggesting risk of overestimation and false positives. Previous studies have questioned the NRS 2002\u0026rsquo;s ability to detect muscle mass alterations specifically, despite its validity for general nutritional risk identification.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt is important to note that screening tools like NRS 2002 and CNS assess a combination of factors\u0026mdash;including food intake, clinical status, and disease impact\u0026mdash;not solely weight loss. This enables early identification of patients at risk for malnutrition or rapid nutritional decline, even before objective body composition changes occur.\u003c/p\u003e \u003cp\u003eOverall, our results support international and national literature, indicating that tools such as NRS 2002 and CNS are valuable for flagging patients who require more detailed assessment but insufficient to detect all at-risk patients, especially those with altered body composition. CT-based body composition analysis emerges as a valuable complement to screening, enabling more precise clinical decision-making. In oncology centers where imaging is routinely available, this may represent an opportunity to enhance personalized nutritional care and improve clinical outcomes.\u003c/p\u003e \u003cp\u003eFrom a supportive care perspective, failure to identify patients with altered body composition during hospitalization represents a missed opportunity for early nutritional intervention, symptom management, and functional preservation. Addressing this diagnostic gap may contribute to improved patient-centered care and more effective allocation of supportive resources in oncology settings.\u003c/p\u003e \u003cp\u003eThis study has limitations inherent to its retrospective and cross-sectional design. Multivariate analyses were not performed, and potential confounding factors such as age, tumor type, and disease stage may have influenced observed associations. Additionally, the single-center design and reliance on available CT scans may limit generalizability. Despite these limitations, the study provides clinically meaningful insights into routine nutritional assessment practices in a real-world oncology setting.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe findings of this study demonstrate that NRS 2002 is associated with computed tomography (CT)-based assessments of muscle mass; however, a substantial proportion of patients were classified as not being at nutritional risk despite exhibiting low muscle mass. These results suggest that, while screening instruments are valuable, they may fail to identify all individuals with clinically relevant alterations in body composition. This underscores the potential value of incorporating body composition assessment into clinical decision-making, particularly in settings where imaging is already available.\u003c/p\u003e \u003cp\u003eFuture research should investigate the application of accessible and cost-effective technologies for evaluating muscle mass in hospital settings, thereby facilitating the early detection of muscle depletion.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical guidelines for research involving human subjects established by the Brazilian National Health Council Resolution No. 466, dated December 12, 2012. All participants provided written informed consent prior to inclusion in the study, and their identities were kept confidential. The study protocol was submitted for review and approval by the Research Ethics Committee of the Funda\u0026ccedil;\u0026atilde;o Ant\u0026ocirc;nio Prudente \u0026ndash; AC Camargo Cancer Center.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Matheus de Souza Lima, Almir Galv\u0026atilde;o Vieira Bitencourt and Thais Manfrinato Miola. The first draft of the manuscript was written by Matheus de Souza Lima and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eDECLARATION OF INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from AC Camargo Cancer Center, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of AC Camargo Cancer Center\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no financial supportfrom any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBenoist S, Brouquet A. D\u0026eacute;pistage de la d\u0026eacute;nutrition. J Chir Viscer. 2015;152(1):3\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuscaritoli M, Lucia S, Farcomeni A, Lorusso V, Saracino V, Barone C, et al. Prevalence of malnutrition in patients at first medical oncology visit: the PreMiO study. Oncotarget. 2017;8(45):79884\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArends J, Bachmann P, Baracos V, Barthelemy N, Bertz H, Bozzetti F, et al. 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Otolaryngol Head Neck Surg. 2017;156(6):1020\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Cancer, Malnutrition, Sarcopenia, Body composition, Computed tomography, Nutritional screening","lastPublishedDoi":"10.21203/rs.3.rs-8496612/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8496612/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003e Nutritional screening tools are widely used in routine oncologic care to identify patients at nutritional risk. However, their ability to identify hospitalized patients with relevant alterations in body composition remains unclear. This study aimed to evaluate whether nutritional screening tools routinely used in clinical practice capture patients with CT-defined changes in muscle mass and quality.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective, cross-sectional study included hospitalized adult cancer patients treated at a tertiary cancer center in S\u0026atilde;o Paulo, Brazil, between 2022 and 2023. Eligible patients underwent abdominal computed tomography (CT) and nutritional screening during hospitalization. Skeletal Muscle Index (SMI) and skeletal muscle density (SMD) were assessed at the third lumbar vertebra (L3) using CoreSlicer\u0026reg; software. Nutritional risk was evaluated using routinely implemented screening tools, and associations with CT-derived body composition parameters were analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e272 patients were included. Low muscle mass was identified in 51.1% of patients and low muscle density in 52.2%, including individuals with normal or elevated body mass index. Nutritional risk identified by NRS-2002 was significantly associated with SMI but showed limited ability to identify CT-defined alterations when used in isolation. The CNS demonstrated high sensitivity but low specificity. Many patients with CT-detected alterations in muscle mass and quality were not classified as at nutritional risk by screening tools.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWidely used screening instruments may overlook clinically relevant alterations in body composition among patients. When imaging is already available as part of routine oncologic care, opportunistic CT-based assessment may complement screening and support more individualized supportive care interventions\u003c/p\u003e","manuscriptTitle":"Nutritional Risk Screening May Overlook Altered Body Composition in Hospitalized Cancer Patients: A Real-world Analysis Using Computed Tomography","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 16:14:59","doi":"10.21203/rs.3.rs-8496612/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-13T07:42:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T22:02:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114660660050948112896934647889987827141","date":"2026-03-02T22:19:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-21T08:55:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-17T02:26:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-24T02:07:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Supportive Care in Cancer","date":"2026-01-01T18:00:47+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":"f37c2192-6214-4b88-a9b2-b423bc3b307a","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T16:39:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 16:14:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8496612","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8496612","identity":"rs-8496612","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}