A Nomogram Based on Body Composition and the Prognostic Nutritional Index to Predict Early Postoperative Complications of Colorectal Cancer

A Nomogram Based on Body Composition and the Prognostic Nutritional Index to Predict Early Postoperative Complications of Colorectal Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Nomogram Based on Body Composition and the Prognostic Nutritional Index to Predict Early Postoperative Complications of Colorectal Cancer Ning Zhu, Yan Liu, Hongqing Yu, Jingya Xu, Yi Wei, Jian Zhai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6437397/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective This study aimed to construct a nomogram based on body composition parameters and the prognostic nutritional index (PNI) using quantitative computed tomography (QCT) to predict early postoperative complications in patients with colorectal cancer (CRC). Materials and Methods We retrospectively analyzed the data of 157 patients who underwent radical resection for CRC between January 2019 and April 2024. All patients underwent QCT 1 month prior to surgery. Body composition was assessed at the level of the third lumbar vertebra, including measurements of the visceral fat area, subcutaneous fat area, and intramuscular fat infiltration (MFI) of the posterior vertebral muscles. The visceral-to-subcutaneous fat ratio (VSR) was calculated. Results Among the 157 patients, 31 (19.7%) experienced early postoperative complications. Univariate analysis revealed that the PNI, albumin level, VSR, and MFI were significantly associated with these complications. Multivariate logistic regression analysis identified the PNI (odds ratio [OR] = 0.801; 95% confidence interval (CI): 0.653–0.983), VSR (OR = 3.084; 95% CI: 1.365–6.968), and MFI (OR = 1.074; 95% CI: 1.009–1.145) as independent risk factors for early postoperative complications in CRC. The areas under the receiver operating characteristic curves for the PNI, VSR, MFI, and nomogram model for predicting postoperative complications were 0.796, 0.798, 0.648, and 0.879, respectively. Based on these three independent risk factors, the nomogram demonstrated good discrimination, calibration, goodness of fit, and clinical utility. Conclusions The nomogram model utilizing QCT-based body composition metrics and the PNI exhibited strong predictive capability for early postoperative complications in patients with CRC. colorectal cancer complications quantitative computed tomography prognostic nutritional index nomogram Figures Figure 1 Figure 2 Figure 3 Introduction Colorectal cancer (CRC) is one of the most prevalent malignant tumors of the digestive tract worldwide; its incidence and mortality have increased annually in China ( 1 ). Despite advancements in surgical techniques and chemoradiotherapy, the incidence of postoperative complications after colorectal surgery remains high due to the invasive surgical procedure and inter-patient differences. These poor postoperative outcomes can lead to reduced overall survival and prolonged hospital stay, negatively impacting patients’ physical, psychological, and economic well-being ( 2 ). Therefore, establishing a reliable model for predicting the postoperative complications of CRC is essential. Several factors influence the progression and prognosis of CRC. In addition to intraoperative variables, such as the surgery duration and technique, the tumor’s pathological characteristics play a significant role. The pathological stage and invasion degree of the tumor are independent risk factors for postoperative recurrence and are currently utilized for the clinical evaluation of patient prognosis ( 3 ). However, these pathological results can only be confirmed through surgical procedures, which can delay timely intervention. Preoperative malnutrition and reduced immune function are also associated with patient prognosis in gastrointestinal tumors; importantly, perioperative malnutrition is a significant risk factor for postoperative complications ( 4 ). Patients with CRC often show changes in body composition, characterized by increased visceral fat levels, decreased skeletal muscle mass, and heightened intermuscular fat infiltration. Conditions such as cachexia, marked by malnutrition, wasting, and immunodeficiency, are risk factors for poorer prognosis in patients with CRC ( 5 , 6 ). The prognostic nutritional index (PNI), an immune-inflammation indicator, is more sensitive than single serological markers, such as the C-reactive protein level or lymphocyte count, in detecting inflammation and predicting disease progression. In addition, the PNI serves as an indicator of nutritional status in patients with gastrointestinal tumors ( 7 ). A significant relationship has been identified between the preoperative PNI and the incidence of postoperative complications, making it a valuable tool for evaluating the prognosis and survival of patients with gastrointestinal malignancies ( 8 – 10 ). Quantitative computed tomography (QCT) offers unique advantages in assessing body composition, including bone, muscle, and abdominal fat. It has been widely applied for diagnosing conditions such as osteoporosis and fatty liver, and has been proven to be effective for quantitatively analyzing body composition in patients with cancer ( 11 ). Abdominal computed tomography (CT) is typically utilized for the preoperative evaluation of patients with cancer, allowing for body composition assessments without incurring additional costs or requiring extra examinations in patients with CRC. Accordingly, the objective of our study was to develop a nomogram prediction model based on preoperative QCT measurements of body composition and serological indicators, and to explore its predictive efficacy for early postoperative complications in patients with CRC. Our findings may provide a valuable reference for clinical practice. Materials and Methods Study participants In this retrospective study, we analyzed the clinical data of patients diagnosed with CRC at our hospital between January 2019 and April 2024. The inclusion criteria were as follows: ( 1 ) patients who had undergone radical resection of CRC at our hospital; ( 2 ) complete clinical and imaging data were available; ( 3 ) CRC was confirmed by postoperative pathology; ( 4 ) no preoperative radiotherapy or chemotherapy was received; ( 5 ) a whole abdominal CT scan was performed within 1 month prior to the operation; and ( 6 ) no other malignant tumors were present. The exclusion criteria were as follows: ( 1 ) patients who underwent preoperative chemoradiotherapy; ( 2 ) imaging did not clearly identify the relevant structures; ( 3 ) lack of complete clinical or pathological data; ( 4 ) fracture of the third lumbar vertebra; ( 5 ) presence of severe primary diseases, such as liver cirrhosis or renal failure; ( 6 ) patients who had undergone palliative resection or emergency surgery; and ( 7 ) patients who received preoperative radiotherapy or chemotherapy. Patient characteristics Using the hospital’s medical record system, we collected the following data: demographic characteristics, including age, sex, and body mass index (BMI); presence of smoking, diabetes, hypertension, coronary artery disease, and other underlying diseases; serum albumin, serum hemoglobin, peripheral blood lymphocyte count, serum tumor marker carcinoembryonic antigen, and carbohydrate antigen-199, which were measured 7 days before surgery. The PNI was defined as the serum albumin level (g/L) + 5× the peripheral blood lymphocyte count (10 9 /L). Tumor pathological characteristics included the maximum tumor diameter, tumor stage, and invasion depth. This study was conducted in compliance with the Helsinki Declaration and approved by the Scientific Research and New Technology institutional review board. The need for informed consent was waived due to the retrospective nature of the study. Postoperative laboratory findings, imaging, endoscopy biopsy, and medical history were consulted to ascertain the occurrence of complications. The short-term outcome was defined as complications within 30 days after surgery. Patients with grade ≥ 2 complications according to the Clavien–Dindo classification were included in the analysis ( 12 ). Target complications included abdominal infection, hemorrhage, abscess, incisional infection, anastomotic leakage, intestinal obstruction, pulmonary infection, respiratory failure, heart failure, myocardial infarction, cerebrovascular accident, lower-extremity thrombosis, and acute kidney injury. Scanning parameters and measurement methods A Philips 64-row CT scanner was used to scan the entire abdomen. Patients were in the supine position with their hands behind their head and the head advanced. The technical parameters were as follows: 120 kV tube voltage, 297 mA tube current, 1.375 cm pitch, 0.5/s tube speed, 50 × 50 cm field of view, 120 cm bed height, and a 512 × 512 matrix. Volume data from CT scans were sent back to the fourth-generation QCTPro analysis software (Mindways). The QCT phantom calibration was performed once a week to ensure measurement accuracy. QCTPro software was used to select the central level of the L3 vertebral body as the measurement point. Using a set threshold, the software automatically distinguished the fat area in the region of interest and obtained the subcutaneous fat area (SFA) in the abdomen, visceral fat area (VFA), total abdominal fat area, and visceral-to-subcutaneous fat ratio (VSR). The fat area (FA) and muscle area (MA) in the posterior vertebral muscles were obtained by drawing the ROI along the edge of the multifidus and erector spinae muscles. Muscle fat infiltration (MFI) of the posterior vertebral muscles was calculated using the following formula: MFI = [FA/(FA + MA)] × 100%. Averaged data from two measurements taken by the same examiner were included in the analysis (Fig. 1 ). Statistical analysis The Kolmogorov–Smirnov test was used to determine the normality of the quantitative data. Qualitative data are expressed as frequencies (percentages), and the chi-square test or Fisher’s exact test was used to compare the qualitative data between groups. An independent samples t-test was used for comparing normally-distributed quantitative data, and the Mann–Whitney U test for non-normally distributed data. Binary logistic multivariate analysis was performed to determine the independent risk factors, and the 95% confidence interval (CI) was calculated. The identified independent risk factors were included in the nomogram model. The predictive accuracy of the model was assessed using the time-dependent area under the receiver operating characteristic curve and concordance index (C-index). The Hosmer–Lemeshow test was used to determine the goodness of fit, and decision curve analysis was used to evaluate the clinical utility. Statistical significance was defined as a two-tailed p-value < 0.05. All statistical analyses were conducted using SPSS (version 26.0; IBM Corporation, Armonk, NY, USA) and R software (version 4.0.2; R Foundation). Results Characteristics of the enrolled patients Clinical data from 237 patients with CRC who underwent radical resection at our hospital between January 2019 and April 2024 were included in the initial analysis. None of these patients received preoperative radiotherapy or chemotherapy. Eighty patients were excluded because of inadequate clinical or imaging data, the existence of other tumors or severe primary disease, or suboptimal image quality. A detailed study flow chart of patient inclusion is shown in Fig. 2 . A final sample of 157 patients was included in the study, comprising 85 men and 72 women, with a mean age of 60.88 ± 9.32 years. Patients were divided into two groups: complications and non-complications. Thirty-one (19.7%) patients had grade ≥ 2 complications within 30 days after surgery, including anastomotic complications (leak, bleeding, and infection) in 7 patients, abdominal infection in 3 patients, pulmonary infection in 7 patients, postoperative blood transfusion in 5 patients, intestinal obstruction in 7 patients, and incision infection in 10 patients. Other complications included lower-extremity thrombosis, lymphatic leakage, acute kidney injury, and arrhythmia (one case each). Some patients had multiple concurrent complications. Significant differences in the PNI, albumin level, VSR, and MFI were observed between the complication and non-complication groups (p < 0.05). These variables were then included in the multivariate regression analysis. Table 1 shows the baseline characteristics of the patients in the two groups. Table 1 Comparison of the clinical characteristics of patients with colorectal cancer between the two groups Characteristics Complications Group n= ( 31 ) Non-complications Group n= (126) P Value Age, years 64.0 (56.0, 69.0) 60.5 (54.0, 68.0) 0.153 Sex, n (%) 0.263 male 14 (45.2) 71 (56.3) female 17 (54.8) 55 (43.7) BMI (kg/m 2 ) 23.2 ± 2.7 22.5 ± 2.9 0.231 Smoking, n (%) 28 (90.3) 28 (90.3) 0.795 Alcohol consumption, n (%) 3 (9.7) 18 (14.3) 0.768 Comorbidity, n (%) Diabetes 6 (19.4) 13 (10.3) 0.167 Hypertension 8 (25.8) 34 (27.0) 0.894 Hemoglobin (g/L) 114 (98, 131) 121 (108, 134) 0.202 PNI 40.9 ± 5.2 47.0 ± 5.2 < 0.001 Albumin (g/L) 34.2 ± 5.0 38.7 ± 4.0 < 0.001 VFA (cm 2 ) 165.7 (75.3, 221.5) 131.7 (75.7, 191.2) 0.116 SFA (cm 2 ) 91.4 (53.4, 110.1) 105.3 (59.3, 148.9) 0.072 TFA (cm 2 ) 268.2 (166.6, 328.4) 247.3 (137.9, 336.4) 0.846 VSR 2.0 (1.7, 2.2) 1.3 (1.0, 1.7) < 0.001 MFI (%) 15.1 (10.2, 17.8) 9.4 (5.2, 13.9) 0.011 CEA (ng/mL) 5.8 (2.7, 27.0) 3.8 (2.1, 10.5) 0.093 CA-199 (U/mL) 8.1 (4.2, 29.4) 8.1 (3.5, 16.8) 0.571 Operation method, n (%) 0.455 open 3 (9.7) 8 (6.3) laparoscopic 28 (90.3) 118 (93.7) Metastasis, n (%) 18 (58.1) 68 (54.0) 0.681 Vascular infiltration, n (%) 7 (22.6) 35 (27.8) 0.558 Mural invasion, n (%) 10 (32.3) 38 (30.2) 0.82 Tumor location, n (%) 0.524 rectum 16 (51.6) 57 (45.2) colon 15 (48.4) 69 (54.8) Tumor size (cm) 5 ( 4 , 5 ) 4 ( 3 , 5 ) 0.317 pT-stage, n (%) 0.541 1–2 4 (12.9) 22 (17.5) 3–4 27 (87.1) 104 (82.5) pN-stage, n (%) 0.815 N0 13 (41.9) 58 (46) N1 10 (32.3) 42 (33.3) N2 8 (25.8) 26 (20.6) Histologic type, n (%) 0.972 low-medium 26 (83.9) 106 (84.1) medium 5 (16.1) 20 (15.9) BMI, body mass index; PNI, prognostic nutritional index; VFA, visceral fat area; SFA, subcutaneous fat area; TFA, total abdominal fat area; VSR, visceral-to-subcutaneous fat ratio; MFI, intramuscular fat infiltration; CA19-9, cancer antigen 19 − 9; CEA, carcinoembryonic antigen; pT, pathological tumour; pN, pathological node Multivariate regression analysis Significant factors identified in the univariate analysis (albumin level, PNI, VSR, and MFI) were included in the multivariate analysis. The results indicated that the PNI (odds ratio [OR] = 0.801, p = 0.034), VSR (OR = 3.084, p = 0.007), and MFI (OR = 1.074, p = 0.026) were independent risk factors for early postoperative complications after CRC resection. Further details are presented in Table 2 . Table 2 Multivariate logistic regression analysis of short-term postoperative complications in patients with CRC ß value SE value Wald P value OR 95% CI Albumin -0.018 0.126 0.02 0.888 0.982 0.767ཞ1.258 PNI -0.222 0.104 4.497 0.034 0.801 0.653ཞ0.983 VSR 1.126 0.416 7.332 0.007 3.084 1.365ཞ6.968 MFI 0.072 0.032 4.948 0.026 1.074 1.009ཞ1.145 ß value, partial regression coefficient; SE value, standard error of partial regression coefficient; OR, odds ratio; CI, confidence interval; PNI, prognostic nutritional index; VSR, visceral-to-subcutaneous fat ratio; MFI, intramuscular fat infiltration; CRC, colorectal cancer Establishment of the nomogram model The independent risk factors identified in the multivariate analysis were used for constructing a prediction model (Fig. 3 a). The nomogram was automatically repeated 1000 times for internal validation with logistic regression, achieving a C-index of 0.879, which indicated good discriminatory ability. The Hosmer–Lemeshow test showed satisfactory agreement (p = 0.977). In the receiver operating characteristic analysis, the PNI, MFI, VSR, and nomogram model showed area under the curve values of 0.796 (95% CI: 0.71–0.881), 0.648 (95% CI: 0.531–0.765), 0.798 (95% CI: 0.711–0.885), and 0.879 (95% CI: 0.816–0.942), respectively (Fig. 3 b). Thus, the nomogram of combined factors demonstrated a higher predictive accuracy compared to each factor individually. The calibration curve confirmed good agreement between the predicted and actual outcomes (Fig. 3 c). Furthermore, our nomogram showed greater net benefit in predicting the optimal threshold probability of patient complications in the decision curve analysis (Fig. 3 d). Discussion In this study, the preoperative body composition of patients with CRC was measured using QCT; a high VSR and MFI, and a low PNI and albumin level were associated with early complications after radical resection. Among these, the VSR, MFI, and PNI were identified as independent risk factors for early postoperative complications. The nomogram prediction model based on the PNI, VSR, and MFI exhibited strong efficacy and can be used as a valuable risk assessment tool to help clinicians implement early intervention and reduce the incidence of complications. Currently, many nutritional and inflammation-based indicators are used to predict tumor prognosis, with the PNI frequently used to evaluate the prognosis and long-term survival of patients with gastrointestinal cancer ( 13 , 14 ). The PNI is a combined serologic index of the serum albumin concentration and peripheral lymphocyte count, which can reflect the nutritional and inflammatory status of patients with gastrointestinal cancer. Lymphocytes are a type of cell line with immune recognition functions. Low peripheral blood lymphocyte levels may reflect an insufficient immune response to tumors, creating a more favorable microenvironment for tumor recurrence and poorer outcomes ( 15 ). Hypoalbuminemia not only reflects malnutrition but also systemic inflammation arising from the body's resistance to malignant tumor invasion. Previous studies have shown that the serum albumin level is related to the prognosis and long-term survival of patients with gastrointestinal tumors, and hypoalbuminemia is one of the risk factors for poor prognosis ( 16 ). The current study found that the albumin level and PNI of patients with CRC who developed complications within 30 days postoperatively were remarkably decreased compared to those of patients without complications. Consistent with the results of previous studies, the PNI was found to be an independent risk factor for early postoperative complications in patients with CRC. However, the preoperative serum albumin level was not, which may be because serum albumin is not the most sensitive biochemical marker to predict acute changes in nutritional status ( 17 ). Considering that the present study focused on early postoperative complications, the limited timeframe might have affected albumin’s predictive utility. Sarcopenia is increasingly recognized as a factor influencing the long-term prognosis of patients with malignant tumors, and an increasing number of skeletal muscle evaluation indicators have been used to quantify patient prognosis ( 18 ). Most studies measured skeletal muscle density and calculated the skeletal muscle index to evaluate muscle quality ( 19 , 20 ). However, the main cause of reduced skeletal muscle density is muscle steatosis. Intermuscular fat infiltration can also be used to reflect muscle quality. Recent studies have indicated that a poor prognosis in CRC is associated with muscle steatosis, and the assessment of intermuscular fat infiltration may be better than a simple assessment of the muscle area for predicting poor outcomes in patients with CRC ( 21 – 23 ). Nie et al. used CT to quantitatively measure muscle parameters in patients with rectal cancer and found that a high intermuscular fat area and low skeletal muscle index were strongly associated with poor overall survival and were independent prognostic factors for disease free survival ( 22 ). However, few studies have investigated the relationship between fat infiltration in the L3 posterior vertebral muscle and postoperative complications in CRC. In the current study, a high MFI was associated with an increased incidence of early postoperative complications in patients with CRC (OR = 1.074, p = 0.026), consistent with the results of previous studies. Intermuscular adipose tissue is a unique regional fat; an increase in its level causes elevated secretion of various pro-inflammatory factors, leading to an increased systemic inflammatory response and insulin resistance. Consequently, the survival outcome of patients with malignant tumors is affected through various signaling pathways ( 24 ). An increase in intermuscular adipose tissue also leads to immune dysfunction and reduced physical function. Given that patients with cancer have muscle movement disorders, and metabolic and endocrine abnormalities, this ultimately accelerates the development of cancer cachexia ( 25 ). Overall, intermuscular fat infiltration is highly associated with the occurrence of postoperative complications in patients with CRC, and intermuscular fat infiltration can serve as a new sarcopenia marker that effectively reflects the skeletal muscle mass. Obesity is another key prognostic factor in CR, and although BMI is easy to measure, it does not fully capture the complexity of fat distribution. However, studies have shown that the distribution of adipose tissue in the body is more important. Adipose tissue is divided into visceral and subcutaneous types. Visceral fat may better explain the relationship between obesity and cancer than BMI ( 26 , 27 ). Visceral fat has more cells and innervation, contains more inflammatory and immune cells, has more glucocorticoids and androgen receptors, and is more metabolically active than subcutaneous fat. Increased visceral fat levels can lead to an abnormal inflammatory response, microvascular dysfunction, and other chronic damage ( 28 ). The VSR was found to be an independent risk factor for early postoperative complications in CRC in the current study, which is in agreement with the results of previous studies ( 29 , 30 ). Kim et al. quantitatively assessed the VFA and SFA on preoperative CT images of 987 patients with CRC, and reported that a higher SFA was associated with better survival outcomes ( 31 ). In our study, although the SFA in the complication group was lower and the VFA was higher than those in the non-complication group, there were no significant differences in visceral and subcutaneous fat volume between the two groups, which may be because the incidence of complications in the study population was low. Most previous studies have focused on predicting disease prognosis and outcomes using CT-based body composition measurements or clinical biochemical indicators. Recently, combining multiple indicators has been shown to improve the predictive performance over single-factor models. A nomogram can reflect the prognosis of patients in a more intuitive way, and clinicians can use it to quickly calculate the risk score for predicting patient outcomes based on different risk factors ( 32 , 33 ). In the present study, multivariate analysis identified three independent risk factors, which were used to construct a prediction model integrating nutritional, immune, and QCT body composition indicators. The combined model outperformed single-factor predictions and could help clinicians in assessing patients with CRC at high risk for early complications as well as offering valuable clinical insights. Our study has some limitations. First, it was a retrospective study with missing data for some patients with CRC with postoperative complications within 30 days, leading to a smaller sample size. Future studies should expand the sample size and use a multicenter approach. Furthermore, as this study included patients with both colon and rectal cancers, analyzing them separately could provide more targeted insights. Finally, relying on adipose tissue and muscle measurements at the L3 vertebral level may not fully represent overall body composition. In conclusion, the combination of preoperative body composition factors, specifically the VSR, MFI, and PNI measured by QCT, improves the prediction of early postoperative complications in patients with CRC. The developed nomogram model enables the early identification of at-risk patients and can facilitate timely nutritional support and personalized treatment strategies. Abbreviations BMI – Body Mass Index CA-199 – Carbohydrate Antigen 19-9 CEA – Carcinoembryonic Antigen CRC – Colorectal Cancer FA – fat area MA – muscle area MFI – intramuscular fat infiltration PNI – prognostic nutritional index QCT – quantitative computed tomography VFA – visceral fat area VSR – visceral-to-subcutaneous fat ratio Declarations Author Contribution Author contribution:Conceptualization:Ning Zhu.Data curation: Yan Liu,Ning Zhu.Formal analysis: Jingya Xu,Yi Wei.Investigation: all authors.Methodology: Hongqing Yu,Ning Zhu.Supervision: Jian Zhai.Writing-original draft: Ning Zhu.Writing-review & editing:all authors. Acknowledgement We would like to thank Editage (www.editage.co.kr) for English language editing. Data Availability The datasets generated or analyzed during the study are available from the corresponding author on reasonable request. References Zheng RS, Zhang SW, Sun KX, Chen R, Wang SM, Li L, et al. Cancer statistics in China, 2016. 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Bocca G, Mastoridis S, Yeung T, James DRC, Cunningham C. Visceral-to-subcutaneous fat ratio exhibits strongest association with early post-operative outcomes in patients undergoing surgery for advanced rectal cancer. Int J Colorectal Dis. 2022;37(8):1893-1900. Pacquelet B, Morello R, Pelage JP, Eid Y, Lebreton G, Alves A, et al. Abdominal adipose tissue quantification and distribution with CT: prognostic value for surgical and oncological outcome in patients with rectal cancer. Eur Radiol. 2022;32(9):6258-6269. Kim JM, Chung E, Cho ES, Lee JH, Shin SJ, Lee HS, et al. Impact of subcutaneous and visceral fat adiposity in patients with colorectal cancer. Clin Nutr. 2021;40(11):5631-5638. Tao C, Hong W, Yin P, Wu S, Fan L, Lei Z, et al. Nomogram Based on Body Composition and Prognostic Nutritional Index Predicts Survival After Curative Resection of Gastric Cancer. Acad Radiol. 2024;31(5):1940-1949. Yin X, Ma X, Sun P, Shen D, Tang Z. A novel nomogram based on inflammatory-nutritional biomarkers for gallbladder cancer after surgical resection. BMC Gastroenterol. 2024;24(1):289. Additional Declarations No competing interests reported. Supplementary Files FullTitlePage.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6437397","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":445685447,"identity":"aacfced1-f16f-4a3d-84d7-2afc41ff44b0","order_by":0,"name":"Ning Zhu","email":"","orcid":"","institution":"First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Ning","middleName":"","lastName":"Zhu","suffix":""},{"id":445685448,"identity":"8066cffa-0e92-42de-9237-4276cffa3877","order_by":1,"name":"Yan Liu","email":"","orcid":"","institution":"First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Liu","suffix":""},{"id":445685451,"identity":"95842221-dd0b-4dd7-9c5a-a81d6376aa05","order_by":2,"name":"Hongqing Yu","email":"","orcid":"","institution":"First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Hongqing","middleName":"","lastName":"Yu","suffix":""},{"id":445685452,"identity":"ae16ccdd-ccfb-4efd-b9d4-c9f8e242d383","order_by":3,"name":"Jingya Xu","email":"","orcid":"","institution":"First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jingya","middleName":"","lastName":"Xu","suffix":""},{"id":445685453,"identity":"292d2bff-2b10-4d76-93ff-aab9e52f78b5","order_by":4,"name":"Yi Wei","email":"","orcid":"","institution":"First Affiliated Hospital of Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Wei","suffix":""},{"id":445685454,"identity":"ba77bc36-57ce-4f7e-8ff3-05a22aff7047","order_by":5,"name":"Jian Zhai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYFCCAwzMDAwSzPzMzAcfkKZFsp0t2YBoe5hBhMF5HjMBopQbHDz++HNBjQW78WEGMwaGGptowloOnDEwnnFMgtnsMEPaA4ZjabkNRGhhSOZhA2s5bsDYcJgYLccfHOb5J8Fs3MzYJkGklgOGzbxtEswGzMxsxGmRPHDGmJm3T4JZ4jAbs0ECMX7huwEMMZ5vdcn8/ec/PvhQY0NYi8KNA2A6GUwmEFIOAvL9EFPtiFE8CkbBKBgFIxQAAM5mPi/hqojoAAAAAElFTkSuQmCC","orcid":"","institution":"First Affiliated Hospital of Wannan Medical College","correspondingAuthor":true,"prefix":"","firstName":"Jian","middleName":"","lastName":"Zhai","suffix":""}],"badges":[],"createdAt":"2025-04-13 05:38:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6437397/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6437397/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81198249,"identity":"fc338c20-b9f8-458f-a8e1-b3e96e25f733","added_by":"auto","created_at":"2025-04-23 10:46:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":258165,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flow chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6437397/v1/e44fb01d99cbfde881eae45c.png"},{"id":81198260,"identity":"b2fce010-411b-4d2e-a64a-cc513d465c4e","added_by":"auto","created_at":"2025-04-23 10:46:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":898903,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6437397/v1/d30d4354af9d7d0e852f3043.png"},{"id":81198261,"identity":"24252c62-e466-49c3-99c4-96d40586de84","added_by":"auto","created_at":"2025-04-23 10:46:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":195479,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictive nomogram model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Nomogram for predicting the early postoperative complications in patients with colorectal cancer.\u003c/p\u003e\n\u003cp\u003e(a) Receiver operating characteristic curves of the risk factors and a nomogram prediction model.\u003c/p\u003e\n\u003cp\u003e(c) Calibration curves of the nomogram model.\u003c/p\u003e\n\u003cp\u003e(d) Decision curve analysis of the nomogram model results.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6437397/v1/577166211e94e4f892d2e235.png"},{"id":81208427,"identity":"801d20d6-97de-472e-94f0-1381975788fc","added_by":"auto","created_at":"2025-04-23 12:47:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2195374,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6437397/v1/243037fc-e3eb-4645-93e8-ae1b36f3b81e.pdf"},{"id":81198251,"identity":"e9933caf-6fbb-4531-9292-d76b60afe7d9","added_by":"auto","created_at":"2025-04-23 10:46:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17682,"visible":true,"origin":"","legend":"","description":"","filename":"FullTitlePage.docx","url":"https://assets-eu.researchsquare.com/files/rs-6437397/v1/102fc053850303259364509f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Nomogram Based on Body Composition and the Prognostic Nutritional Index to Predict Early Postoperative Complications of Colorectal Cancer ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is one of the most prevalent malignant tumors of the digestive tract worldwide; its incidence and mortality have increased annually in China (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Despite advancements in surgical techniques and chemoradiotherapy, the incidence of postoperative complications after colorectal surgery remains high due to the invasive surgical procedure and inter-patient differences. These poor postoperative outcomes can lead to reduced overall survival and prolonged hospital stay, negatively impacting patients\u0026rsquo; physical, psychological, and economic well-being (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Therefore, establishing a reliable model for predicting the postoperative complications of CRC is essential.\u003c/p\u003e \u003cp\u003eSeveral factors influence the progression and prognosis of CRC. In addition to intraoperative variables, such as the surgery duration and technique, the tumor\u0026rsquo;s pathological characteristics play a significant role. The pathological stage and invasion degree of the tumor are independent risk factors for postoperative recurrence and are currently utilized for the clinical evaluation of patient prognosis (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). However, these pathological results can only be confirmed through surgical procedures, which can delay timely intervention.\u003c/p\u003e \u003cp\u003ePreoperative malnutrition and reduced immune function are also associated with patient prognosis in gastrointestinal tumors; importantly, perioperative malnutrition is a significant risk factor for postoperative complications (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Patients with CRC often show changes in body composition, characterized by increased visceral fat levels, decreased skeletal muscle mass, and heightened intermuscular fat infiltration. Conditions such as cachexia, marked by malnutrition, wasting, and immunodeficiency, are risk factors for poorer prognosis in patients with CRC (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The prognostic nutritional index (PNI), an immune-inflammation indicator, is more sensitive than single serological markers, such as the C-reactive protein level or lymphocyte count, in detecting inflammation and predicting disease progression. In addition, the PNI serves as an indicator of nutritional status in patients with gastrointestinal tumors (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). A significant relationship has been identified between the preoperative PNI and the incidence of postoperative complications, making it a valuable tool for evaluating the prognosis and survival of patients with gastrointestinal malignancies (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eQuantitative computed tomography (QCT) offers unique advantages in assessing body composition, including bone, muscle, and abdominal fat. It has been widely applied for diagnosing conditions such as osteoporosis and fatty liver, and has been proven to be effective for quantitatively analyzing body composition in patients with cancer (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Abdominal computed tomography (CT) is typically utilized for the preoperative evaluation of patients with cancer, allowing for body composition assessments without incurring additional costs or requiring extra examinations in patients with CRC. Accordingly, the objective of our study was to develop a nomogram prediction model based on preoperative QCT measurements of body composition and serological indicators, and to explore its predictive efficacy for early postoperative complications in patients with CRC. Our findings may provide a valuable reference for clinical practice.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eIn this retrospective study, we analyzed the clinical data of patients diagnosed with CRC at our hospital between January 2019 and April 2024. The inclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) patients who had undergone radical resection of CRC at our hospital; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) complete clinical and imaging data were available; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) CRC was confirmed by postoperative pathology; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) no preoperative radiotherapy or chemotherapy was received; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) a whole abdominal CT scan was performed within 1 month prior to the operation; and (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) no other malignant tumors were present. The exclusion criteria were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) patients who underwent preoperative chemoradiotherapy; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) imaging did not clearly identify the relevant structures; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) lack of complete clinical or pathological data; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) fracture of the third lumbar vertebra; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) presence of severe primary diseases, such as liver cirrhosis or renal failure; (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) patients who had undergone palliative resection or emergency surgery; and (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) patients who received preoperative radiotherapy or chemotherapy.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient characteristics\u003c/h3\u003e\n\u003cp\u003eUsing the hospital\u0026rsquo;s medical record system, we collected the following data: demographic characteristics, including age, sex, and body mass index (BMI); presence of smoking, diabetes, hypertension, coronary artery disease, and other underlying diseases; serum albumin, serum hemoglobin, peripheral blood lymphocyte count, serum tumor marker carcinoembryonic antigen, and carbohydrate antigen-199, which were measured 7 days before surgery. The PNI was defined as the serum albumin level (g/L)\u0026thinsp;+\u0026thinsp;5\u0026times; the peripheral blood lymphocyte count (10\u003csup\u003e9\u003c/sup\u003e/L). Tumor pathological characteristics included the maximum tumor diameter, tumor stage, and invasion depth. This study was conducted in compliance with the Helsinki Declaration and approved by the Scientific Research and New Technology institutional review board. The need for informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e \u003cp\u003ePostoperative laboratory findings, imaging, endoscopy biopsy, and medical history were consulted to ascertain the occurrence of complications. The short-term outcome was defined as complications within 30 days after surgery. Patients with grade\u0026thinsp;\u0026ge;\u0026thinsp;2 complications according to the Clavien\u0026ndash;Dindo classification were included in the analysis (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Target complications included abdominal infection, hemorrhage, abscess, incisional infection, anastomotic leakage, intestinal obstruction, pulmonary infection, respiratory failure, heart failure, myocardial infarction, cerebrovascular accident, lower-extremity thrombosis, and acute kidney injury.\u003c/p\u003e\n\u003ch3\u003eScanning parameters and measurement methods\u003c/h3\u003e\n\u003cp\u003eA Philips 64-row CT scanner was used to scan the entire abdomen. Patients were in the supine position with their hands behind their head and the head advanced. The technical parameters were as follows: 120 kV tube voltage, 297 mA tube current, 1.375 cm pitch, 0.5/s tube speed, 50 \u0026times; 50 cm field of view, 120 cm bed height, and a 512 \u0026times; 512 matrix. Volume data from CT scans were sent back to the fourth-generation QCTPro analysis software (Mindways). The QCT phantom calibration was performed once a week to ensure measurement accuracy.\u003c/p\u003e \u003cp\u003eQCTPro software was used to select the central level of the L3 vertebral body as the measurement point. Using a set threshold, the software automatically distinguished the fat area in the region of interest and obtained the subcutaneous fat area (SFA) in the abdomen, visceral fat area (VFA), total abdominal fat area, and visceral-to-subcutaneous fat ratio (VSR). The fat area (FA) and muscle area (MA) in the posterior vertebral muscles were obtained by drawing the ROI along the edge of the multifidus and erector spinae muscles. Muscle fat infiltration (MFI) of the posterior vertebral muscles was calculated using the following formula: MFI = [FA/(FA\u0026thinsp;+\u0026thinsp;MA)] \u0026times; 100%. Averaged data from two measurements taken by the same examiner were included in the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe Kolmogorov\u0026ndash;Smirnov test was used to determine the normality of the quantitative data. Qualitative data are expressed as frequencies (percentages), and the chi-square test or Fisher\u0026rsquo;s exact test was used to compare the qualitative data between groups. An independent samples t-test was used for comparing normally-distributed quantitative data, and the Mann\u0026ndash;Whitney U test for non-normally distributed data. Binary logistic multivariate analysis was performed to determine the independent risk factors, and the 95% confidence interval (CI) was calculated. The identified independent risk factors were included in the nomogram model. The predictive accuracy of the model was assessed using the time-dependent area under the receiver operating characteristic curve and concordance index (C-index). The Hosmer\u0026ndash;Lemeshow test was used to determine the goodness of fit, and decision curve analysis was used to evaluate the clinical utility. Statistical significance was defined as a two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All statistical analyses were conducted using SPSS (version 26.0; IBM Corporation, Armonk, NY, USA) and R software (version 4.0.2; R Foundation).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of the enrolled patients\u003c/h2\u003e \u003cp\u003eClinical data from 237 patients with CRC who underwent radical resection at our hospital between January 2019 and April 2024 were included in the initial analysis. None of these patients received preoperative radiotherapy or chemotherapy. Eighty patients were excluded because of inadequate clinical or imaging data, the existence of other tumors or severe primary disease, or suboptimal image quality. A detailed study flow chart of patient inclusion is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A final sample of 157 patients was included in the study, comprising 85 men and 72 women, with a mean age of 60.88\u0026thinsp;\u0026plusmn;\u0026thinsp;9.32 years. Patients were divided into two groups: complications and non-complications. Thirty-one (19.7%) patients had grade\u0026thinsp;\u0026ge;\u0026thinsp;2 complications within 30 days after surgery, including anastomotic complications (leak, bleeding, and infection) in 7 patients, abdominal infection in 3 patients, pulmonary infection in 7 patients, postoperative blood transfusion in 5 patients, intestinal obstruction in 7 patients, and incision infection in 10 patients. Other complications included lower-extremity thrombosis, lymphatic leakage, acute kidney injury, and arrhythmia (one case each). Some patients had multiple concurrent complications. Significant differences in the PNI, albumin level, VSR, and MFI were observed between the complication and non-complication groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These variables were then included in the multivariate regression analysis. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the baseline characteristics of the patients in the two groups.\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\u003eComparison of the clinical characteristics of patients with colorectal cancer between the two groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComplications Group n= (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-complications Group \u003c/p\u003e \u003cp\u003en= (126)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.0 (56.0, 69.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.5 (54.0, 68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (43.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (90.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (90.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e114 (98, 131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121 (108, 134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVFA (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165.7 (75.3, 221.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.7 (75.7, 191.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSFA (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.4 (53.4, 110.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105.3 (59.3, 148.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTFA (cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e268.2 (166.6, 328.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e247.3 (137.9, 336.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0 (1.7, 2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3 (1.0, 1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMFI (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.1 (10.2, 17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.4 (5.2, 13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.8 (2.7, 27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8 (2.1, 10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA-199 (U/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.1 (4.2, 29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.1 (3.5, 16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation method, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eopen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elaparoscopic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (90.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118 (93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastasis, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVascular infiltration, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMural invasion, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erectum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecolon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epT-stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (87.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (82.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epN-stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistologic type, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elow-medium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (83.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106 (84.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (16.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBMI, body mass index; PNI, prognostic nutritional index; VFA, visceral fat area; SFA, subcutaneous fat area; TFA, total abdominal fat area; VSR, visceral-to-subcutaneous fat ratio; MFI, intramuscular fat infiltration; CA19-9, cancer antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9; CEA, carcinoembryonic antigen; pT, pathological tumour; pN, pathological node\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultivariate regression analysis\u003c/h3\u003e\n\u003cp\u003eSignificant factors identified in the univariate analysis (albumin level, PNI, VSR, and MFI) were included in the multivariate analysis. The results indicated that the PNI (odds ratio [OR]\u0026thinsp;=\u0026thinsp;0.801, p\u0026thinsp;=\u0026thinsp;0.034), VSR (OR\u0026thinsp;=\u0026thinsp;3.084, p\u0026thinsp;=\u0026thinsp;0.007), and MFI (OR\u0026thinsp;=\u0026thinsp;1.074, p\u0026thinsp;=\u0026thinsp;0.026) were independent risk factors for early postoperative complications after CRC resection. Further details are presented in 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\u003eMultivariate logistic regression analysis of short-term postoperative complications in patients with CRC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026szlig; value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.767ཞ1.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.653ཞ0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.365ཞ6.968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.009ཞ1.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u0026szlig; value, partial regression coefficient; SE value, standard error of partial regression coefficient; OR, odds ratio; CI, confidence interval; PNI, prognostic nutritional index; VSR, visceral-to-subcutaneous fat ratio; MFI, intramuscular fat infiltration; CRC, colorectal cancer\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eEstablishment of the nomogram model\u003c/h3\u003e\n\u003cp\u003eThe independent risk factors identified in the multivariate analysis were used for constructing a prediction model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The nomogram was automatically repeated 1000 times for internal validation with logistic regression, achieving a C-index of 0.879, which indicated good discriminatory ability. The Hosmer\u0026ndash;Lemeshow test showed satisfactory agreement (p\u0026thinsp;=\u0026thinsp;0.977). In the receiver operating characteristic analysis, the PNI, MFI, VSR, and nomogram model showed area under the curve values of 0.796 (95% CI: 0.71\u0026ndash;0.881), 0.648 (95% CI: 0.531\u0026ndash;0.765), 0.798 (95% CI: 0.711\u0026ndash;0.885), and 0.879 (95% CI: 0.816\u0026ndash;0.942), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Thus, the nomogram of combined factors demonstrated a higher predictive accuracy compared to each factor individually. The calibration curve confirmed good agreement between the predicted and actual outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Furthermore, our nomogram showed greater net benefit in predicting the optimal threshold probability of patient complications in the decision curve analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, the preoperative body composition of patients with CRC was measured using QCT; a high VSR and MFI, and a low PNI and albumin level were associated with early complications after radical resection. Among these, the VSR, MFI, and PNI were identified as independent risk factors for early postoperative complications. The nomogram prediction model based on the PNI, VSR, and MFI exhibited strong efficacy and can be used as a valuable risk assessment tool to help clinicians implement early intervention and reduce the incidence of complications.\u003c/p\u003e \u003cp\u003eCurrently, many nutritional and inflammation-based indicators are used to predict tumor prognosis, with the PNI frequently used to evaluate the prognosis and long-term survival of patients with gastrointestinal cancer (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The PNI is a combined serologic index of the serum albumin concentration and peripheral lymphocyte count, which can reflect the nutritional and inflammatory status of patients with gastrointestinal cancer. Lymphocytes are a type of cell line with immune recognition functions. Low peripheral blood lymphocyte levels may reflect an insufficient immune response to tumors, creating a more favorable microenvironment for tumor recurrence and poorer outcomes (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Hypoalbuminemia not only reflects malnutrition but also systemic inflammation arising from the body's resistance to malignant tumor invasion. Previous studies have shown that the serum albumin level is related to the prognosis and long-term survival of patients with gastrointestinal tumors, and hypoalbuminemia is one of the risk factors for poor prognosis (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The current study found that the albumin level and PNI of patients with CRC who developed complications within 30 days postoperatively were remarkably decreased compared to those of patients without complications. Consistent with the results of previous studies, the PNI was found to be an independent risk factor for early postoperative complications in patients with CRC. However, the preoperative serum albumin level was not, which may be because serum albumin is not the most sensitive biochemical marker to predict acute changes in nutritional status (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Considering that the present study focused on early postoperative complications, the limited timeframe might have affected albumin\u0026rsquo;s predictive utility.\u003c/p\u003e \u003cp\u003eSarcopenia is increasingly recognized as a factor influencing the long-term prognosis of patients with malignant tumors, and an increasing number of skeletal muscle evaluation indicators have been used to quantify patient prognosis (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Most studies measured skeletal muscle density and calculated the skeletal muscle index to evaluate muscle quality (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, the main cause of reduced skeletal muscle density is muscle steatosis. Intermuscular fat infiltration can also be used to reflect muscle quality. Recent studies have indicated that a poor prognosis in CRC is associated with muscle steatosis, and the assessment of intermuscular fat infiltration may be better than a simple assessment of the muscle area for predicting poor outcomes in patients with CRC (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Nie et al. used CT to quantitatively measure muscle parameters in patients with rectal cancer and found that a high intermuscular fat area and low skeletal muscle index were strongly associated with poor overall survival and were independent prognostic factors for disease free survival (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). However, few studies have investigated the relationship between fat infiltration in the L3 posterior vertebral muscle and postoperative complications in CRC. In the current study, a high MFI was associated with an increased incidence of early postoperative complications in patients with CRC (OR\u0026thinsp;=\u0026thinsp;1.074, p\u0026thinsp;=\u0026thinsp;0.026), consistent with the results of previous studies.\u003c/p\u003e \u003cp\u003eIntermuscular adipose tissue is a unique regional fat; an increase in its level causes elevated secretion of various pro-inflammatory factors, leading to an increased systemic inflammatory response and insulin resistance. Consequently, the survival outcome of patients with malignant tumors is affected through various signaling pathways (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). An increase in intermuscular adipose tissue also leads to immune dysfunction and reduced physical function. Given that patients with cancer have muscle movement disorders, and metabolic and endocrine abnormalities, this ultimately accelerates the development of cancer cachexia (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Overall, intermuscular fat infiltration is highly associated with the occurrence of postoperative complications in patients with CRC, and intermuscular fat infiltration can serve as a new sarcopenia marker that effectively reflects the skeletal muscle mass.\u003c/p\u003e \u003cp\u003eObesity is another key prognostic factor in CR, and although BMI is easy to measure, it does not fully capture the complexity of fat distribution. However, studies have shown that the distribution of adipose tissue in the body is more important. Adipose tissue is divided into visceral and subcutaneous types. Visceral fat may better explain the relationship between obesity and cancer than BMI (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Visceral fat has more cells and innervation, contains more inflammatory and immune cells, has more glucocorticoids and androgen receptors, and is more metabolically active than subcutaneous fat. Increased visceral fat levels can lead to an abnormal inflammatory response, microvascular dysfunction, and other chronic damage (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The VSR was found to be an independent risk factor for early postoperative complications in CRC in the current study, which is in agreement with the results of previous studies (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Kim et al. quantitatively assessed the VFA and SFA on preoperative CT images of 987 patients with CRC, and reported that a higher SFA was associated with better survival outcomes (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). In our study, although the SFA in the complication group was lower and the VFA was higher than those in the non-complication group, there were no significant differences in visceral and subcutaneous fat volume between the two groups, which may be because the incidence of complications in the study population was low.\u003c/p\u003e \u003cp\u003eMost previous studies have focused on predicting disease prognosis and outcomes using CT-based body composition measurements or clinical biochemical indicators. Recently, combining multiple indicators has been shown to improve the predictive performance over single-factor models. A nomogram can reflect the prognosis of patients in a more intuitive way, and clinicians can use it to quickly calculate the risk score for predicting patient outcomes based on different risk factors (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). In the present study, multivariate analysis identified three independent risk factors, which were used to construct a prediction model integrating nutritional, immune, and QCT body composition indicators. The combined model outperformed single-factor predictions and could help clinicians in assessing patients with CRC at high risk for early complications as well as offering valuable clinical insights.\u003c/p\u003e \u003cp\u003eOur study has some limitations. First, it was a retrospective study with missing data for some patients with CRC with postoperative complications within 30 days, leading to a smaller sample size. Future studies should expand the sample size and use a multicenter approach. Furthermore, as this study included patients with both colon and rectal cancers, analyzing them separately could provide more targeted insights. Finally, relying on adipose tissue and muscle measurements at the L3 vertebral level may not fully represent overall body composition.\u003c/p\u003e \u003cp\u003eIn conclusion, the combination of preoperative body composition factors, specifically the VSR, MFI, and PNI measured by QCT, improves the prediction of early postoperative complications in patients with CRC. The developed nomogram model enables the early identification of at-risk patients and can facilitate timely nutritional support and personalized treatment strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI \u0026ndash; Body Mass Index\u003c/p\u003e\n\u003cp\u003eCA-199 \u0026ndash; Carbohydrate Antigen 19-9\u003c/p\u003e\n\u003cp\u003eCEA \u0026ndash; Carcinoembryonic Antigen\u003c/p\u003e\n\u003cp\u003eCRC \u0026ndash; Colorectal Cancer\u003c/p\u003e\n\u003cp\u003eFA \u0026ndash; fat area\u003c/p\u003e\n\u003cp\u003eMA \u0026ndash; muscle area\u003c/p\u003e\n\u003cp\u003eMFI \u0026ndash; intramuscular fat infiltration\u003c/p\u003e\n\u003cp\u003ePNI \u0026ndash; prognostic nutritional index\u003c/p\u003e\n\u003cp\u003eQCT \u0026ndash; quantitative computed tomography\u003c/p\u003e\n\u003cp\u003eVFA \u0026ndash; visceral fat area\u003c/p\u003e\n\u003cp\u003eVSR \u0026ndash; visceral-to-subcutaneous fat ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor contribution:Conceptualization:Ning Zhu.Data curation: Yan Liu,Ning Zhu.Formal analysis: Jingya Xu,Yi Wei.Investigation: all authors.Methodology: Hongqing Yu,Ning Zhu.Supervision: Jian Zhai.Writing-original draft: Ning Zhu.Writing-review \u0026amp; editing:all authors.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe would like to thank Editage (www.editage.co.kr) for English language editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated or analyzed during the study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZheng RS, Zhang SW, Sun KX, Chen R, Wang SM, Li L, et al. 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Nomogram Based on Body Composition and Prognostic Nutritional Index Predicts Survival After Curative Resection of Gastric Cancer. Acad Radiol. 2024;31(5):1940-1949.\u003c/li\u003e\n \u003cli\u003eYin X, Ma X, Sun P, Shen D, Tang Z. A novel nomogram based on inflammatory-nutritional biomarkers for gallbladder cancer after surgical resection. BMC Gastroenterol. 2024;24(1):289.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"colorectal cancer, complications, quantitative computed tomography, prognostic nutritional index, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-6437397/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6437397/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to construct a nomogram based on body composition parameters and the prognostic nutritional index (PNI) using quantitative computed tomography (QCT) to predict early postoperative complications in patients with colorectal cancer (CRC).\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eWe retrospectively analyzed the data of 157 patients who underwent radical resection for CRC between January 2019 and April 2024. All patients underwent QCT 1 month prior to surgery. Body composition was assessed at the level of the third lumbar vertebra, including measurements of the visceral fat area, subcutaneous fat area, and intramuscular fat infiltration (MFI) of the posterior vertebral muscles. The visceral-to-subcutaneous fat ratio (VSR) was calculated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 157 patients, 31 (19.7%) experienced early postoperative complications. Univariate analysis revealed that the PNI, albumin level, VSR, and MFI were significantly associated with these complications. Multivariate logistic regression analysis identified the PNI (odds ratio [OR]\u0026thinsp;=\u0026thinsp;0.801; 95% confidence interval (CI): 0.653\u0026ndash;0.983), VSR (OR\u0026thinsp;=\u0026thinsp;3.084; 95% CI: 1.365\u0026ndash;6.968), and MFI (OR\u0026thinsp;=\u0026thinsp;1.074; 95% CI: 1.009\u0026ndash;1.145) as independent risk factors for early postoperative complications in CRC. The areas under the receiver operating characteristic curves for the PNI, VSR, MFI, and nomogram model for predicting postoperative complications were 0.796, 0.798, 0.648, and 0.879, respectively. Based on these three independent risk factors, the nomogram demonstrated good discrimination, calibration, goodness of fit, and clinical utility.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe nomogram model utilizing QCT-based body composition metrics and the PNI exhibited strong predictive capability for early postoperative complications in patients with CRC.\u003c/p\u003e","manuscriptTitle":"A Nomogram Based on Body Composition and the Prognostic Nutritional Index to Predict Early Postoperative Complications of Colorectal Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-23 10:46:05","doi":"10.21203/rs.3.rs-6437397/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"acbfd833-390b-47bf-a612-bbab24afd334","owner":[],"postedDate":"April 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-23T12:38:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-23 10:46:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6437397","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6437397","identity":"rs-6437397","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}