Laparoscopic Radical Resection as an Independent Favorable Prognostic Factor for Stage I-III Colon Cancer: A Propensity Score-Matched Study

Laparoscopic Radical Resection as an Independent Favorable Prognostic Factor for Stage I-III Colon Cancer: A Propensity Score-Matched Study | 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 Laparoscopic Radical Resection as an Independent Favorable Prognostic Factor for Stage I-III Colon Cancer: A Propensity Score-Matched Study yansong xu, Hui Li, Fangfang Liang, Huage Zhong, Ruiying Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6678114/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 Background‌: Surgical approach selection critically impacts postoperative morbidity and long-term oncologic outcomes in colorectal cancer management. This study aimed to compare survival benefits between laparoscopic and open radical resection for stage I-III colon cancer, while establishing a validated prognostic prediction model. ‌Methods: ‌Patients with colon cancer who underwent surgery at our hospital were researched in this retrospective study. Propensity score matching (PSM) was used to minimize the preoperative baseline variables. The clinical and pathological data between open and laparoscopic surgery were compared, and the effect of factors on overall survival (OS) was analyzed by the Cox proportional hazard model. Then, a personalized nomogram to predict the patient's prognosis was constructed. Results: A total of 324 colon cancer samples were selected by PSM. Patients in the laparoscopic group had a higher number of lymph node dissections, fewer blood transfusions, and fewer postoperative complications (P<0.05). The 3- and 5-year OS rates were 72% and 60% in the open group, and 82% and 76.8% in the laparoscopic group, respectively (P< 0.05). The preoperative CEA level, pathological T (pT) stage, differentiation, operation mode, lymph node metastasis (LNM), postoperative chemotherapy, and perineural invasion (PNI) were independent predictors of survival (P<0.05). A prognostic model based on these seven factors was constructed. The final nomogram showed excellent discrimination (C=0.796) for OS. ‌Conclusion: Laparoscopic resection demonstrates superior long-term survival compared to open surgery in localized colon cancer. The developed nomogram provides clinically valuable prognostic stratification, potentially guiding postoperative surveillance . Colorectal cancer Laparascopic resection Propensity Score-Matched Study Figures Figure 1 Figure 2 Figure 3 Introduction Colorectal cancer (CRC) represents a major global health burden, ranking as the third most commonly diagnosed malignancy and the second leading cause of cancer-related mortality worldwide[ 1 ]. While advancements in multimodal therapies including targeted agents and immunotherapies have transformed CRC management, radical surgical resection remains the cornerstone of curative treatment[ 2 ]. The evolution of minimally invasive techniques in CRC surgery commenced with Jacobs' pioneering laparoscopic colectomy in 1991[ 3 ]. Subsequent randomized controlled trials (RCTs) have consistently validated the safety profile and short-term advantages of laparoscopic approaches, particularly regarding accelerated postoperative recovery[ 4 – 7 ]. Nevertheless, persistent controversies exist concerning long-term oncological equivalence between laparoscopic and open techniques[ 8 , 9 ]. Two critical factors may explain this discrepancy: First, the learning curve phenomenon in early laparoscopic adoption. Initial series demonstrated suboptimal lymph node yields during the formative period of minimally invasive surgery (MIS)[ 11 , 12 ], a critical prognostic determinant in CRC management[ 10 ]. Second, the technical refinement of modern laparoscopic complete mesocolic excision (CME) with D3 lymphadenectomy now enables meticulous dissection of metastatic lymph nodes[ 13 ]. This raises a pivotal clinical question: Can contemporary laparoscopic expertise translate into superior long-term survival compared to conventional open resection? To address these controversies, we developed a prognostic nomogram incorporating 3- and 5-year overall survival (OS) predictors for stage I-III colon cancer patients. Recognizing the inherent limitations of observational studies, particularly selection bias and confounding variables, we implemented propensity score matching (PSM) analysis to ensure balanced baseline characteristics between surgical cohorts. This methodological rigor enhances the validity of comparative long-term outcome assessments. Materials and methods Database and candidate variables The study cohort was retrospectively identified from the Gastrointestinal Surgery Database of Guangxi Medical University Affiliated Cancer Hospital (2010–2020), comprising patients undergoing curative-intent colectomy with histologically confirmed stage I-III adenocarcinoma. Tumor staging adhered to AJCC/UICC TNM 8th edition criteria, verified by two independent pathologists. Inclusion Criteria: ①Pathologically confirmed colonic adenocarcinoma; ②R0 resection with complete mesocolic excision; ③≥3 months postoperative follow-up; ④No neoadjuvant therapy history. Exclusion Criteria: ①Emergency/palliative procedures; ②Prior malignancy history; ③Incomplete clinical biomarker profiles. The analytical framework incorporated: ①Demographics: Age, gender, BMI, ECOG status; ③Tumor characteristics: TNM stage, differentiation grade, lymphovascular/perineural invasion; ③Surgical parameters: Operative approach (laparoscopic vs. open), lymph node yield, transfusion requirement.Follow-up was conducted through multiple modalities including telephone interviews, outpatient clinic visits, and WeChat-based communications. The Institutional Review Board of First Affiliated Hospital of Guangxi Medical University approved this study, waiving informed consent under retrospective design per Declaration of Helsinki provisions. Univariate and multivariate analysis The following variables for univariate associations with overall survival (OS) were analyzed: (1) clinical and pathological data: age, sex, body mass index (BMI), Tumor location, postoperative chemotherapy, number of lymph nodes dissected (LND), operation mode, intestinal obstruction, pathology T (pT) stage, lymph node metastasis (LNM), lymphovascular invasion (LVI), perineural invasion (PNI), postoperative complications, blood transfusion; (2) laboratory markers: CEA, platelets to lymphocytes (PLR), neutrophils to lymphocytes (NLR). Finally, the following variables, including CEA, pT stage, differentiation, postoperative chemotherapy, operation mode, LNM, and PNI showing statistical significance at a p value of less than 0.05, were subjected to multivariable modeling. Model construction and validation Multivariate Cox proportional hazards models of OS were formulated from all variables and two-way interactions, showing statistically significant correlations with their respective endpoints. If a variable's influence has a clinical difference in the level of interaction, it will reach clinical significance. The final model, including all significant and pairwise interactions, was still statistically significant (P < 0.05) and clinically significant after a backward stepwise method. Based on the final model, a nomogram of 3-year and 5-year OS probability was constructed with the R software package. In internal calibration plots, points parallel to the reference line represent the covariates' similar prediction results in the training. Statistical analysis PSM was applied to achieve a balanced exposure group at baseline (including age, sex, BMI, tumor location), in accordance with the recommendations by Lonjon et al.[ 13 ] After PSM, we followed the methods of Yansong Xu et al[ 14 ]. Clinical and pathological outcomes were compared between the laparoscopic group and the open group. The Kaplan‒Meier method was used for OS by IBM SPSS 26.0 software (version 26.0; SPSS, Chicago, IL). Statistical significance was set at 0.05. We developed the prognostic model with univariate evaluation of the significance of each factor. Next, multivariate analyses were performed using the Cox proportional hazards model. Univariate predictive variables with P < 0.05 were applied to multivariate analyses to identify the independent prognostic factors. Nomograms and calibration plots were constructed using R software, version 3.3.3 (CRAN; R Foundation for Statistical Computing, Vienna, Austria). Results Clinical samples A total of 557 samples were included in this study, including 387 samples in the laparoscopic group and 170 samples in the open group. Initially, we found no difference between the two groups concerning sex, BMI, PLR, NLR, size of tumor, tumor differentiation, pT stage, LNM, postoperative complications and blood transfusion. However, there were significant differences in CEA, intestinal obstruction, LNDs, LVI, PNI and postoperative chemotherapy between the two groups (P < 0.05). In order to ensure the consistency of the two groups as far as possible, we used PSM analysis to re-screen the baseline data. Finally, the remaining 324 samples were included in the study, including 162 samples in the laparoscopic group and 162 samples in the open group. There was a significant difference in intestinal obstruction, pT stage, blood transfusion, and postoperative complications (P < 0.05) (Table 1 ). Table 1 Demographic and pathologic data before and after propensity score matching Variables Before PSM N = 557 After PSM N = 324 OG LG P value OG LG P value Sex 0.567 1.000 Male 113 239 108 108 Female 57 148 54 54 BMI (kg/㎡), mean 22 21 0.213 23 22 0.334 Age (years) mean 59 59 0.702 58 58.1 0.621 Location of tumor 0.152 0.504 Left colon 80 200 78 72 Right colon 90 187 84 90 CEA (ng/ml), mean 15.3 16.0 ≤ 0.001 16.1 16.0 0.637 PLR 222 220 0.399 219 221 0.863 NLR 3.5 3.2 0.586 3.7 3.4 0.695 Size of tumor(cm), mean 5.1 5.3 0.817 5.3 5.4 0.817 Intestinal obstruction ≤ 0.001 ≤ 0.001 Absent 110 339 105 140 present 60 48 57 22 Tumor differentiation 0.278 0.136 Poorly 38 53 36 23 Moderately 121 318 116 131 highly 11 13 10 8 Pathology T stage 0.122 0.029 T1 0 2 0 1 T2 10 15 10 4 T3 54 167 51 73 T4 106 203 101 84 LNM 0.447 0.784 Absent 98 232 92 95 Present 70 154 68 66 LNDs, mean 17 19 ≤ 0.001 16 19 0.002 LVI ≤ 0.001 0.496 Absent 98 246 94 100 Present 72 141 68 62 PNI ≤ 0.001 0.363 Absent 74 165 68 60 Present 96 222 94 102 Postoperative chemotherapy ≤ 0.001 0.892 Absent 35 82 34 35 Present 135 305 128 127 Postoperative complications 0.111 0.012 Absent 147 356 147 157 Present 13 31 15 5 Blood transfusion 0.132 0.032 Absent 130 350 152 148 Present 40 37 10 14 OG:open group, LG:laparoscopic group, PLR: Platelets to lymphocytes, NLR: Neutrophils to lymphocytes, LNM: Lymph node metastasis, LVI: Lymphovascular invasion, PNI: Perineural invasion, BMI: Body mass index, LND: Number of lymph nodes dissected Survival analysis We then explored OS of patients in the laparoscopic and open groups by Kaplan‒Meier method. The 3- and 5-year OS rates after resection were 72% and 60% in the open group and 82% and 76.8% in the laparoscopic group, respectively. Kaplan‒Meier curves showed that the prognosis of patients in the laparoscopic group was significantly better than that in the open group (P < 0.05) (Fig. 1 ). Univariate and multivariate analyses for OS Univariate Analysis Findings In order to explore the relationship between clinicopathological features and prognosis of patients with colon cancer, we included clinicopathological features in the univariate and multivariate analyses. It is obvious that CEA (P ≤ 0.001), PNI (P ≤ 0.001), LVI (P ≤ 0.001), postoperative chemotherapy (P = 0.020), and LNM (P ≤ 0.001) were negatively correlated with colon cancer patients' OS, but differentiation (P = 0.002), pT stage (P = 0.006), and mode of operation (P = 0.011) were positively correlated with colon cancer patient's OS in the univariate analysis. Then, we identified CEA (P ≤ 0.001), mode of operation (P = 0.002), differentiation (P = 0.002), pT stage (P = 0.015), LNM (P ≤ 0.001), PNI (P = 0.034) and postoperative chemotherapy (P = 0.002) as independent prognostic factors for the patients with colon cancer through multivariate analysis (Table 2 ). Table 2 Univariate and multivariate analyses Features Univariate analysis 95%CI P Multivariate analysis 95%CI P Sex 0.954 0.597–1.542 0.844 BMI 0.749 0.597–1.042 0.110 Age 0.991 0.974–1.008 0.315 Tumor location 0.963 0.615–1.506 0.868 CEA 1.005 1.002–1.008 ≤ 0.001 1.006 1.003–1.010 ≤ 0.001 PLR 1.000 0.997–1.002 0.686 NLR 0.993 0.965–1.022 0.645 Size of tumor 1.015 0.935–1.102 0.719 Obstruction 1.635 0.995–2.698 0.053 Mode of operation 0.533 0.327–0.867 0.011 0.457 0.277–0.755 0.002 Differentiation 0.507 0.329–0.782 0.002 0.494 0.318–0.767 0.002 pT stage 1.902 1.207–2.998 0.006 1.820 1.123–2.950 0.015 LNM 3.316 2.058–5.345 ≤ 0.001 3.028 1.855–4.942 ≤ 0.001 LNDs 0.979 0.956–1.003 0.092 LVI 2.425 1.516–3.879 ≤ 0.001 PNI 2.460 1.414–4.279 ≤ 0.001 1.886 1.050–3.389 0.034 Adjuvant chemotherapy 0.565 0.349–0.916 0.020 0.457 0.269–0.744 0.002 Complications 1.546 1.207–1.998 0.667 Blood transfusion 3.754 2.207–4.998 0.425 PLR: platelets to lymphocytes NLR: neutrophils to lymphocytes LNM: lymph node metastasis LVI: lymphovascular invasion PNI: perineural invasion BMI: body mass index LND: number of lymph node dissection Construction and validation of the prognostic nomogram for OS The clinicopathological features with P values less than 0.01 in the multivariate analysis were used for the nomogram construction. As shown in Fig. 2 , the nomogram consisted of eleven clinicopathological features, including CEA, pT stage, LNM, differentiation, postoperative chemotherapy, PNI and mode of operation. The C-index of the nomogram for predicting OS was 0.796. The calibration curves for 3-year and 5-year OS predicted by the nomogram were highly consistent with the actual observations (Fig. 3 ). Discussion Through propensity score matching (PSM) analysis, this retrospective study achieved balanced clinicopathological characteristics between the laparoscopic and open surgery cohorts. Comparative analysis of overall survival (OS) between groups was performed, followed by development of a prognostic nomogram for stage I-III colon cancer through univariate and multivariate analyses. Post-PSM, baseline parameters including sex, age, preoperative CEA levels, platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and tumor location showed effective equilibrium. However, persistent disparities were observed in transfusion requirements and postoperative complications. Survival analysis revealed superior OS outcomes in the laparoscopic cohort, with 3- and 5-year survival rates of 82% and 76.8%, respectively, compared to 72% and 60% in the open surgery group (P < 0.05). Multivariable analysis confirmed laparoscopic resection as an independent prognostic factor. The constructed nomogram demonstrated robust predictive accuracy for stage I-III colon cancer prognosis. ‌ Existing studies present conflicting evidence. Ringressi et al. [ 15 ] reported a 5-year OS of 86.8% for laparoscopic surgery, marginally exceeding our findings. Conversely, Kitano et al. [ 16 ] and Kim et al. [ 17 ] observed nonsignificant intergroup differences (3-year OS: 96.0% vs. 97.5%, P = 0.790). The comparatively lower survival rates in our cohort may be attributed to the inclusion of a substantial proportion (≈ 50%) of pT4 patients, typically associated with advanced disease and poorer prognoses.‌ The OS advantage in the laparoscopic group may stem from: Reduced postoperative complications and transfusion needs, potentially preserving early postoperative immune function. Enhanced lymph node dissection (LND) efficacy. Current guidelines emphasize the prognostic significance of harvesting ≥ 12 lymph nodes, a threshold consistently achieved in minimally invasive procedures [18,19].‌ Adjuvant chemotherapy has become integral for stage III and high-risk stage II colon cancer management [ 20 ]. Our protocol aligned with international standards, incorporating multidisciplinary evaluations and administering chemotherapy to stage II patients with high-risk features such as perineural invasion (PNI), an established independent prognostic indicator [ 21 – 23 ].‌While elevated postoperative CEA levels correlate with colorectal cancer recurrence [ 24 ], preoperative CEA lacks consensus as a prognostic marker in current AJCC guidelines [25]. Notably, our findings align with studies identifying preoperative CEA as an independent predictor in stage II-III disease [25–28]. The predominance of pT4 tumors in our cohort likely contributed to the observed survival discrepancy compared to prior research [29].‌ Conclusion Traditional prognostic scoring systems often oversimplify risk stratification by equally weighting factors and neglecting variable interactions. Our nomogram addresses these limitations by differentially weighting parameters, thereby enhancing individualized prognostic predictions. This visual predictive tool demonstrates growing clinical utility across disease progression, treatment outcomes, and recurrence assessments.‌ Declarations Acknowledgments Not applicable. Funding There are no resources of funding to be reported Ethics approval and consent to participate The Ethics Committee at first affiliated hospital of Guangxi medical university approved this retrospective study of clinical data study, which was conducted in accordance with the principles of the Declaration of Helsinki.. Consent for publication Informed consent was obtained from all individual participants included in the study. Author contributions Yansong Xu: conceptualization, data curation, writing—original draft; Fangfang Liang and Ruiying Wei software; Hui Li and Huage Zhong: methodology, formal analysis. All authors have read and agreed to the published version of the manuscript. Funding This research received no external funding Data Availability Statement The data used to support the findings of this study are available from the corresponding author upon request. Conflicts of Interest The authors declare that there is no conflict of interest regarding the publication of this paper. References Siegel RL, Miller KD, Wagle NS, et al. Cancer statistics, 2023[J]. Ca-a Cancer Journal for Clinicians, 2023, 73(1): 17-48. Benson AIII, Venook AP, Al-Hawary MM, et al. NCCN Guidelines® Insights Colon Cancer, Version 2.2018 Featured Updates to the NCCN Guidelines[J]. Journal of the National Comprehensive Cancer Network, 2018, 16(4): 359-369. Jacobs M, Verdeja JC and Goldstein HS. Minimally invasive colon resection (laparoscopic colectomy)[J]. Surg Laparosc Endosc, 1991, 1(3): 144-150. Hayashi H, Ozaki N, Ogawa K, et al. 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Left colon as a novel high-risk factor for postoperative recurrence of stage II colon cancer[J]. World Journal of Surgical Oncology, 2020, 18(1);54-63. Shida D, Inoue M, Tanabe T, et al. Prognostic impact of primary tumor location in Stage III colorectal cancer-right-sided colon versus left-sided colon versus rectum: a nationwide multicenter retrospective study[J]. Journal of Gastroenterology, 2020, 55(10): 958-968. Additional Declarations No competing interests reported. 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. 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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-6678114","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":460174616,"identity":"ae7c8fd7-8b68-4cff-bb0a-22b0a3e9a30f","order_by":0,"name":"yansong xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBACAwkQWSEhx8befoAULWcsjPl4ziSQoIWxrSJxnoSDAXFazKV7zCR/nJFIb5NgSGD4UbGNsBbLOcfSpHkqJHLbpBsPMPacuU2Ew24kH5NmOAPUInMggZmxjSgtiW2SP9sk0tkkEgyI1ZJ8TIK3TSKBBC13jiVb85yRMGwDBvJB4vxyu8fw5o+KOnn59vaDD35UEKEFCFgkYKwDRKkHAuYPxKocBaNgFIyCEQoA/GY7ylzK7CcAAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"yansong","middleName":"","lastName":"xu","suffix":""},{"id":460174617,"identity":"89682a69-de7a-4a87-b5c8-4b5b9e59e2c4","order_by":1,"name":"Hui Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Li","suffix":""},{"id":460174618,"identity":"5bd2c7d5-7a92-420f-aa8d-7470211fa204","order_by":2,"name":"Fangfang Liang","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fangfang","middleName":"","lastName":"Liang","suffix":""},{"id":460174619,"identity":"3bc4f861-945c-4a45-bbf0-a1dbcbc83459","order_by":3,"name":"Huage Zhong","email":"","orcid":"","institution":"Guangxi Medical University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Huage","middleName":"","lastName":"Zhong","suffix":""},{"id":460174620,"identity":"21ad68ae-7375-4a01-8111-57e040620bbf","order_by":4,"name":"Ruiying Wei","email":"","orcid":"","institution":"The First Affiliated Hospital of Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruiying","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2025-05-16 07:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6678114/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6678114/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83421661,"identity":"30f51ca8-cbde-424c-98fd-1a771e8562f4","added_by":"auto","created_at":"2025-05-26 02:12:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51492,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan‒Meier curves for OS of the laparoscopic and open groups.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6678114/v1/88d916074b1a75a36d7c2804.jpg"},{"id":83421207,"identity":"04c6f183-a971-4b55-adb9-2a404f7a4273","added_by":"auto","created_at":"2025-05-26 02:04:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54084,"visible":true,"origin":"","legend":"\u003cp\u003eThe nomogram for predicting OS of colon cancer patient.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6678114/v1/a1bb3636754a98be0a5db8cd.jpg"},{"id":83421208,"identity":"7f082f99-c6f1-4400-aab5-a8bd0c11d218","added_by":"auto","created_at":"2025-05-26 02:04:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46855,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curves of the nomogram for predicting the 3- and 5-year OS. (a) Calibration curves of the nomogram for predicting the 3-year OS. (b) Calibration curves of the nomogram for predicting the 5-year OS.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6678114/v1/1dbe99bf185cfc71dac04610.jpg"},{"id":85024687,"identity":"9a2ce611-1695-4161-a587-af3a879d4902","added_by":"auto","created_at":"2025-06-20 05:38:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1045946,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6678114/v1/f7cec904-ba86-4e83-a7e2-2152748b85e2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eLaparoscopic Radical Resection as an Independent Favorable Prognostic Factor for Stage I-III Colon Cancer: A Propensity Score-Matched Study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) represents a major global health burden, ranking as the third most commonly diagnosed malignancy and the second leading cause of cancer-related mortality worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While advancements in multimodal therapies including targeted agents and immunotherapies have transformed CRC management, radical surgical resection remains the cornerstone of curative treatment[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe evolution of minimally invasive techniques in CRC surgery commenced with Jacobs' pioneering laparoscopic colectomy in 1991[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Subsequent randomized controlled trials (RCTs) have consistently validated the safety profile and short-term advantages of laparoscopic approaches, particularly regarding accelerated postoperative recovery[\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Nevertheless, persistent controversies exist concerning long-term oncological equivalence between laparoscopic and open techniques[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Two critical factors may explain this discrepancy:\u003c/p\u003e \u003cp\u003eFirst, the learning curve phenomenon in early laparoscopic adoption. Initial series demonstrated suboptimal lymph node yields during the formative period of minimally invasive surgery (MIS)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], a critical prognostic determinant in CRC management[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Second, the technical refinement of modern laparoscopic complete mesocolic excision (CME) with D3 lymphadenectomy now enables meticulous dissection of metastatic lymph nodes[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This raises a pivotal clinical question: Can contemporary laparoscopic expertise translate into superior long-term survival compared to conventional open resection?\u003c/p\u003e \u003cp\u003eTo address these controversies, we developed a prognostic nomogram incorporating 3- and 5-year overall survival (OS) predictors for stage I-III colon cancer patients. Recognizing the inherent limitations of observational studies, particularly selection bias and confounding variables, we implemented propensity score matching (PSM) analysis to ensure balanced baseline characteristics between surgical cohorts. This methodological rigor enhances the validity of comparative long-term outcome assessments.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDatabase and candidate variables\u003c/h2\u003e \u003cp\u003eThe study cohort was retrospectively identified from the Gastrointestinal Surgery Database of Guangxi Medical University Affiliated Cancer Hospital (2010\u0026ndash;2020), comprising patients undergoing curative-intent colectomy with histologically confirmed stage I-III adenocarcinoma. Tumor staging adhered to AJCC/UICC TNM 8th edition criteria, verified by two independent pathologists.\u003c/p\u003e \u003cp\u003eInclusion Criteria: ①Pathologically confirmed colonic adenocarcinoma; ②R0 resection with complete mesocolic excision; ③\u0026ge;3 months postoperative follow-up; ④No neoadjuvant therapy history. Exclusion Criteria: ①Emergency/palliative procedures; ②Prior malignancy history; ③Incomplete clinical biomarker profiles. The analytical framework incorporated: ①Demographics: Age, gender, BMI, ECOG status; ③Tumor characteristics: TNM stage, differentiation grade, lymphovascular/perineural invasion; ③Surgical parameters: Operative approach (laparoscopic vs. open), lymph node yield, transfusion requirement.Follow-up was conducted through multiple modalities including telephone interviews, outpatient clinic visits, and WeChat-based communications.\u003c/p\u003e \u003cp\u003e The Institutional Review Board of First Affiliated Hospital of Guangxi Medical University approved this study, waiving informed consent under retrospective design per Declaration of Helsinki provisions.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUnivariate and multivariate analysis\u003c/h3\u003e\n\u003cp\u003eThe following variables for univariate associations with overall survival (OS) were analyzed: (1) clinical and pathological data: age, sex, body mass index (BMI), Tumor location, postoperative chemotherapy, number of lymph nodes dissected (LND), operation mode, intestinal obstruction, pathology T (pT) stage, lymph node metastasis (LNM), lymphovascular invasion (LVI), perineural invasion (PNI), postoperative complications, blood transfusion; (2) laboratory markers: CEA, platelets to lymphocytes (PLR), neutrophils to lymphocytes (NLR). Finally, the following variables, including CEA, pT stage, differentiation, postoperative chemotherapy, operation mode, LNM, and PNI showing statistical significance at a p value of less than 0.05, were subjected to multivariable modeling.\u003c/p\u003e\n\u003ch3\u003eModel construction and validation\u003c/h3\u003e\n\u003cp\u003eMultivariate Cox proportional hazards models of OS were formulated from all variables and two-way interactions, showing statistically significant correlations with their respective endpoints. If a variable's influence has a clinical difference in the level of interaction, it will reach clinical significance. The final model, including all significant and pairwise interactions, was still statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and clinically significant after a backward stepwise method. Based on the final model, a nomogram of 3-year and 5-year OS probability was constructed with the R software package. In internal calibration plots, points parallel to the reference line represent the covariates' similar prediction results in the training.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003e PSM was applied to achieve a balanced exposure group at baseline (including age, sex, BMI, tumor location), in accordance with the recommendations by Lonjon et al.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] After PSM, we followed the methods of Yansong Xu et al[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Clinical and pathological outcomes were compared between the laparoscopic group and the open group. The Kaplan‒Meier method was used for OS by IBM SPSS 26.0 software (version 26.0; SPSS, Chicago, IL). Statistical significance was set at 0.05. We developed the prognostic model with univariate evaluation of the significance of each factor. Next, multivariate analyses were performed using the Cox proportional hazards model. Univariate predictive variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were applied to multivariate analyses to identify the independent prognostic factors. Nomograms and calibration plots were constructed using R software, version 3.3.3 (CRAN; R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical samples\u003c/h2\u003e \u003cp\u003eA total of 557 samples were included in this study, including 387 samples in the laparoscopic group and 170 samples in the open group. Initially, we found no difference between the two groups concerning sex, BMI, PLR, NLR, size of tumor, tumor differentiation, pT stage, LNM, postoperative complications and blood transfusion. However, there were significant differences in CEA, intestinal obstruction, LNDs, LVI, PNI and postoperative chemotherapy between the two groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In order to ensure the consistency of the two groups as far as possible, we used PSM analysis to re-screen the baseline data. Finally, the remaining 324 samples were included in the study, including 162 samples in the laparoscopic group and 162 samples in the open group. There was a significant difference in intestinal obstruction, pT stage, blood transfusion, and postoperative complications (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eDemographic and pathologic data before and after propensity score matching\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBefore PSM N\u0026thinsp;=\u0026thinsp;557\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAfter PSM N\u0026thinsp;=\u0026thinsp;324\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\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\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\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\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\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\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/㎡), mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years) mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation of tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft colon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight colon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA (ng/ml), mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize of tumor(cm), mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntestinal obstruction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehighly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathology T stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNDs, mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostoperative chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostoperative complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood transfusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eOG:open group, LG:laparoscopic group, PLR: Platelets to lymphocytes, NLR: Neutrophils to lymphocytes, LNM: Lymph node metastasis, LVI: Lymphovascular invasion, PNI: Perineural invasion, BMI: Body mass index, LND: Number of lymph nodes dissected\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSurvival analysis\u003c/h3\u003e\n\u003cp\u003eWe then explored OS of patients in the laparoscopic and open groups by Kaplan‒Meier method. The 3- and 5-year OS rates after resection were 72% and 60% in the open group and 82% and 76.8% in the laparoscopic group, respectively. Kaplan‒Meier curves showed that the prognosis of patients in the laparoscopic group was significantly better than that in the open group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eUnivariate and multivariate analyses for OS\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate Analysis Findings\u003c/h2\u003e \u003cp\u003eIn order to explore the relationship between clinicopathological features and prognosis of patients with colon cancer, we included clinicopathological features in the univariate and multivariate analyses. It is obvious that CEA (P\u0026thinsp;\u0026le;\u0026thinsp;0.001), PNI (P\u0026thinsp;\u0026le;\u0026thinsp;0.001), LVI (P\u0026thinsp;\u0026le;\u0026thinsp;0.001), postoperative chemotherapy (P\u0026thinsp;=\u0026thinsp;0.020), and LNM (P\u0026thinsp;\u0026le;\u0026thinsp;0.001) were negatively correlated with colon cancer patients' OS, but differentiation (P\u0026thinsp;=\u0026thinsp;0.002), pT stage (P\u0026thinsp;=\u0026thinsp;0.006), and mode of operation (P\u0026thinsp;=\u0026thinsp;0.011) were positively correlated with colon cancer patient's OS in the univariate analysis. Then, we identified CEA (P\u0026thinsp;\u0026le;\u0026thinsp;0.001), mode of operation (P\u0026thinsp;=\u0026thinsp;0.002), differentiation (P\u0026thinsp;=\u0026thinsp;0.002), pT stage (P\u0026thinsp;=\u0026thinsp;0.015), LNM (P\u0026thinsp;\u0026le;\u0026thinsp;0.001), PNI (P\u0026thinsp;=\u0026thinsp;0.034) and postoperative chemotherapy (P\u0026thinsp;=\u0026thinsp;0.002) as independent prognostic factors for the patients with colon cancer through multivariate analysis (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\u003eUnivariate and multivariate analyses\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 \u003cp\u003eFeatures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.597\u0026ndash;1.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.597\u0026ndash;1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.974\u0026ndash;1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.615\u0026ndash;1.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.002\u0026ndash;1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.003\u0026ndash;1.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.997\u0026ndash;1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.965\u0026ndash;1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSize of tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.935\u0026ndash;1.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstruction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.995\u0026ndash;2.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMode of operation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.327\u0026ndash;0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.277\u0026ndash;0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.329\u0026ndash;0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.318\u0026ndash;0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.207\u0026ndash;2.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.123\u0026ndash;2.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.058\u0026ndash;5.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.855\u0026ndash;4.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLNDs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.956\u0026ndash;1.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.516\u0026ndash;3.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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\u003e2.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.414\u0026ndash;4.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.050\u0026ndash;3.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjuvant chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.349\u0026ndash;0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.269\u0026ndash;0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.207\u0026ndash;1.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood transfusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.207\u0026ndash;4.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ePLR: platelets to lymphocytes NLR: neutrophils to lymphocytes LNM: lymph node metastasis LVI: lymphovascular invasion PNI: perineural invasion BMI: body mass index LND: number of lymph node dissection\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and validation of the prognostic nomogram for OS\u003c/h2\u003e \u003cp\u003eThe clinicopathological features with P values less than 0.01 in the multivariate analysis were used for the nomogram construction. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the nomogram consisted of eleven clinicopathological features, including CEA, pT stage, LNM, differentiation, postoperative chemotherapy, PNI and mode of operation. The C-index of the nomogram for predicting OS was 0.796. The calibration curves for 3-year and 5-year OS predicted by the nomogram were highly consistent with the actual observations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThrough propensity score matching (PSM) analysis, this retrospective study achieved balanced clinicopathological characteristics between the laparoscopic and open surgery cohorts. Comparative analysis of overall survival (OS) between groups was performed, followed by development of a prognostic nomogram for stage I-III colon cancer through univariate and multivariate analyses.\u003c/p\u003e \u003cp\u003ePost-PSM, baseline parameters including sex, age, preoperative CEA levels, platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and tumor location showed effective equilibrium. However, persistent disparities were observed in transfusion requirements and postoperative complications. Survival analysis revealed superior OS outcomes in the laparoscopic cohort, with 3- and 5-year survival rates of 82% and 76.8%, respectively, compared to 72% and 60% in the open surgery group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariable analysis confirmed laparoscopic resection as an independent prognostic factor. The constructed nomogram demonstrated robust predictive accuracy for stage I-III colon cancer prognosis.\u003c/p\u003e \u003cp\u003e\u0026zwnj; Existing studies present conflicting evidence. Ringressi et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] reported a 5-year OS of 86.8% for laparoscopic surgery, marginally exceeding our findings. Conversely, Kitano et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and Kim et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] observed nonsignificant intergroup differences (3-year OS: 96.0% vs. 97.5%, P\u0026thinsp;=\u0026thinsp;0.790). The comparatively lower survival rates in our cohort may be attributed to the inclusion of a substantial proportion (\u0026asymp;\u0026thinsp;50%) of pT4 patients, typically associated with advanced disease and poorer prognoses.\u0026zwnj; The OS advantage in the laparoscopic group may stem from: Reduced postoperative complications and transfusion needs, potentially preserving early postoperative immune function. Enhanced lymph node dissection (LND) efficacy. Current guidelines emphasize the prognostic significance of harvesting\u0026thinsp;\u0026ge;\u0026thinsp;12 lymph nodes, a threshold consistently achieved in minimally invasive procedures [18,19].\u0026zwnj;\u003c/p\u003e \u003cp\u003eAdjuvant chemotherapy has become integral for stage III and high-risk stage II colon cancer management [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our protocol aligned with international standards, incorporating multidisciplinary evaluations and administering chemotherapy to stage II patients with high-risk features such as perineural invasion (PNI), an established independent prognostic indicator [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u0026zwnj;While elevated postoperative CEA levels correlate with colorectal cancer recurrence [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], preoperative CEA lacks consensus as a prognostic marker in current AJCC guidelines [25]. Notably, our findings align with studies identifying preoperative CEA as an independent predictor in stage II-III disease [25\u0026ndash;28]. The predominance of pT4 tumors in our cohort likely contributed to the observed survival discrepancy compared to prior research [29].\u0026zwnj;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTraditional prognostic scoring systems often oversimplify risk stratification by equally weighting factors and neglecting variable interactions. Our nomogram addresses these limitations by differentially weighting parameters, thereby enhancing individualized prognostic predictions. This visual predictive tool demonstrates growing clinical utility across disease progression, treatment outcomes, and recurrence assessments.\u0026zwnj;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no resources of funding to be reported\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Ethics Committee at first affiliated hospital of Guangxi medical university approved this retrospective study of clinical data study, which was conducted in accordance with the principles of the Declaration of Helsinki..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYansong Xu: conceptualization, data curation, writing\u0026mdash;original draft; Fangfang Liang and Ruiying Wei software; Hui Li and Huage Zhong: methodology, formal analysis. All authors have read and agreed to the published version of the manuscript. Funding This research received no external funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used to support the findings of this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Wagle NS, et al. Cancer statistics, 2023[J]. Ca-a Cancer Journal for Clinicians, 2023, 73(1): 17-48.\u003c/li\u003e\n\u003cli\u003eBenson AIII, Venook AP, Al-Hawary MM, et al. NCCN Guidelines\u0026reg; Insights Colon Cancer, Version 2.2018 Featured Updates to the NCCN Guidelines[J]. Journal of the National Comprehensive Cancer Network, 2018, 16(4): 359-369.\u003c/li\u003e\n\u003cli\u003eJacobs M, Verdeja JC and Goldstein HS. Minimally invasive colon resection (laparoscopic colectomy)[J]. Surg Laparosc Endosc, 1991, 1(3): 144-150.\u003c/li\u003e\n\u003cli\u003eHayashi H, Ozaki N, Ogawa K, et al. Assessing the economic advantage of laparoscopic vs. open approaches for colorectal cancer by a propensity score matching analysis[J]. Surgery Today, 2018, 48(4): 439-448.\u003c/li\u003e\n\u003cli\u003eNumata M, Sawazaki S, Morita J, et al. Comparison of Laparoscopic and Open Surgery for Colorectal Cancer in Patients with Severe Comorbidities[J]. Anticancer Research, 2018, 38(2): 963-967.\u003c/li\u003e\n\u003cli\u003evan der Pas MHGM, Haglind E, Cuesta MA, et al. Laparoscopic versus open surgery for rectal cancer (COLOR II): short-term outcomes of a randomised, phase 3 trial[J]. Lancet Oncology, 2013, 14(3): 210-218.\u003c/li\u003e\n\u003cli\u003eDevoto L, Celentano V, Cohen R, et al. Colorectal cancer surgery in the very elderly patient: a systematic review of laparoscopic versus open colorectal resection[J]. International Journal of Colorectal Disease, 2017, 32(9): 1237-1242.\u003c/li\u003e\n\u003cli\u003eJayne DG, Thorpe HC, Copeland J, et al. Five-year follow-up of the Medical Research Council CLASICC trial of laparoscopically assisted open surgery for colorectal cancer[J]. British Journal of Surgery, 2010, 97(11): 1638-1645.\u003c/li\u003e\n\u003cli\u003eGuillou PJ, Quirke P, Thorpe H, et al. Short-term endpoints of conventional versus laparoscopic-assisted surgery in patients with colorectal cancer (MRC CLASICC trial): multicentre, randomised controlled trial[J]. Lancet, 2005, 365(9472): 1718-1726.\u003c/li\u003e\n\u003cli\u003eCompton CC, Fielding LP, Burgart LJ, et al. Prognostic factors in colorectal cancer - College of American Pathologists Consensus Statement 1999[J]. Archives of Pathology \u0026amp; Laboratory Medicine, 2000, 124(7): 979-994.\u003c/li\u003e\n\u003cli\u003eWu YH, Sun XJ, Qi J, et al. Comparative study of short- and long-term outcomes of laparoscopic-assisted versus open rectal cancer resection during and after the learning curve period[J]. Medicine, 2017, 96(19).\u003c/li\u003e\n\u003cli\u003eSon GM, Kim JG, Lee JC, et al. Multidimensional Analysis of the Learning Curve for Laparoscopic Rectal Cancer Surgery[J]. Journal of Laparoendoscopic \u0026amp; Advanced Surgical Techniques, 2010, 20(7): 609-617.\u003c/li\u003e\n\u003cli\u003eLonjon G, Porcher R, Ergina P, et al. Potential Pitfalls of Reporting and Bias in Observational Studies With Propensity Score Analysis Assessing a Surgical Procedure: A Methodological Systematic Review[J]. Ann Surg, 2017, 265(5): 901-909.\u003c/li\u003e\n\u003cli\u003eXu Y, Liang F, Chen Y, et al. Novel Model to Predict the Prognosis of Patients with Stage II\u0026ndash;III Colon Cancer[J]. BioMed Research International, 2020, 2020: 8812974.\u003c/li\u003e\n\u003cli\u003eRingressi MN, Boni L, Freschi G, et al. Comparing laparoscopic surgery with open surgery for long-term outcomes in patients with stage I to III colon cancer[J]. Surgical Oncology-Oxford, 2018, 27(2): 115-122.\u003c/li\u003e\n\u003cli\u003eKim CW, Hur H, Min BS, et al. Oncologic outcomes of single-incision laparoscopic surgery for right colon cancer: A propensity score-matching analysis[J]. International Journal of Surgery, 2017, 45: 125-130.\u003c/li\u003e\n\u003cli\u003eKitano S, Inomata M, Mizusawa J, et al. Survival outcomes following laparoscopic versus open D3 dissection for stage II or III colon cancer (JCOG0404): a phase 3, randomised controlled trial[J]. Lancet Gastroenterology \u0026amp; Hepatology, 2017, 2(4): 261-268.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Boyle S and Stephenson K. More is better: Lymph node harvesting in colorectal cancer[J]. American Journal of Surgery, 2017, 213(5): 926-930.\u003c/li\u003e\n\u003cli\u003eBatista VL, Iglesias AC, Madureira FA, et al. Adequate lymphadenectomy for colorectal cancer: a comparative analysis between open and laparoscopic surgery[J]. Arq Bras Cir Dig, 2015, 28(2): 105-108.\u003c/li\u003e\n\u003cli\u003eCienfuegos JA, Mart\u0026iacute;nez P, Baixauli J, et al. Perineural Invasion is a Major Prognostic and Predictive Factor of Response to Adjuvant Chemotherapy in Stage I-II Colon Cancer[J]. Annals of Surgical Oncology, 2017, 24(4): 1077-1084.\u003c/li\u003e\n\u003cli\u003eMirkin KA, Hollenbeak CS, Mohamed A, et al. Impact of perineural invasion on survival in node negative colon cancer[J]. Cancer Biology \u0026amp; Therapy, 2017, 18(9): 740-745.\u003c/li\u003e\n\u003cli\u003eLeijssen LGJ, Dinaux AM, Taylor MS, et al. Perineural Invasion Is a Prognostic but not a Predictive Factor in Nonmetastatic Colon Cancer[J]. Dis Colon Rectum, 2019, 62(10): 1212-1221.\u003c/li\u003e\n\u003cli\u003eHuh JW, Kim HR and Kim YJ. Prognostic Value of Perineural Invasion in Patients with Stage II Colorectal Cancer[J]. Annals of Surgical Oncology, 2010, 17(8): 2066-2072.\u003c/li\u003e\n\u003cli\u003eKim H, Jung HI, Kwon SH, et al. Preoperative neutrophil-lymphocyte ratio and CEA is associated with poor prognosis in patients with synchronous colorectal cancer liver metastasis[J]. Annals of Surgical Treatment and Research, 2019, 96(4): 191-200.\u003c/li\u003e\n\u003cli\u003eKonishi T, Shimada Y, Hsu M, et al. Association of Preoperative and Postoperative Serum Carcinoembryonic Antigen and Colon Cancer Outcome[J]. Jama Oncology, 2018, 4(3): 309-315.\u003c/li\u003e\n\u003cli\u003eMargalit O, Mamtani R, Yang YX, et al. Assessing the prognostic value of carcinoembryonic antigen levels in stage I and II colon cancer[J]. European Journal of Cancer, 2018, 94: 1-5.\u003c/li\u003e\n\u003cli\u003eSpindler BA, Bergquist JR, Thiels CA, et al. Incorporation of CEA Improves Risk Stratification in Stage II Colon Cancer[J]. Journal of Gastrointestinal Surgery, 2017, 21(5): 770-777.\u003c/li\u003e\n\u003cli\u003eWang LM, Hirano Y, Ishii T, et al. Left colon as a novel high-risk factor for postoperative recurrence of stage II colon cancer[J]. World Journal of Surgical Oncology, 2020, 18(1);54-63.\u003c/li\u003e\n\u003cli\u003eShida D, Inoue M, Tanabe T, et al. Prognostic impact of primary tumor location in Stage III colorectal cancer-right-sided colon versus left-sided colon versus rectum: a nationwide multicenter retrospective study[J]. Journal of Gastroenterology, 2020, 55(10): 958-968.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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, Laparascopic resection, Propensity Score-Matched Study","lastPublishedDoi":"10.21203/rs.3.rs-6678114/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6678114/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground‌: \u003c/strong\u003eSurgical approach selection critically impacts postoperative morbidity and long-term oncologic outcomes in colorectal cancer management. This study aimed to compare survival benefits between laparoscopic and open radical resection for stage I-III colon cancer, while establishing a validated prognostic prediction model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e‌Methods: \u003c/strong\u003e‌Patients with colon cancer who underwent surgery at our hospital were researched in this retrospective study. Propensity score matching (PSM) was used to minimize the preoperative baseline variables. The clinical and pathological data between open and laparoscopic surgery were compared, and the effect of factors on overall survival (OS) was analyzed by the Cox proportional hazard model. Then, a personalized nomogram to predict the patient's prognosis was constructed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 324 colon cancer samples were selected by PSM. Patients in the laparoscopic group had a higher number of lymph node dissections, fewer blood transfusions, and fewer postoperative complications (P\u0026lt;0.05). The 3- and 5-year OS rates were 72% and 60% in the open group, and 82% and 76.8% in the laparoscopic group, respectively (P\u0026lt; 0.05). The preoperative CEA level, pathological T (pT) stage, differentiation, operation mode, lymph node metastasis (LNM), postoperative chemotherapy, and perineural invasion (PNI) were independent predictors of survival (P\u0026lt;0.05). A prognostic model based on these seven factors was constructed. The final nomogram showed excellent discrimination (C=0.796) for OS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e‌Conclusion:\u003c/strong\u003e Laparoscopic resection demonstrates superior long-term survival compared to open surgery in localized colon cancer. The developed nomogram provides clinically valuable prognostic stratification, potentially guiding postoperative surveillance .\u003c/p\u003e","manuscriptTitle":"Laparoscopic Radical Resection as an Independent Favorable Prognostic Factor for Stage I-III Colon Cancer: A Propensity Score-Matched Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-26 02:04:41","doi":"10.21203/rs.3.rs-6678114/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":"86079a63-abef-443f-a0cd-de7d89a8a669","owner":[],"postedDate":"May 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-19T16:53:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-26 02:04:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6678114","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6678114","identity":"rs-6678114","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}