A Predictive Nomogram Model for Overall Survival in Obstructive Colorectal Cancer Based on Clinical and Laboratory Indicators

A Predictive Nomogram Model for Overall Survival in Obstructive Colorectal Cancer Based on Clinical and Laboratory Indicators | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Predictive Nomogram Model for Overall Survival in Obstructive Colorectal Cancer Based on Clinical and Laboratory Indicators Pingxia Lu, Wanyun Su, Dingman Huang, Xinyu Huang, Cuifeng Zheng, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7554925/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 Obstructive colorectal cancer (oCRC) correlates with advanced disease and poor outcomes. This study aimed to identify independent prognostic factors using clinical and laboratory data and construct a predictive nomogram for oCRC patients' individualized survival estimation and clinical decision-making. Methods A retrospective cohort of 167 patients with histologically confirmed oCRC admitted to Fujian Medical University Union Hospital between February 2010 and February 2021 was analyzed. Patients were randomly divided into a training cohort (n = 116) and a validation cohort (n = 51) in a 7:3 ratio. Prognostic variables were identified using univariate and multivariate Cox proportional hazards regression analyses. A nomogram was developed based on independent prognostic factors. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA) to evaluate its discrimination, calibration, and clinical utility, respectively. Results Multivariate Cox regression analysis identified five independent prognostic factors: M stage (HR = 1.917, 95% CI:1.005–3.657, P = 0.048), tumor grade (HR = 0.229, 95% CI: 0.096–0.543, P < 0.001), CA19-9 (HR = 3.919, 95% CI: 2.038–7.538, P < 0.001), albumin-to-globulin ratio (AGR; HR = 2.103, 95% CI:1.158–3.817, P = 0.015), and platelet-to-lymphocyte ratio (PLR; HR = 1.873, 95% CI: 1.013–3.464, P = 0.045). These variables were incorporated into a prognostic nomogram. The model showed good discriminatory ability (AUC = 0.721 in the training set; 0.776 in the validation set), reliable calibration, and strong clinical applicability as demonstrated by DCA. Conclusion The nomogram (incorporating M stage, tumor grade, CA19-9, AGR, PLR) provides accurate, individualized prognosis for oCRC patients, and may aid clinical risk stratification and therapeutic decision-making. intestinal obstruction of colorectal cancer prognostic nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Colorectal cancer (CRC) ranks as the third most commonly diagnosed malignancy and the second leading cause of cancer-related mortality worldwide[ 1 ]. In 2022, China reported approximately 517,100 new CRC cases, accounting for 10.7% of all malignant tumors diagnosed that year[ 2 ]. A severe and life-threatening complication of CRC is intestinal obstruction, which is frequently encountered in clinical emergency settings and associated with a high mortality rate[ 3 ]. Notably, nearly 20% of CRC patients present with intestinal obstruction at initial diagnosis [ 4 ]. Compared to non-obstructive CRC cases, patients with obstruction typically exhibit more advanced disease stages, lower tumor differentiation postoperatively, and a significantly higher risk of distant metastasis. Consequently, their 5-year overall survival (OS) rate ranges from only 31–42%[ 5 – 7 ]. Nomograms, as visual tools derived from multivariate regression models such as logistic or Cox regression, have been increasingly employed in oncology to support individualized prognostication and clinical decision-making[ 8 , 9 ]. These models translate complex statistical data into an accessible graphical interface, allowing for intuitive estimation of outcome probabilities based on multiple clinical variables [ 10 , 11 ]. Compared with traditional prognostic scoring systems, nomograms offer improved accuracy and usability by simultaneously incorporating and visualizing multiple independent predictors. For example, a study by Liu et al. developed a nomogram based on established clinical factors to predict survival in stage IV CRC patients with distant metastases, providing a useful tool for guiding therapeutic strategies [ 12 ]. Emerging evidence suggests that the host immune system plays a dual role in tumor development, contributing to both tumor suppression and promotion [ 13 – 16 ]. Inflammatory responses, reflected in alterations of hematologic biomarkers, may hold prognostic significance in malignancies including CRC [ 17 , 18 ]. Several inflammation-based indices such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic inflammatory response index (SIRI) have been independently associated with CRC prognosis [ 19 – 22 ]. However, prior studies have largely examined these markers in isolation, without evaluating their combined prognostic utility. Furthermore, the prognostic relevance of inflammation-based biomarkers in patients with obstructive CRC remains underexplored. Additionally, reduced albumin-to-globulin ratio (AGR) has been linked to unfavorable OS outcomes in CRC patients [ 23 ]. Given these gaps, the present study aims to comprehensively investigate the prognostic value of preoperative blood-based inflammatory and nutritional markers in patients with oCRC. Furthermore, we sought to develop and validate a nomogram model integrating these parameters to improve individualized survival prediction and inform clinical decision-making. Methods Study population This retrospective study included 167 patients diagnosed with oCRC who were admitted to the Emergency Surgery Department of Fujian Medical University Union Hospital between February 2010 and February 2021 (Fig. 1 ). To develop and internally validate a prognostic model, patients were randomly assigned to a training cohort (n = 116) and a validation cohort (n = 51) at a 7:3 ratio. Inclusion criteria were as follows: initial diagnosis of obstructive CRC; histopathological confirmation of primary colorectal adenocarcinoma; clinical and radiological evidence of bowel obstruction; and availability of complete clinical and laboratory data. Exclusion criteria included: pregnancy; psychiatric disorders; hematologic diseases; chronic liver disease; chronic kidney disease; autoimmune disorders; long-term corticosteroid therapy; co-existing infectious diseases involving other organ systems; recurrent CRC or multiple primary CRCs; history of other malignancies; inoperability due to advanced tumor stage or severe cardiopulmonary dysfunction; and incomplete clinical data. The study protocol was reviewed and approved by the Institutional Review Board of Fujian Medical University Union Hospital. Written informed consent was obtained from all participants prior to data collection, in accordance with the Declaration of Helsinki. Data collection A retrospective review was conducted using the original electronic medical records of all included patients. Baseline clinical data were extracted, encompassing demographic variables (age and sex), as well as clinical features such as the site and type of bowel obstruction, surgical approach, tumor-node-metastasis (TNM) staging, and histological classification at the time of diagnosis. Preoperative laboratory parameters were obtained from routine blood tests performed on the day of hospital admission. These included hematologic indices, serum biochemistry profiles—specifically albumin (Alb), globulin (Glo), and high-density lipoprotein (HDL)—as well as tumor biomarkers such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19 − 9 (CA19-9). Based on these raw laboratory values, the following inflammation- and nutrition-related composite indices were calculated: neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic inflammatory response index (SIRI), and albumin-to-globulin ratio (AGR). Statistical analysis All statistical analyses were performed using SPSS software version 27.0 (IBM Corp., Armonk, NY, USA) and R software version 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria). Optimal cutoff values for continuous variables were determined using receiver operating characteristic (ROC) curve analysis, with thresholds selected based on the point maximizing the Youden index. To assess multicollinearity among candidate variables, the variance inflation factor (VIF) was calculated. Variables exhibiting significant collinearity (VIF > 5) were excluded from subsequent analyses. Patients were randomly assigned to either the training cohort (n = 116) or the validation cohort (n = 51) in a 7:3 ratio. Baseline characteristics between the two cohorts were compared using the chi-square test for categorical variables. Univariate Cox proportional hazards regression analyses were conducted to identify potential prognostic factors associated with overall survival (OS). Variables with a P-value < 0.05 in univariate analysis were subsequently included in the multivariate Cox regression model. Independent prognostic factors were identified based on multivariate analysis results (P < 0.05). Model variable selection was performed using backward stepwise regression guided by the Akaike information criterion (AIC) to optimize model fit. A prognostic nomogram was then constructed based on the final multivariate Cox model using the "rms" package in R. The predictive performance of the model was assessed in both the training and validation cohorts by evaluating discrimination, calibration, and clinical utility. Discrimination was measured using time-dependent ROC curves generated with the "pROC" package. Calibration was assessed using calibration plots created with the "rms" package, comparing predicted and observed survival probabilities. Clinical utility was further evaluated via decision curve analysis (DCA) using the "rmda" package in R, which quantified the net benefit across a range of threshold probabilities. Results Patient characteristics and Cox regression analysis Baseline characteristics of patients in the training and validation cohorts were compared using the chi-square test to assess distributional balance. Variables analyzed included demographic data (sex and age), clinical features (stent placement, chemotherapy, tumor size, site of obstruction, type of surgery), tumor staging (T, N, M, and overall TNM stage), histologic grade, obstruction type, and laboratory parameters (WBC, mono, PLT, MPV, TP, ALB, CEA, CA19-9, LMR, SIRI, PLR, PNI, and AGR). Among these, no statistically significant differences were observed between cohorts for most variables (P > 0.05), indicating comparability between the two groups. However, a significant difference was identified in AGR (P = 0.037) and MLR (P = 0.034), suggesting mild imbalance in these parameters. Despite this, the overall similarity across variables supports the assumption that the training and validation cohorts are derived from the same underlying population, ensuring generalizability of the predictive model (Table 1 ). Table 1 Baseline Characteristics and Laboratory Indicators of Patients in the Training and Validation Cohorts Variables training set validation set p n % n % All patients 116 100 51 100 Gender female 47 40.5 23 45.1 0.581 male 69 59.5 28 54.9 Age <60 55 47.4 29 56.9 0.261 ≥ 60 61 52.6 22 43.1 Support without 74 63.8 38 74.5 0.175 exist 42 36.2 13 25.5 Chemo without 55 47.4 27 52.9 0.510 exist 61 52.6 24 47.1 Size of the tumor <5cm 26 22.4 12 23.5 0.874 ≥ 5cm 90 77.6 39 76.5 Obstructive site rectum 12 10.3 3 5.9 0.353 colon 104 89.7 48 94.1 Surgical procedure Palliative resection 73 62.9 32 62.7 0.982 Radical resection 43 37.1 19 37.3 T stage T1 + 2 + 3 75 64.7 28 54.9 0.232 T4 41 35.3 23 45.1 N stage N0 + 1 88 75.9 38 74.5 0.852 N2 28 24.1 13 25.5 M stage M0 85 73.3 37 72.5 0.922 M1 31 26.7 14 27.5 TNM stage I + II 32 27.6 17 33.3 0.453 III་IV 84 72.4 34 66.7 Grade G3 9 7.8 7 13.7 0.228 G1、G2 107 92.2 44 86.3 Obstructive type incomplete 85 73.3 38 74.5 0.992 complete 31 26.7 13 25.5 WBC(*10 9 /L) < 13.83 103 88.8 48 94.1 0.282 ≥ 13.83 13 11.2 3 5.9 Mono(*10 9 /L) < 0.9 103 88.8 44 86.3 0.644 ≥ 0.9 13 11.2 7 13.7 PLT(*10 9 /L) < 344 87 75.0 40 78.4 0.632 ≥ 344 29 25.0 11 21.6 MPV(fL) < 10.9 100 86.2 46 90.2 0.474 ≥ 10.9 16 13.8 5 9.8 TP(g/L) <50 12 10.3 6 11.8 0.785 ≥ 50 104 89.7 45 88.2 ALB(g/L) <26 12 10.3 4 7.8 0.613 ≥ 26 104 89.7 47 92.2 CEA(ng/mL) <5 59 50.9 20 39.2 0.165 ≥ 5 57 49.1 31 60.8 CA19-9(U/mL) <37 87 75.0 39 76.5 0.839 ≥ 37 29 25.0 12 23.5 LMR <2.7 82 70.7 35 68.6 0.789 ≥ 2.7 34 29.3 16 31.4 SIRI <5.8 94 81.0 41 80.4 0.923 ≥ 5.8 22 19.0 10 19.6 AGR <1.26 48 41.4 30 58.8 0.037 ≥ 1.26 68 58.6 21 41.2 PLR <245 60 51.7 26 51.0 0.929 ≥ 245 56 48.3 25 49.0 MLR <0.03 39 33.6 26 51.0 0.034 ≥ 0.03 77 66.4 25 49.0 Table 2 Univariate cox regression analysis for overall survival in the training cohort Variables Univariate analysis HR(95%CI) P Gender female 1.00 male 0.98(0.58–1.66) 0.947 Age < 60 1.00 ≥ 60 1.08(0.64–1.80) 0.780 Support without 1.00 exist 0.87(0.51–1.50) 0.626 Chemo without 1.00 exist 0.64(0.38–1.08) 0.093 Size of the tumor < 5cm 1.00 ≥ 5cm 0.86(0.48–1.55) 0.610 Obstructive site rectum 1.00 colon 0.62(0.28–1.37) 0.239 Surgical procedure Palliative resection 1.00 Radical resection 0.72(0.41–1.25) 0.242 T stage T1 + 2 + 3 1.00 T4 2.62(1.55–4.42) < 0.001 N stage N0 + 1 1.00 N2 1.91(1.1–3.40) 0.024 M stage M0 1.00 M1 3.13(1.84–5.33) < 0.001 TNM stage I + II 1.00 III + IV 1.98(1.00-3.92) 0.050 Grade G3 1.00 G1、G2 0.17(0.08–0.37) < 0.001 Obstructive type incomplete 1.00 complete 0.52(0.28–0.99) 0.046 WBC(*10 9 /L) < 13.83 1.00 ≥ 13.83 1.78(0.90–3.54) 0.097 mono(*10 9 /L) < 0.9 1.00 ≥ 0.9 1.89(0.92–3.87) 0.083 PLT(*10 9 /L) < 344 1.00 ≥ 344 1.77(1.03–3.03) 0.041 MPV(fL) < 10.9 1.00 ≥ 10.9 0.50(0.2–1.25) 0.137 TP(g/L) < 50 1.00 ≥ 50 1.65(0.66–4.15) 0.286 ALB(g/L) < 26 1.00 ≥ 26 1.51(0.64–3.56) 0.343 CEA(ng/mL) < 5 1.00 ≥ 5 1.38(0.82–2.32) 0.231 CA19-9(U/mL) < 37 1.00 ≥ 37 3.59(2.03–6.32) < 0.001 LMR < 2.7 1.00 ≥ 2.7 0.52(0.28–0.96) 0.038 SIRI < 5.8 1.00 ≥ 5.8 1.56(0.87–2.78) 0.133 AGR < 1.26 1.00 ≥ 1.26 1.79(1.04–3.08) 0.036 PLR < 245 1.00 ≥ 245 1.88(1.11–3.20) 0.019 MLR < 0.03 1.00 ≥ 0.03 0.64(0.38–1.07) 0.087 To identify prognostic indicators for OS, univariate Cox proportional hazards regression analysis was performed on the training set. Of the 27 clinical and laboratory variables evaluated, 10 were significantly associated with OS (P < 0.05): T stage, N stage, M stage, TNM stage, obstruction type, tumor grade, PLT, CA19-9, AGR, and PLR (Table 4). Specific hazard ratios (HR) and 95% confidence intervals (CI) were as follows: T stage: HR = 2.62 (95%CI: 1.55–4.42), P < 0.001; N stage: HR = 1.97 (1.10–3.40), P < 0.001; M stage: HR = 3.13 (1.84–5.33), P < 0.001; TNM stage: HR = 0.48 (0.36–0.62), P < 0.001; Grade: HR = 2.13 (1.18–3.76), P < 0.001; Obstruction type: HR = 0.52 (0.28–0.99), P = 0.045; PLT: HR = 1.77 (1.03–3.03), P = 0.038; CA19-9: HR = 3.59 (2.03–6.32), P < 0.001; AGR: HR = 1.79 (1.04–3.08), P = 0.037; PLR: HR = 1.88 (1.11–3.20), P = 0.019. Kaplan-Meier survival curves were generated to visualize the survival differences associated with each prognostic variable (Figs. 2 , 3 ). The divergence between curves reflects the magnitude of prognostic impact, with greater separation indicating more significant differences in survival outcomes. Subsequently, multivariate Cox regression analysis was conducted using the 10 significant variables from the univariate analysis. Five independent prognostic factors remained statistically significant: M stage: HR = 1.917 (1.005–3.657), P = 0.048; Tumor grade: HR = 0.229 (0.096–0.543), P = 0.001; CA19-9: HR = 3.919 (2.038–7.538), P < 0.001; AGR: HR = 2.108 (1.158–3.817), P = 0.015; PLR: HR = 1.873 (1.013–3.464), P = 0.045. These variables were retained in the final prognostic model (Fig. 4 ). Elevated M stage, CA19-9, AGR, and PLR levels, along with poorly differentiated tumor grade, were identified as independent risk factors associated with decreased overall survival in patients with oCRC (P < 0.05 for all). Nomogram Model for Predicting Prognosis in oCRC Based on the five independent prognostic factors identified through multivariate Cox regression analysis—M stage, tumor grade, CA19-9 level, AGR, and PLR—a nomogram was constructed to estimate the 1-year and 3-year OS probabilities in patients with oCRC (Fig. 5 ). In the nomogram, each prognostic variable was assigned a weighted point value according to its relative contribution to the survival outcome. By summing the individual scores across all variables, a total risk score was calculated for each patient. This total score was then mapped to the corresponding survival probability using the nomogram’s risk scale. For illustrative purposes, consider a hypothetical patient presenting with the following characteristics: presence of distant metastasis (M1, 66 points), poorly differentiated tumor (Grade III, 100 points), AGR < 1.26 (0 points), PLR < 245 (0 points), and CA19-9 < 37 U/mL (0 points). The cumulative total score for this patient is 166. According to the nomogram, this score corresponds to an estimated 1-year OS probability of approximately 70% and a 3-year OS probability of approximately 28%. This predictive model enables individualized risk assessment and may facilitate more informed clinical decision-making regarding treatment planning and follow-up strategies for patients with obstructive CRC. Model performance and validation The predictive performance of the nomogram model was evaluated through discrimination, calibration, and clinical utility analyses. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC). The model demonstrated good discriminative ability, with an AUC of 0.721 in the training cohort and 0.776 in the validation cohort (Fig. 6 ), indicating acceptable predictive accuracy for OS. Calibration was evaluated using calibration plots, which compared the predicted survival probabilities with the actual observed outcomes at 1 and 3 years. The calibration curves for both the training and validation sets showed close alignment with the ideal 45-degree line, indicating high concordance between predicted and observed survival probabilities (Fig. 7 ). To assess the clinical applicability of the nomogram, decision curve analysis (DCA) was performed. As shown in Fig. 8 , the nomogram model demonstrated a higher net benefit across a range of threshold probabilities (50%-90%) compared with the default strategies of treating all patients or treating none. This indicates that the model provides meaningful clinical utility and can support personalized decision-making in the management of patients with oCRC. Discussion CRC remains one of the leading causes of cancer-related mortality globally. According to the National Comprehensive Cancer Network (NCCN) guidelines, intestinal obstruction is considered a high-risk factor for recurrence and poor outcomes. CRC-associated intestinal obstruction often presents complex clinical challenges and is typically associated with more advanced disease stages, poorer tumor differentiation, and a higher likelihood of metastasis compared to non-obstructive CRC. Despite its clinical significance, prognostic models specifically tailored for oCRC remain inadequately developed. In the present study, we constructed a nomogram model based on five independent prognostic factors—M stage, tumor differentiation, AGR, PLR, and CA19-9—identified through univariate and multivariate Cox regression analyses. This model was designed to provide individualized estimates of 1-year and 3-year overall survival OS in patients with oCRC, thereby facilitating more informed clinical decision-making. The American Joint Committee on Cancer (AJCC) TNM staging system continues to serve as a cornerstone in prognostic stratification for CRC. In our analysis, distant metastasis (M stage) emerged as a robust and independent predictor of poor survival, in line with existing literature. Although N stage demonstrated significance in univariate analysis, it did not retain its predictive value in multivariate modeling, potentially due to limited sample size or interaction with other variables in the final model. Tumor differentiation is another critical determinant of CRC prognosis. Poorly differentiated tumors exhibit greater cellular heterogeneity, increased invasiveness, and early metastatic potential. Our findings reaffirm that lower differentiation grades are significantly associated with reduced survival and serve as independent prognostic indicators for oCRC. The tumor microenvironment—comprising neutrophils, lymphocytes, monocytes, and platelets—plays a pivotal role in cancer progression and host immune responses[ 24 ]. Several inflammation-based biomarkers, including NLR, LMR, PLR, SIRI, and prognostic nutritional index (PNI), have been explored for their prognostic relevance in CRC [ 25 , 26 ]. In our study, PLR was identified as an independent prognostic factor. Elevated PLR may reflect a pro-inflammatory and immunosuppressive milieu that facilitates tumor progression. Nutritional status, as assessed by serum albumin and globulin levels, also influences cancer outcomes. AGR, a composite marker of nutrition and systemic inflammation, demonstrated significant prognostic value in this cohort. Hypoalbuminemia often indicates malnutrition or systemic inflammation, while elevated globulin levels may reflect chronic immune activation. A low AGR (< 1.26 in our study) was associated with worse prognosis, consistent with previous findings linking it to tumor burden, hepatic metastasis, and diminished chemotherapy tolerance. Tumor biomarkers such as CEA, CA19-9, and CA242 are widely utilized in CRC diagnosis and surveillance[ 27 ]. Elevated preoperative levels of these markers are correlated with tumor aggressiveness and poor prognosis[ 28 ]. In this study, CA19-9 ≥ 37 U/mL emerged as an independent prognostic factor, likely reflecting a higher tumor burden, greater lymph node involvement, and poor differentiation. These findings support the inclusion of CA19-9 in prognostic models for oCRC. Compared to the traditional TNM staging system, nomogram-based prediction models offer a more comprehensive and individualized assessment by integrating multiple prognostic variables[ 29 ] [ 30 , 31 ]. Nomograms have been successfully applied across various malignancies—including gastric, colorectal, and prostate cancers—to improve survival prediction and guide personalized treatment strategies. [ 32 – 35 ]. Our model demonstrated good discrimination and calibration in both the training and validation cohorts. DCA further confirmed its clinical utility by showing superior net benefit across a wide range of threshold probabilities. These findings suggest that the model has potential value in routine clinical practice for stratifying oCRC patients based on mortality risk. Nonetheless, the study has limitations. It is a single-center retrospective analysis, which may introduce selection bias. Additionally, the relatively small sample size and limited follow-up duration may affect the generalizability and robustness of the model. Future studies incorporating larger, multicenter cohorts with extended follow-up are warranted to further validate and refine the predictive accuracy of the proposed nomogram. Conclusion This study identified M stage, tumor differentiation, AGR, PLR, and CA19-9 as independent prognostic factors for overall survival in patients with oCRC through univariate and multivariate Cox regression analyses. Based on these variables, a nomogram was developed to provide an individualized and visual tool for survival prediction. The model demonstrated favorable discriminatory ability and calibration in both the training and validation cohorts, as evidenced by ROC and DCA. These findings suggest that the proposed nomogram may serve as a practical and reliable prognostic tool to support clinical decision-making and personalized risk stratification in patients with oCRC. Abbreviations oCRC obstructive colorectal cancer Alb Albumin Glo Globulin HDL High-density lipoprotein CEA carcinoembry onic antigen CA19-9 carbohydrate antigens 19 − 9 NLR neutrophil to lymphocyte ratio LMR lymphocyte to monocyte ratio SIRI systemic inflammatory response index PLR platelet to lymphocyte ratio MLR monocyte to lymphocyte ratio AGR albumin globulin ratio AJCC American Joint Committee on Cancer NCCN National Comprehensive Cancer Network. Declarations Funding This work was supported by the Joint Funds for the Innovation of Science and Technology, Fujian province [grant numbers: 2018Y9203 to Jun-rong Zhang]; and the Fujian Provincial Natural Science Foundation of China [grant number 2024J01596 to Ping-xia Lu]. Acknowledgements Not applicable Author contributions Conceptualization: Pingxia Lu and Zhengyuan Huang; Data curation: Wanyun Su and Dingman Huang ; Formal analysis: Wanyun Su and Dingman Huang; Funding acquisition: Junrong Zhang and Pingxia Lu; Investigation: Cuifeng Zheng and BaoWei Xu; Methodology: Wanyun Su and Dingman Huang; Project administration: Zhengyuan Huang and Junrong Zhang; Resources: Zhengyuan Huang, Junrong Zhang, and Xianqiang Chen; Software:Pingxia Lu, Wanyun Su, and Dingman Huang; Supervision: Junrong Zhang and Zhengyuan Huang; Validation: Pingxia Lu, Wanyun Su, and Dingman Huang; Visualization: Cuifeng Zheng, BaoWei Xu, and Pingxia Lu; Roles/Writing – original draft: Pingxia Lu and Wanyun Su; Writing – review & editing:Zhengyuan Huang and Junrong Zhang. Availability of data and materials The data that support the findings of this study are available from the corresponding author,Junrong Zhang and Zhengyuan Huang, upon reasonable request. Corresponding to Junrong Zhang and Zhengyuan Huang when necessary. Ethics approval and consent to participate The study protocol was approved by the Institutional Review Board of Fujian Medical University Union Hospital[2024KJT060]. Written informed consent was obtained from all individual participants prior to their inclusion in the study. All procedures performed in studies involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Consent for publication Not applicable. Competing interests The authors have no competing interests to declare. Clinical Trial Number Not applicable. References Freddie B, Jacques F, Isabelle S, Rebecca LS, Lindsey AT, Ahmedin J. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018, 68. 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Max P, David E-E, Piet K, Ugo Giovanni F, Martin JC, Taimur TS, Joost JCV, Pekka T, Hannu JA, Juha K et al. Predicting the Need for Biopsy to Detect Clinically Significant Prostate Cancer in Patients with a Magnetic Resonance Imaging-detected Prostate Imaging Reporting and Data System/Likert ≥ 3 Lesion: Development and Multinational External Validation of the Imperial Rapid Access to Prostate Imaging and Diagnosis Risk Score. Eur Urol 2022, 82. Pu C, Haipeng C, Fei H, Jiyun L, Hengchang L, Zhaoxu Z, Zhao L. Nomograms predicting cancer-specific survival for stage IV colorectal cancer with synchronous lung metastases. Sci Rep 2022, 12. 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. 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1","display":"","copyAsset":false,"role":"figure","size":153655,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of Patient Enrollment\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7554925/v1/c332d01c15f8272c8082df26.png"},{"id":91956215,"identity":"9c8a2a6b-2d61-420a-a562-5daf89a20fa3","added_by":"auto","created_at":"2025-09-23 07:11:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":213604,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curves of OS for patients with oCRC in the training cohort. a T stage, b N stage, c M stage, d TNM stage, e grade，f obstruction type\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7554925/v1/4213d371729fbb4c7458cdb1.png"},{"id":91957903,"identity":"70a18c82-7111-4665-aede-25f569d36522","added_by":"auto","created_at":"2025-09-23 07:27:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":140503,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curves of OS for patients with oCRC in the training set g PLT, h CA19-9,i AGR and j PLR.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7554925/v1/9bcba698ab8ca86fe6e22c1c.png"},{"id":91956225,"identity":"1ec09158-aec4-4c56-a4c9-24fb6908dcf5","added_by":"auto","created_at":"2025-09-23 07:11:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":216254,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of multivariate cox regression analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7554925/v1/9795e00ac76b412e1a32f71f.png"},{"id":91956218,"identity":"22973e34-15c9-4585-ac7b-e43918af5065","added_by":"auto","created_at":"2025-09-23 07:11:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":132174,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for predicting overall survival\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7554925/v1/a2449e79f461fe4596771abf.png"},{"id":91956839,"identity":"7394568d-2805-4677-bc94-0c63725a2bd0","added_by":"auto","created_at":"2025-09-23 07:19:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":228212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve of the predictive model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7554925/v1/b49fa5dc2276250f13c94e78.png"},{"id":91956220,"identity":"fff00a56-54c9-4346-84ee-1ac537548c7d","added_by":"auto","created_at":"2025-09-23 07:11:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":181935,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curves for predicting patient OS at a 1 year and b 3 years in the internal verifcation and c 1 year and d 3 years in the external verification.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7554925/v1/ee3f6f78b5c8bfa4a513c092.png"},{"id":91956223,"identity":"598103b8-0246-4bf6-8503-160ebdbd2ddc","added_by":"auto","created_at":"2025-09-23 07:11:13","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":142459,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision curve analysis of the predictive model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7554925/v1/affe83eb23c6d291aada8471.png"},{"id":92690661,"identity":"6238d27e-9b02-4a25-8a9f-981fc5220761","added_by":"auto","created_at":"2025-10-03 04:53:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2556010,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7554925/v1/548e6bfc-7926-460d-823a-05a4078260c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Predictive Nomogram Model for Overall Survival in Obstructive Colorectal Cancer Based on Clinical and Laboratory Indicators","fulltext":[{"header":"Background","content":"\u003cp\u003eColorectal cancer (CRC) ranks 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]. In 2022, China reported approximately 517,100 new CRC cases, accounting for 10.7% of all malignant tumors diagnosed that year[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A severe and life-threatening complication of CRC is intestinal obstruction, which is frequently encountered in clinical emergency settings and associated with a high mortality rate[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Notably, nearly 20% of CRC patients present with intestinal obstruction at initial diagnosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Compared to non-obstructive CRC cases, patients with obstruction typically exhibit more advanced disease stages, lower tumor differentiation postoperatively, and a significantly higher risk of distant metastasis. Consequently, their 5-year overall survival (OS) rate ranges from only 31\u0026ndash;42%[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNomograms, as visual tools derived from multivariate regression models such as logistic or Cox regression, have been increasingly employed in oncology to support individualized prognostication and clinical decision-making[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These models translate complex statistical data into an accessible graphical interface, allowing for intuitive estimation of outcome probabilities based on multiple clinical variables [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Compared with traditional prognostic scoring systems, nomograms offer improved accuracy and usability by simultaneously incorporating and visualizing multiple independent predictors. For example, a study by Liu et al. developed a nomogram based on established clinical factors to predict survival in stage IV CRC patients with distant metastases, providing a useful tool for guiding therapeutic strategies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEmerging evidence suggests that the host immune system plays a dual role in tumor development, contributing to both tumor suppression and promotion [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Inflammatory responses, reflected in alterations of hematologic biomarkers, may hold prognostic significance in malignancies including CRC [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Several inflammation-based indices such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic inflammatory response index (SIRI) have been independently associated with CRC prognosis [\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, prior studies have largely examined these markers in isolation, without evaluating their combined prognostic utility. Furthermore, the prognostic relevance of inflammation-based biomarkers in patients with obstructive CRC remains underexplored. Additionally, reduced albumin-to-globulin ratio (AGR) has been linked to unfavorable OS outcomes in CRC patients [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven these gaps, the present study aims to comprehensively investigate the prognostic value of preoperative blood-based inflammatory and nutritional markers in patients with oCRC. Furthermore, we sought to develop and validate a nomogram model integrating these parameters to improve individualized survival prediction and inform clinical decision-making.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eThis retrospective study included 167 patients diagnosed with oCRC who were admitted to the Emergency Surgery Department of Fujian Medical University Union Hospital between February 2010 and February 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To develop and internally validate a prognostic model, patients were randomly assigned to a training cohort (n\u0026thinsp;=\u0026thinsp;116) and a validation cohort (n\u0026thinsp;=\u0026thinsp;51) at a 7:3 ratio.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eInclusion criteria were as follows: initial diagnosis of obstructive CRC; histopathological confirmation of primary colorectal adenocarcinoma; clinical and radiological evidence of bowel obstruction; and availability of complete clinical and laboratory data. Exclusion criteria included: pregnancy; psychiatric disorders; hematologic diseases; chronic liver disease; chronic kidney disease; autoimmune disorders; long-term corticosteroid therapy; co-existing infectious diseases involving other organ systems; recurrent CRC or multiple primary CRCs; history of other malignancies; inoperability due to advanced tumor stage or severe cardiopulmonary dysfunction; and incomplete clinical data.\u003c/p\u003e\u003cp\u003e The study protocol was reviewed and approved by the Institutional Review Board of Fujian Medical University Union Hospital. Written informed consent was obtained from all participants prior to data collection, in accordance with the Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003e A retrospective review was conducted using the original electronic medical records of all included patients. Baseline clinical data were extracted, encompassing demographic variables (age and sex), as well as clinical features such as the site and type of bowel obstruction, surgical approach, tumor-node-metastasis (TNM) staging, and histological classification at the time of diagnosis.\u003c/p\u003e\u003cp\u003ePreoperative laboratory parameters were obtained from routine blood tests performed on the day of hospital admission. These included hematologic indices, serum biochemistry profiles\u0026mdash;specifically albumin (Alb), globulin (Glo), and high-density lipoprotein (HDL)\u0026mdash;as well as tumor biomarkers such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9 (CA19-9).\u003c/p\u003e\u003cp\u003eBased on these raw laboratory values, the following inflammation- and nutrition-related composite indices were calculated: neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic inflammatory response index (SIRI), and albumin-to-globulin ratio (AGR).\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using SPSS software version 27.0 (IBM Corp., Armonk, NY, USA) and R software version 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria). Optimal cutoff values for continuous variables were determined using receiver operating characteristic (ROC) curve analysis, with thresholds selected based on the point maximizing the Youden index.\u003c/p\u003e\u003cp\u003eTo assess multicollinearity among candidate variables, the variance inflation factor (VIF) was calculated. Variables exhibiting significant collinearity (VIF\u0026thinsp;\u0026gt;\u0026thinsp;5) were excluded from subsequent analyses. Patients were randomly assigned to either the training cohort (n\u0026thinsp;=\u0026thinsp;116) or the validation cohort (n\u0026thinsp;=\u0026thinsp;51) in a 7:3 ratio. Baseline characteristics between the two cohorts were compared using the chi-square test for categorical variables.\u003c/p\u003e\u003cp\u003eUnivariate Cox proportional hazards regression analyses were conducted to identify potential prognostic factors associated with overall survival (OS). Variables with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis were subsequently included in the multivariate Cox regression model. Independent prognostic factors were identified based on multivariate analysis results (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Model variable selection was performed using backward stepwise regression guided by the Akaike information criterion (AIC) to optimize model fit.\u003c/p\u003e\u003cp\u003eA prognostic nomogram was then constructed based on the final multivariate Cox model using the \"rms\" package in R. The predictive performance of the model was assessed in both the training and validation cohorts by evaluating discrimination, calibration, and clinical utility. Discrimination was measured using time-dependent ROC curves generated with the \"pROC\" package. Calibration was assessed using calibration plots created with the \"rms\" package, comparing predicted and observed survival probabilities. Clinical utility was further evaluated via decision curve analysis (DCA) using the \"rmda\" package in R, which quantified the net benefit across a range of threshold probabilities.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003ePatient characteristics and Cox regression analysis\u003c/h2\u003e\u003cp\u003eBaseline characteristics of patients in the training and validation cohorts were compared using the chi-square test to assess distributional balance. Variables analyzed included demographic data (sex and age), clinical features (stent placement, chemotherapy, tumor size, site of obstruction, type of surgery), tumor staging (T, N, M, and overall TNM stage), histologic grade, obstruction type, and laboratory parameters (WBC, mono, PLT, MPV, TP, ALB, CEA, CA19-9, LMR, SIRI, PLR, PNI, and AGR). Among these, no statistically significant differences were observed between cohorts for most variables (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating comparability between the two groups. However, a significant difference was identified in AGR (P\u0026thinsp;=\u0026thinsp;0.037) and MLR (P\u0026thinsp;=\u0026thinsp;0.034), suggesting mild imbalance in these parameters. Despite this, the overall similarity across variables supports the assumption that the training and validation cohorts are derived from the same underlying population, ensuring generalizability of the predictive model (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\u003eBaseline Characteristics and Laboratory Indicators of Patients in the Training and Validation Cohorts\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=\"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\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003etraining set\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003evalidation set\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAll patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" 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\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.581\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e54.9\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\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e56.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e43.1\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\u003eSupport\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ewithout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e74.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eexist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e25.5\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\u003eChemo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ewithout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e52.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.510\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eexist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e47.1\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\u003eSize of the tumor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;5cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.874\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;5cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e76.5\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\u003eObstructive site\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003erectum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.353\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecolon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e94.1\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\u003eSurgical procedure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePalliative resection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e62.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.982\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRadical resection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e37.3\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\u003eT stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT1\u0026thinsp;+\u0026thinsp;2\u0026thinsp;+\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e54.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.232\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45.1\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\u003eN stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN0\u0026thinsp;+\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e74.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.852\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e25.5\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\u003eM stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e72.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.922\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e27.5\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\u003eTNM stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI\u0026thinsp;+\u0026thinsp;II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.453\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIII་IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e66.7\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\u003eGrade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.228\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG1、G2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e92.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.3\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\u003eObstructive type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eincomplete\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e74.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecomplete\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e25.5\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\u003eWBC(*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;13.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e94.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.282\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;13.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.9\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\u003eMono(*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e88.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e86.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.644\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.7\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\u003ePLT(*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e78.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.632\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e21.6\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\u003eMPV(fL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e90.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.474\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.8\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\u003eTP(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e11.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.785\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e88.2\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\u003eALB(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e92.2\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)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e39.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e60.8\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\u003eCA19-9(U/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e76.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.839\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23.5\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\u003eLMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e68.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.789\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e31.4\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\u003eSIRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e80.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.923\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19.6\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\u003eAGR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e41.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e58.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.037\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e41.2\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\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e51.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.929\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e49.0\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\u003eMLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e51.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.034\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e49.0\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\u003c/table\u003e\u003c/div\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 cox regression analysis for overall survival in the training cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR(95%CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.98(0.58\u0026ndash;1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.947\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.08(0.64\u0026ndash;1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.780\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSupport\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ewithout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eexist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.87(0.51\u0026ndash;1.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.626\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ewithout\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eexist\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.64(0.38\u0026ndash;1.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize of the tumor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;5cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;5cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.86(0.48\u0026ndash;1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.610\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObstructive site\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003erectum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecolon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.62(0.28\u0026ndash;1.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical procedure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePalliative resection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRadical resection\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.72(0.41\u0026ndash;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.242\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT1\u0026thinsp;+\u0026thinsp;2\u0026thinsp;+\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.62(1.55\u0026ndash;4.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN0\u0026thinsp;+\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.91(1.1\u0026ndash;3.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.13(1.84\u0026ndash;5.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI\u0026thinsp;+\u0026thinsp;II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIII\u0026thinsp;+\u0026thinsp;IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.98(1.00-3.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eG1、G2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.17(0.08\u0026ndash;0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObstructive type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eincomplete\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecomplete\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.52(0.28\u0026ndash;0.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC(*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;13.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;13.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.78(0.90\u0026ndash;3.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emono(*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.89(0.92\u0026ndash;3.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLT(*10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.77(1.03\u0026ndash;3.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.041\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMPV(fL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.50(0.2\u0026ndash;1.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.137\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTP(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.65(0.66\u0026ndash;4.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALB(g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.51(0.64\u0026ndash;3.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.343\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEA(ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.38(0.82\u0026ndash;2.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA19-9(U/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.59(2.03\u0026ndash;6.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.52(0.28\u0026ndash;0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSIRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.56(0.87\u0026ndash;2.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.79(1.04\u0026ndash;3.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\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\u003e\u0026lt;\u0026thinsp;245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.88(1.11\u0026ndash;3.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.64(0.38\u0026ndash;1.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo identify prognostic indicators for OS, univariate Cox proportional hazards regression analysis was performed on the training set. Of the 27 clinical and laboratory variables evaluated, 10 were significantly associated with OS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05): T stage, N stage, M stage, TNM stage, obstruction type, tumor grade, PLT, CA19-9, AGR, and PLR (Table\u0026nbsp;4). Specific hazard ratios (HR) and 95% confidence intervals (CI) were as follows: T stage: HR\u0026thinsp;=\u0026thinsp;2.62 (95%CI: 1.55\u0026ndash;4.42), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; N stage: HR\u0026thinsp;=\u0026thinsp;1.97 (1.10\u0026ndash;3.40), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; M stage: HR\u0026thinsp;=\u0026thinsp;3.13 (1.84\u0026ndash;5.33), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; TNM stage: HR\u0026thinsp;=\u0026thinsp;0.48 (0.36\u0026ndash;0.62), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Grade: HR\u0026thinsp;=\u0026thinsp;2.13 (1.18\u0026ndash;3.76), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Obstruction type: HR\u0026thinsp;=\u0026thinsp;0.52 (0.28\u0026ndash;0.99), P\u0026thinsp;=\u0026thinsp;0.045; PLT: HR\u0026thinsp;=\u0026thinsp;1.77 (1.03\u0026ndash;3.03), P\u0026thinsp;=\u0026thinsp;0.038; CA19-9: HR\u0026thinsp;=\u0026thinsp;3.59 (2.03\u0026ndash;6.32), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; AGR: HR\u0026thinsp;=\u0026thinsp;1.79 (1.04\u0026ndash;3.08), P\u0026thinsp;=\u0026thinsp;0.037; PLR: HR\u0026thinsp;=\u0026thinsp;1.88 (1.11\u0026ndash;3.20), P\u0026thinsp;=\u0026thinsp;0.019. Kaplan-Meier survival curves were generated to visualize the survival differences associated with each prognostic variable (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e,\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The divergence between curves reflects the magnitude of prognostic impact, with greater separation indicating more significant differences in survival outcomes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSubsequently, multivariate Cox regression analysis was conducted using the 10 significant variables from the univariate analysis. Five independent prognostic factors remained statistically significant: M stage: HR\u0026thinsp;=\u0026thinsp;1.917 (1.005\u0026ndash;3.657), P\u0026thinsp;=\u0026thinsp;0.048; Tumor grade: HR\u0026thinsp;=\u0026thinsp;0.229 (0.096\u0026ndash;0.543), P\u0026thinsp;=\u0026thinsp;0.001; CA19-9: HR\u0026thinsp;=\u0026thinsp;3.919 (2.038\u0026ndash;7.538), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; AGR: HR\u0026thinsp;=\u0026thinsp;2.108 (1.158\u0026ndash;3.817), P\u0026thinsp;=\u0026thinsp;0.015; PLR: HR\u0026thinsp;=\u0026thinsp;1.873 (1.013\u0026ndash;3.464), P\u0026thinsp;=\u0026thinsp;0.045. These variables were retained in the final prognostic model (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Elevated M stage, CA19-9, AGR, and PLR levels, along with poorly differentiated tumor grade, were identified as independent risk factors associated with decreased overall survival in patients with oCRC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eNomogram Model for Predicting Prognosis in oCRC\u003c/h2\u003e\u003cp\u003eBased on the five independent prognostic factors identified through multivariate Cox regression analysis\u0026mdash;M stage, tumor grade, CA19-9 level, AGR, and PLR\u0026mdash;a nomogram was constructed to estimate the 1-year and 3-year OS probabilities in patients with oCRC (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In the nomogram, each prognostic variable was assigned a weighted point value according to its relative contribution to the survival outcome. By summing the individual scores across all variables, a total risk score was calculated for each patient. This total score was then mapped to the corresponding survival probability using the nomogram\u0026rsquo;s risk scale.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFor illustrative purposes, consider a hypothetical patient presenting with the following characteristics: presence of distant metastasis (M1, 66 points), poorly differentiated tumor (Grade III, 100 points), AGR\u0026thinsp;\u0026lt;\u0026thinsp;1.26 (0 points), PLR\u0026thinsp;\u0026lt;\u0026thinsp;245 (0 points), and CA19-9\u0026thinsp;\u0026lt;\u0026thinsp;37 U/mL (0 points). The cumulative total score for this patient is 166. According to the nomogram, this score corresponds to an estimated 1-year OS probability of approximately 70% and a 3-year OS probability of approximately 28%.\u003c/p\u003e\u003cp\u003eThis predictive model enables individualized risk assessment and may facilitate more informed clinical decision-making regarding treatment planning and follow-up strategies for patients with obstructive CRC.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eModel performance and validation\u003c/h3\u003e\n\u003cp\u003eThe predictive performance of the nomogram model was evaluated through discrimination, calibration, and clinical utility analyses. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC). The model demonstrated good discriminative ability, with an AUC of 0.721 in the training cohort and 0.776 in the validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), indicating acceptable predictive accuracy for OS.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eCalibration was evaluated using calibration plots, which compared the predicted survival probabilities with the actual observed outcomes at 1 and 3 years. The calibration curves for both the training and validation sets showed close alignment with the ideal 45-degree line, indicating high concordance between predicted and observed survival probabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo assess the clinical applicability of the nomogram, decision curve analysis (DCA) was performed. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the nomogram model demonstrated a higher net benefit across a range of threshold probabilities (50%-90%) compared with the default strategies of treating all patients or treating none. This indicates that the model provides meaningful clinical utility and can support personalized decision-making in the management of patients with oCRC.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCRC remains one of the leading causes of cancer-related mortality globally. According to the National Comprehensive Cancer Network (NCCN) guidelines, intestinal obstruction is considered a high-risk factor for recurrence and poor outcomes. CRC-associated intestinal obstruction often presents complex clinical challenges and is typically associated with more advanced disease stages, poorer tumor differentiation, and a higher likelihood of metastasis compared to non-obstructive CRC. Despite its clinical significance, prognostic models specifically tailored for oCRC remain inadequately developed.\u003c/p\u003e\u003cp\u003eIn the present study, we constructed a nomogram model based on five independent prognostic factors\u0026mdash;M stage, tumor differentiation, AGR, PLR, and CA19-9\u0026mdash;identified through univariate and multivariate Cox regression analyses. This model was designed to provide individualized estimates of 1-year and 3-year overall survival OS in patients with oCRC, thereby facilitating more informed clinical decision-making.\u003c/p\u003e\u003cp\u003e The American Joint Committee on Cancer (AJCC) TNM staging system continues to serve as a cornerstone in prognostic stratification for CRC. In our analysis, distant metastasis (M stage) emerged as a robust and independent predictor of poor survival, in line with existing literature. Although N stage demonstrated significance in univariate analysis, it did not retain its predictive value in multivariate modeling, potentially due to limited sample size or interaction with other variables in the final model.\u003c/p\u003e\u003cp\u003eTumor differentiation is another critical determinant of CRC prognosis. Poorly differentiated tumors exhibit greater cellular heterogeneity, increased invasiveness, and early metastatic potential. Our findings reaffirm that lower differentiation grades are significantly associated with reduced survival and serve as independent prognostic indicators for oCRC.\u003c/p\u003e\u003cp\u003eThe tumor microenvironment\u0026mdash;comprising neutrophils, lymphocytes, monocytes, and platelets\u0026mdash;plays a pivotal role in cancer progression and host immune responses[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Several inflammation-based biomarkers, including NLR, LMR, PLR, SIRI, and prognostic nutritional index (PNI), have been explored for their prognostic relevance in CRC [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In our study, PLR was identified as an independent prognostic factor. Elevated PLR may reflect a pro-inflammatory and immunosuppressive milieu that facilitates tumor progression.\u003c/p\u003e\u003cp\u003eNutritional status, as assessed by serum albumin and globulin levels, also influences cancer outcomes. AGR, a composite marker of nutrition and systemic inflammation, demonstrated significant prognostic value in this cohort. Hypoalbuminemia often indicates malnutrition or systemic inflammation, while elevated globulin levels may reflect chronic immune activation. A low AGR (\u0026lt;\u0026thinsp;1.26 in our study) was associated with worse prognosis, consistent with previous findings linking it to tumor burden, hepatic metastasis, and diminished chemotherapy tolerance.\u003c/p\u003e\u003cp\u003eTumor biomarkers such as CEA, CA19-9, and CA242 are widely utilized in CRC diagnosis and surveillance[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Elevated preoperative levels of these markers are correlated with tumor aggressiveness and poor prognosis[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In this study, CA19-9\u0026thinsp;\u0026ge;\u0026thinsp;37 U/mL emerged as an independent prognostic factor, likely reflecting a higher tumor burden, greater lymph node involvement, and poor differentiation. These findings support the inclusion of CA19-9 in prognostic models for oCRC.\u003c/p\u003e\u003cp\u003eCompared to the traditional TNM staging system, nomogram-based prediction models offer a more comprehensive and individualized assessment by integrating multiple prognostic variables[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Nomograms have been successfully applied across various malignancies\u0026mdash;including gastric, colorectal, and prostate cancers\u0026mdash;to improve survival prediction and guide personalized treatment strategies. [\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur model demonstrated good discrimination and calibration in both the training and validation cohorts. DCA further confirmed its clinical utility by showing superior net benefit across a wide range of threshold probabilities. These findings suggest that the model has potential value in routine clinical practice for stratifying oCRC patients based on mortality risk.\u003c/p\u003e\u003cp\u003eNonetheless, the study has limitations. It is a single-center retrospective analysis, which may introduce selection bias. Additionally, the relatively small sample size and limited follow-up duration may affect the generalizability and robustness of the model. Future studies incorporating larger, multicenter cohorts with extended follow-up are warranted to further validate and refine the predictive accuracy of the proposed nomogram.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identified M stage, tumor differentiation, AGR, PLR, and CA19-9 as independent prognostic factors for overall survival in patients with oCRC through univariate and multivariate Cox regression analyses. Based on these variables, a nomogram was developed to provide an individualized and visual tool for survival prediction. The model demonstrated favorable discriminatory ability and calibration in both the training and validation cohorts, as evidenced by ROC and DCA. These findings suggest that the proposed nomogram may serve as a practical and reliable prognostic tool to support clinical decision-making and personalized risk stratification in patients with oCRC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eoCRC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eobstructive colorectal cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAlb\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAlbumin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGlo\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlobulin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHDL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHigh-density lipoprotein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCEA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecarcinoembry onic antigen\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCA19-9\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecarbohydrate antigens 19\u0026thinsp;\u0026minus;\u0026thinsp;9\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eneutrophil to lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLMR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elymphocyte to monocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSIRI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esystemic inflammatory response index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eplatelet to lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emonocyte to lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAGR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ealbumin globulin ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAJCC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAmerican Joint Committee on Cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNCCN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNational Comprehensive Cancer Network.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Joint Funds for the Innovation of Science and Technology, Fujian province [grant numbers: 2018Y9203 to Jun-rong Zhang]; and the Fujian Provincial Natural Science Foundation of China [grant number 2024J01596 to Ping-xia Lu].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Pingxia Lu and Zhengyuan Huang; Data curation: Wanyun Su and Dingman Huang ; Formal analysis: \u0026nbsp;Wanyun Su and Dingman Huang; Funding acquisition: \u0026nbsp;Junrong Zhang and Pingxia Lu; Investigation: Cuifeng Zheng and BaoWei Xu; Methodology: Wanyun Su and Dingman Huang; Project administration: Zhengyuan Huang and Junrong Zhang; Resources: Zhengyuan Huang, Junrong Zhang, and Xianqiang Chen; Software:Pingxia Lu, Wanyun Su, and Dingman Huang; Supervision: Junrong Zhang and Zhengyuan Huang; Validation: Pingxia Lu, Wanyun Su, and Dingman Huang; Visualization: Cuifeng Zheng, BaoWei Xu, and Pingxia Lu; Roles/Writing \u0026ndash; original draft: Pingxia Lu and Wanyun Su; Writing \u0026ndash; review \u0026amp; editing:Zhengyuan Huang and Junrong Zhang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author,Junrong Zhang and Zhengyuan Huang, upon reasonable request. Corresponding to Junrong Zhang and Zhengyuan Huang when necessary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Review Board of Fujian Medical University Union Hospital[2024KJT060]. Written informed consent was obtained from all individual participants prior to their inclusion in the study. All procedures performed in studies involving human participants were conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFreddie B, Jacques F, Isabelle S, Rebecca LS, Lindsey AT, Ahmedin J. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Cancer Cell. 2019;36:319\u0026ndash;e336317.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarc VE, Bernhard M, Gabriela B, Tessa F, Sarah EC, Lucie L, Nacilla H, Florence M, Mihaela A, Angela V et al. The Link between the Multiverse of Immune Microenvironments in Metastases and the Survival of Colorectal Cancer Patients. Cancer Cell 2018, 34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMingyang S, Reiko N, Molin W, Andrew TC, Zhi Rong Q, Kentaro I, Xuehong Z, Kimmie N, Sun AK, Kosuke M et al. Plasma 25-hydroxyvitamin D and colorectal cancer risk according to tumour immunity status. Gut 2015, 65.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNathan AC, Jeffrey M, Puneeth I, Chul A, Kenneth DW, Hak C, Robert T. Neutrophil-lymphocyte and platelet-lymphocyte ratios as prognostic factors after stereotactic radiation therapy for early-stage non-small-cell lung cancer. J Thorac Oncol 2014, 10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHariruk Y, Akihisa M, Masao M, Satoshi M, Nobuyuki S, Marina Y, Eiji U. Prognostic Significance of Neutrophil-to-Lymphocyte Ratio and Platelet-to-Lymphocyte Ratio in Oncologic Outcomes of Esophageal Cancer: A Systematic Review and Meta-analysis. Ann Surg Oncol 2015, 23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYi-Wei D, Yan-Qiang S, Li-Wen H, Pei-Zhu S. Prognostic significance of neutrophil-to-lymphocyte ratio in rectal cancer: a meta-analysis. Onco Targets Ther 2016, 9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuifang G, Yixing W, Yixin Z, Qi Q, Yijun Z, Haohua W, Bei Z, Liangping X. Immune cell concentrations among the primary tumor microenvironment in colorectal cancer patients predicted by clinicopathologic characteristics and blood indexes. J Immunother Cancer 2019, 7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJoseph CYC, David LC, Connie ID, Alexander E, Nick P, Anthony G, Stephen JC. The Lymphocyte-to-Monocyte Ratio is a Superior Predictor of Overall Survival in Comparison to Established Biomarkers of Resectable Colorectal Cancer. Ann Surg 2016, 265.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWilliam D, Crystal B, Will SR, Hoang N. The Effectiveness of Albumin-to-Globulin Ratio as a Prognostic Biomarker in Primary Gastrointestinal Cancer: A Systematic Review and Meta-Analysis. Cureus 2024, 16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTakehito Y, Kenji K, Kazutaka O. Inflammation-Related Biomarkers for the Prediction of Prognosis in Colorectal Cancer Patients. Int J Mol Sci 2021, 22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYasuhiko M, Yasuhiro I, Koji T, Junichirou H, Keiichi U, Masato K. Prognostic nutritional index predicts postoperative outcome in colorectal cancer. World J Surg 2013, 37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTokunaga R, Sakamoto Y, Nakagawa S, Izumi D, Kosumi K, Taki K, Higashi T, Miyata T, Miyamoto Y, Yoshida N, Baba H. Comparison of systemic inflammatory and nutritional scores in colorectal cancer patients who underwent potentially curative resection. Int J Clin Oncol. 2017;22:740\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDouglas PC. Biomarkers for immune checkpoint inhibitors: The importance of tumor topography and the challenges to cytopathology. Cancer Cytopathol 2017, 126.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCora NS. Are nomograms better than currently available stage groupings for bladder cancer? J Clin Oncol 2006, 24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDuan C, Zhou X, Wang J, Li N, Liu F, Gao S, Liu X, Xu W. A radiomics nomogram for predicting the meningioma grade based on enhanced T(1)WI images. Br J Radiol. 2022;95:20220141.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiao H, Zhou L, De-Yuan F. ASO Author Reflections: Simplified Nomogram Predictive of Survival for Young Breast Cancer Patients. Ann Surg Oncol 2022, 29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZiHao N, BoLin W, Meng L, Xue H, XiaoWen H, Yue Z, Wen C, CunLi G. Prediction Model and Nomogram of Early Recurrence of Hepatocellular Carcinoma after Radiofrequency Ablation Based on Logistic Regression Analysis. Ultrasound Med Biol 2022, 48.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiao H, Zhou L, Wei L, Guojian X, Xusen L, Jiaxiang G, Chenxiao L, Xiangnan X, Deyuan F. Survival Nomogram for Young Breast Cancer Patients Based on the SEER Database and an External Validation Cohort. Ann Surg Oncol 2022, 29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMasaya N, Colin MC, Laura HT, Mithat G, Yelena YJ, Steven BM, Daniela M, Daniel GC, Murray FB, Vivian ES. Validation of the Memorial Sloan Kettering Gastric Cancer Post-Resection Survival Nomogram: Does It Stand the Test of Time? J Am Coll Surg, 235.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMax P, David E-E, Piet K, Ugo Giovanni F, Martin JC, Taimur TS, Joost JCV, Pekka T, Hannu JA, Juha K et al. Predicting the Need for Biopsy to Detect Clinically Significant Prostate Cancer in Patients with a Magnetic Resonance Imaging-detected Prostate Imaging Reporting and Data System/Likert\u0026thinsp;\u0026ge;\u0026thinsp;3 Lesion: Development and Multinational External Validation of the Imperial Rapid Access to Prostate Imaging and Diagnosis Risk Score. Eur Urol 2022, 82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePu C, Haipeng C, Fei H, Jiyun L, Hengchang L, Zhaoxu Z, Zhao L. Nomograms predicting cancer-specific survival for stage IV colorectal cancer with synchronous lung metastases. Sci Rep 2022, 12.\u003c/span\u003e\u003c/li\u003e\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":"intestinal obstruction of colorectal cancer, prognostic, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-7554925/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7554925/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eObstructive colorectal cancer (oCRC) correlates with advanced disease and poor outcomes. This study aimed to identify independent prognostic factors using clinical and laboratory data and construct a predictive nomogram for oCRC patients' individualized survival estimation and clinical decision-making.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA retrospective cohort of 167 patients with histologically confirmed oCRC admitted to Fujian Medical University Union Hospital between February 2010 and February 2021 was analyzed. Patients were randomly divided into a training cohort (n\u0026thinsp;=\u0026thinsp;116) and a validation cohort (n\u0026thinsp;=\u0026thinsp;51) in a 7:3 ratio. Prognostic variables were identified using univariate and multivariate Cox proportional hazards regression analyses. A nomogram was developed based on independent prognostic factors. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA) to evaluate its discrimination, calibration, and clinical utility, respectively.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMultivariate Cox regression analysis identified five independent prognostic factors: M stage (HR\u0026thinsp;=\u0026thinsp;1.917, 95% CI:1.005\u0026ndash;3.657, P\u0026thinsp;=\u0026thinsp;0.048), tumor grade (HR\u0026thinsp;=\u0026thinsp;0.229, 95% CI: 0.096\u0026ndash;0.543, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CA19-9 (HR\u0026thinsp;=\u0026thinsp;3.919, 95% CI: 2.038\u0026ndash;7.538, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), albumin-to-globulin ratio (AGR; HR\u0026thinsp;=\u0026thinsp;2.103, 95% CI:1.158\u0026ndash;3.817, P\u0026thinsp;=\u0026thinsp;0.015), and platelet-to-lymphocyte ratio (PLR; HR\u0026thinsp;=\u0026thinsp;1.873, 95% CI: 1.013\u0026ndash;3.464, P\u0026thinsp;=\u0026thinsp;0.045). These variables were incorporated into a prognostic nomogram. The model showed good discriminatory ability (AUC\u0026thinsp;=\u0026thinsp;0.721 in the training set; 0.776 in the validation set), reliable calibration, and strong clinical applicability as demonstrated by DCA.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe nomogram (incorporating M stage, tumor grade, CA19-9, AGR, PLR) provides accurate, individualized prognosis for oCRC patients, and may aid clinical risk stratification and therapeutic decision-making.\u003c/p\u003e","manuscriptTitle":"A Predictive Nomogram Model for Overall Survival in Obstructive Colorectal Cancer Based on Clinical and Laboratory Indicators","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 07:11:08","doi":"10.21203/rs.3.rs-7554925/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":"83ca9fb2-f580-4204-9367-b0b8f6af9d6e","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-12T09:53:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 07:11:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7554925","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7554925","identity":"rs-7554925","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}