A prediction nomogram for neural perineural invasion in colorectal cancer patients: A retrospective study

A prediction nomogram for neural perineural invasion in colorectal cancer patients: A retrospective study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A prediction nomogram for neural perineural invasion in colorectal cancer patients: A retrospective study Yao Que, Ruiping Wu, Hong Li, Jinli Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3524524/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Mar, 2024 Read the published version in BMC Surgery → Version 1 posted 11 You are reading this latest preprint version Abstract Background Perineural invasion (PNI), as the fifth recognized pathway for the spread and metastasis of colorectal cancer (CRC), has increasingly garnered widespread attention. The preoperative identification of whether colorectal cancer (CRC) patients exhibit PNI can assist clinical practitioners in enhancing preoperative decision-making, including determining the necessity of neoadjuvant therapy and the appropriateness of surgical resection. The primary objective of this study is to construct and validate a preoperative predictive model for assessing the risk of perineural invasion (PNI) in patients diagnosed with colorectal cancer (CRC). Materials and methods A total of 335 patients diagnosed with colorectal cancer (CRC) at a single medical center were subject to random allocation, with 221 individuals assigned to a training dataset and 114 to a validation dataset, maintaining a ratio of 2:1. Comprehensive preoperative clinical and pathological data were meticulously gathered for analysis. Initial exploration involved conducting univariate logistic regression analysis, with subsequent inclusion of variables demonstrating a significance level of p < 0.05 into the multivariate logistic regression analysis, aiming to ascertain independent predictive factors, all while maintaining a p-value threshold of less than 0.05. From the culmination of these factors, a nomogram was meticulously devised. Rigorous evaluation of this nomogram's precision and reliability encompassed Receiver Operating Characteristic (ROC) curve analysis, calibration curve assessment, and Decision Curve Analysis (DCA). The robustness and accuracy were further fortified through application of the bootstrap method, which entailed 1000 independent dataset samplings to perform discrimination and calibration procedures. Results The results of multivariate logistic regression analysis unveiled independent risk factors for perineural invasion (PNI) in patients diagnosed with colorectal cancer (CRC). These factors included tumor histological differentiation (grade) (OR = 0.15, 95% CI = 0.03–0.74, p = 0.02), primary tumor location (OR = 2.49, 95% CI = 1.21–5.12, p = 0.013), gross tumor type (OR = 0.42, 95% CI = 0.22–0.81, p = 0.01), N staging in CT (OR = 3.44, 95% CI = 1.74–6.80, p < 0.001), carcinoembryonic antigen (CEA) level (OR = 3.13, 95% CI = 1.60–6.13, p = 0.001), and platelet-to-lymphocyte ratio (PLR) (OR = 2.07, 95% CI = 1.08–3.96, p = 0.028).These findings formed the basis for constructing a predictive nomogram, which exhibited an impressive area under the receiver operating characteristic (ROC) curve (AUC) of 0.772 (95% CI, 0.712–0.833). The Hosmer-Lemeshow test confirmed the model's excellent fit (p = 0.47), and the calibration curve demonstrated consistent performance. Furthermore, decision curve analysis (DCA) underscored a substantial net benefit across the risk range of 13–85%, reaffirming the nomogram's reliability through rigorous internal validation. Conclusion We have formulated a highly reliable nomogram that provides valuable assistance to clinical practitioners in preoperatively assessing the likelihood of perineural invasion (PNI) among colorectal cancer (CRC) patients. This tool holds significant potential in offering guidance for treatment strategy formulation. Colorectal cancer Perineural invasion Nomogram Surgery Systemic inflammatory markers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Colorectal cancer (CRC) stands as a prominent driver of cancer-related occurrences and fatalities globally, holding the distinction of being the second most prevalent malignancy in adults and a substantial contributor to cancer-related mortality [ 1 ] . According to projections from the American Cancer Society, the year 2023 is expected to witness an estimated 153,020 new CRC diagnoses alongside 52,550 unfortunate fatalities attributed to the disease [ 2 ] . Despite the pivotal roles played by tumor histological grading and clinical staging in shaping patient prognoses, recent investigations have spotlighted an interconnectedness between perineural invasion (PNI) and unfavorable clinical outcomes among individuals affected by CRC [ 3 , 4 ] . The term "perineural invasion" (PNI) refers to the infiltration of tumor cells into various layers of nerve walls, nerve bundle sheaths, or the encirclement of nerves by tumor cells exceeding 33% [ 5 ] . Within the colorectal cancer (CRC) patient population, the prevalence of perineural invasion (PNI) ranges between 20% and 57% [ 6 ] . Several pertinent studies have established a significant correlation between PNI and reduced patient survival rates, augmented tumor recurrence, and heightened metastasis rates [ 7 , 8 ] . PNI has emerged as a predictive factor for CRC progression or recurrence, potentially offering valuable insights for clinical practitioners when devising patient-specific treatment strategies [ 9 , 10 ] . Furthermore, in accordance with the American Joint Committee on Cancer (AJCC) staging manual, perineural invasion (PNI) stands out as a specific and significant prognostic indicator for colorectal cancer [ 11 ] . The clinical practice guidelines outlined by the National Comprehensive Cancer Network (NCCN) similarly recognize PNI as a high-risk factor for postoperative recurrence in colorectal cancer and advocate for adjuvant therapy in cases of stage II colorectal cancer combined with PNI [ 12 ] . Prior investigations have established a connection between PNI status and the secretion of extracellular vesicles (EVs), as well as the expression levels of plasma miR-21 and nerve growth factor (NGF) [ 13 – 15 ] , though their clinical utility as biomarkers remains somewhat limited. While perineural invasion (PNI) holds significant prognostic value, its assessment is currently reliant on postoperative pathological biopsies. The preoperative identification of PNI could serve as a valuable tool for clinicians, aiding in the optimization of clinical decisions such as the necessity of neoadjuvant therapy and the adequacy of surgical resection. As such, the primary objective of this study is to construct and validate a nomogram grounded in the preoperative clinical and pathological attributes of colorectal cancer (CRC) patients. This nomogram is designed to predict the likelihood of PNI occurrence. Materials and Methods Patient Selection This retrospective study protocol received approval from the Ethics Committee of the First People's Hospital of Changde, affiliated with Xiangya Medical College, Central South University. All experiments and methodologies were executed in strict accordance with applicable guidelines and regulations. The analysis encompassed a cohort of 388 patients diagnosed with colorectal cancer (CRC) who underwent surgical procedures at the Department of General Surgery of the First People's Hospital of Changde, affiliated with Xiangya Medical College, Central South University, spanning from June 2021 to June 2023. Inclusion Criteria:1. Patients with a verified diagnosis of colorectal cancer (CRC) and tumor staging conducted in accordance with the guidelines outlined in the American Joint Committee on Cancer (AJCC-TNM Staging Manual, 8th edition).2. Availability of postoperative histopathological reports providing details regarding the perineural invasion (PNI) status.3. Completion of both CT plain scan and enhanced examination conducted within 7 days preceding the surgical intervention. Exclusion Criteria:1. Patients presenting with an inflammatory response or infection.2. Patients exhibiting signs of liver or kidney dysfunction.3. Patients who underwent preoperative treatments, including radiotherapy, chemotherapy, or chemoradiotherapy.4. Patients diagnosed with metastatic cancer or malignancies in other anatomical sites.5. Patients with incomplete medical records or insufficient examination data. Following adherence to the defined inclusion and exclusion criteria, a total of 335 patients were ultimately included in this study. These patients possessed comprehensive data and were subsequently allocated, in a 2:1 ratio, to two distinct datasets: the training group, comprising 221 cases, and the validation group, comprising 114 cases. This allocation was executed through the utilization of computer-generated random numbers, as depicted in Fig. 1 . Data collection The collected dataset encompassed a range of variables, including age, gender, body mass index (BMI), smoking history, serum tumor markers, complete blood count results, T-stage and N-stage determined via computerized tomography (CT) scans, preoperative histological type, histological differentiation (grading), and tumor macroscopic type. The staging of T-stage and N-stage adhered to the guidelines outlined in the 8th edition of the American Joint Committee on Cancer (AJCC) staging manual. Definitions Based on the complete blood count results, we computed the following ratios: neutrophils to lymphocytes (NLR), platelets to lymphocytes (PLR), and lymphocytes to monocytes (LMR). Utilizing ROC curve analysis and the determination of optimal cutoff values, we categorized the patients into two distinct groups for each ratio: NLR ≤ 2.52 and NLR > 2.52 groups, PLR ≤ 136.98 and PLR > 136.98 groups, LMR ≤ 2.76 and LMR > 2.76 groups.Furthermore, employing the reference values of tumor markers, we stratified the patients into groups based on each marker: CEA ≤ 5 (ng/ml) and CEA > 5 (ng/ml) groups, CA199 ≤ 35 (KU/L) and CA199 > 35 (KU/L) groups, CA125 ≤ 35 (KU/L) and CA125 > 35 (KU/L) groups, TAP ≤ 121 (um²) and TAP > 121 (um²) groups. Nomogram construction and performance assessment We conducted a thorough assessment of independent predictive factors (p < 0.05) within the training group through multivariate logistic regression analysis. This data was then utilized to formulate a nomogram designed for predicting the occurrence of perineural invasion (PNI) among colorectal cancer (CRC) patients. To gauge the goodness of fit between observed and predicted values in both the training and validation groups, we employed calibration curves and conducted the Hosmer-Lemeshow test.For an in-depth evaluation of the nomogram's performance in both groups, we utilized Receiver Operating Characteristic (ROC) curves and calculated the Area Under the Curve (AUC). Additionally, we carried out Decision Curve Analysis (DCA) to assess the clinical utility of the nomogram, evaluating the net benefit at various threshold probabilities within both the training and validation datasets. To enhance the robustness of our analysis, we employed the bootstrap method with 1000 iterations for sample resampling, focusing on discrimination and calibration. Statistical Analysis All statistical analyses were conducted utilizing R software (version 4.3.0). Logistic regression analysis, nomogram construction and calibration were executed with the rms package. Calibration curves were generated using the calibrate and val.prob functions from the rms package, while decision curve analysis (DCA) was facilitated with the rmda package.Numerical variables were reported as the median and interquartile range (IQR), and group comparisons were conducted employing the Mann-Whitney U test. Categorical variables were expressed as counts (percentages), and group comparisons were performed using \(\chi 2\) tests or Fisher's exact tests. All statistical tests were two-tailed, with a significance level set at p < 0.05. Results Clinicopathological characteristics of the patients with colorectal cancer (CRC) This study encompassed a total of 335 patients, and an overview of the patients' demographic characteristics is presented in Table 1 . The training dataset consisted of 221 cases, with 110 males and 111 females, while the validation dataset comprised 114 cases, consisting of 54 males and 60 females. Within this cohort, there were 146 cases of rectal cancer, 105 cases of left-sided colon cancer, and 84 cases of right-sided colon cancer. As per the postoperative pathological reports, 148 patients were diagnosed with perineural invasion (PNI). Table 1 Clinicopathologic characteristics of the patients with colorectal cancer (CRC) Characteristics Training set (n = 221) Validation set (n = 114) PNI-negative n (%) PNI-positive n (%) P-value PNI-negative n (%) PNI-positive n (%) P-value Gender 0.942 0.208 Female 59 (50) 52 (51) 32 (47) 28 (61) Male 60 (50) 50 (49) 36 (53) 18 (39) Age (years) 0.761 0.454 Median (IQR) (year) 60 (53, 69) 59.5 (54, 68.75) 66 (55.25, 73) 63.5 (56.25, 68.75) BMI (Kg/m²) 0.266 0.436 Median (IQR)(Kg/m²) 21.63 (20.24, 23.61) 21.52 (19.82, 23.05) 21.6 (20.07, 23.51) 21.05 (20.19, 22.56) Smoking 0.243 0.953 NO 90 (76) 69 (68) 43 (63) 28 (61) YES 29 (24) 33 (32) 25 (37) 18 (39) CEA(ng/ml) 0.006 0.371 ≤ 5 59 (50) 31 (30) 41 (60) 23 (50) ༞5 60 (50) 71 (70) 27 (40) 23 (50) CA199(KU/L) 0.044 0.347 ≤ 35 108 (91) 82 (80) 63 (93) 40 (87) ༞35 11 (9) 20 (20) 5 (7) 6 (13) CA125(KU/L) 0.127 0.391 ≤ 35 113 (95) 101 (99) 66 (97) 43 (93) ༞35 6 (5) 1 (1) 2 (3) 3 (7) TAP(um 2 ) 0.899 0.934 ≤ 121 10 (8) 10 (10) 4 (6) 2 (4) ༞121 109 (92) 92 (90) 64 (94) 44 (96) NLR 0.026 0.024 ≤ 2.52 47 (39) 25 (25) 30 (44) 10 (22) ༞2.52 72 (61) 77 (75) 38 (56) 36 (78) PLR 0.013 0.339 ≤ 136.98 53 (45) 28 (27) 31 (46) 16 (35) ༞136.98 66 (55) 74 (73) 37 (54) 30 (65) LMR 0.383 0.032 ≤ 2.76 39 (33) 27 (26) 24 (35) 7 (15) ༞2.76 80 (67) 75 (74) 44 (65) 39 (85) CT N-stage 0.065 0.416 N0 44 (37) 25 (25) 27 (40) 14 (30) N1-N2 75 (63) 77 (75) 41 (60) 32 (70) CT T-stage 0.001 0.182 T1-T2 64 (54) 32 (31) 35 (51) 17 (37) T3-T4 55 (46) 70 (69) 33 (49) 29 (63) Primary site < 0.001 0.008 Left colon 48 (40) 26 (25) 24 (35) 7 (15) Right colon 32 (27) 17 (17) 23 (34) 12 (26) Rectum 39 (33) 59 (58) 21 (31) 27 (59) Endoscopic biopsy 0.004 0.002 Poorly 10 (8) 16 (16) 5 (7) 7 (15) Moderately 91 (76) 83 (81) 51 (75) 39 (85) Well 18 (15) 3 (3) 12 (18) 0 (0) Pathological type 0.536 0.875 adenocarcinoma 111 (93) 98 (96) 64 (94) 44 (96) non-adenocarcinoma 8 (7) 4 (4) 4 (6) 2 (4) Tumor gross type 0.037 0.071 Ulceration 44 (37) 54 (53) 22 (32) 25 (54) Infiltrative 4 (3) 4 (4) 5 (7) 3 (7) Protruded 69 (58) 40 (39) 40 (59) 17 (37) Other 2 (2) 4 (4) 1 (1) 1 (2) Notes :BMI: Body Mass Index; Primary site: Primary tumor site; Smoking: Smoking history; CT T-stage: Tumor stage determined by CT scan; CT N-stage: Lymph node stage determined by CT scan; Endoscopic biopsy: Histological differentiation (grading); Pathological type: Histological type; Tumor gross type: Gross classification of the tumor; CEA: Carcinoembryonic Antigen; CA199: Carbohydrate Antigen 199; CA125: Carbohydrate Antigen 125; TAP: Aberrant Glycosylation Protein Detection; NLR: Neutrophil-to-Lymphocyte Ratio; PLR: Platelet-to-Lymphocyte Ratio; LMR: Lymphocyte-to-Monocyte Ratio; IQR: Interquartile Range. No statistically significant differences (p > 0.05) were observed between the PNI-positive and PNI-negative groups concerning age, gender, smoking history, body mass index (BMI), abnormal glycoprotein TAP, carbohydrate antigen CA125 (CA125), lymphocyte-to-monocyte ratio (LMR), and histological classification. However, noteworthy distinctions (p < 0.05) emerged between these two groups in terms of T-stage and N-stage in CT, tumor histological differentiation (grading), tumor macroscopic type, tumor primary location, carcinoembryonic antigen (CEA), carbohydrate antigen CA199 (CA199), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR).Multivariate logistic regression analysis identified six clinical and pathological characteristics as independent risk factors for perineural invasion (PNI) in patients with colorectal cancer (CRC). These findings are summarized as follows: tumor histological differentiation (grading) (OR = 0.15, 95% CI = 0.03–0.74, p = 0.02), primary tumor location (OR = 2.49, 95% CI = 1.21–5.12, p = 0.013), tumor macroscopic type (OR = 0.42, 95% CI = 0.22–0.81, p = 0.01), N staging in CT (OR = 3.44, 95% CI = 1.74–6.80, p < 0.001), CEA (OR = 3.13, 95% CI = 1.60–6.13, p = 0.001), and PLR (OR = 2.07, 95% CI = 1.08–3.96, p = 0.028) (as depicted in Table 2 ). Table 2 Univariate and Multivariate logistic regression analysis of the patients with colorectal cancer (CRC) Variable Univariate analysis Multivariate analysis OR (95%CI) P-value OR (95%CI) P-value Age (years) 1.00(0.97–1.02) 0.828 BMI (Kg/m²) 0.93(0.83–1.06) 0.277 Gender Female Reference Male 0.95(0.56–1.61) 0.836 Smoking NO Reference YES 1.48(0.82–2.68) 0.189 CA125(KU/L) ≤35 Reference ༞35 0.19(0.02–1.57) 0.123 CA199(KU/L) ≤35 Reference ༞35 2.39(1.09–5.28) 0.030 CEA(ng/ml) ≤5 Reference ༞5 2.25(1.29–3.92) 0.004 3.13(1.60–6.13) 0.001 TAP(um 2 ) ≤121 Reference ༞121 0.84(0.34–2.12) 0.718 LMR ≤2.76 Reference ༞2.76 1.35(0.76–2.43) 0.308 PLR ≤136.98 Reference ༞136.98 2.12(1.20–3.74) 0.009 2.07(1.08–3.96) 0.028 NLR ≤2.52 Reference ༞2.52 2.01(1.12–3.60) 0.019 CT N-stage N0 Reference N1-N2 2.55(1.46–4.42) 0.001 3.44(1.74–6.80) < 0.001 CT T-stage T1-T2 Reference T3-T4 1.81(1.01–3.24) 0.047 Endoscopic biopsy Poorly Reference Moderately 0.57(0.24–1.33) 0.192 0.69(0.26–1.84) 0.457 Well 0.10(0.02–0.45) 0.002 0.15(0.03–0.74) 0.020 Pathological type adenocarcinoma Reference non-adenocarcinoma 0.57(0.17–1.94) 0.365 Primary site Left colon Reference Right colon 0.98(0.46–2.09) 0.960 0.79(0.34–1.85) 0.593 Rectum 2.79(1.49–5.22) 0.001 2.49(1.21–5.12) 0.013 Tumor gross type Ulceration Reference Infiltrative 0.81(0.19–3.45) 0.781 0.65(0.14–2.91) 0.572 Protruded 0.47(0.27–0.82) 0.008 0.42(0.22–0.81) 0.010 Other 1.63(0.28–9.33) 0.583 1.02(0.16–6.66) 0.983 Nomogram construction and performance A nomogram (see Fig. 2 ) was developed employing the six clinical and pathological characteristics derived from the multivariate logistic regression analysis. In this nomogram, a higher total score denotes an increased risk of perineural invasion (PNI) for each colorectal cancer (CRC) patient. To illustrate, consider a specific CRC patient with the following attribute scores: primary tumor location in the rectum (60 points), N stage in CT as N0 (0 points), ulcerative-type tumor macroscopic classification (22 points), poorly differentiated tumor histological differentiation (100 points), positive carcinoembryonic antigen (CEA) (60 points), and platelet-to-lymphocyte ratio (PLR) > 136.98 (39 points). These values sum up to a total score of 281 points, corresponding to an estimated PNI occurrence probability of approximately 70%. Based on the 50% threshold, this patient falls into the high-risk category and may be considered for neoadjuvant chemotherapy. The receiver operating characteristic (ROC) curve analysis revealed an area under the curve (AUC) of 0.772 (95% CI, 0.712–0.833) for the predicted nomogram within the training group dataset and 0.752 (95% CI, 0.664–0.839) within the validation group dataset (as illustrated in Fig. 3 ). Moreover, the calibration curves exhibited a favorable agreement between the predicted probabilities and the actual outcomes for both the training and validation group datasets (refer to Fig. 4 ). The p-values obtained from the Hosmer-Lemeshow test were 0.47 and 0.72, respectively, indicating no significant deviation and confirming the model's robust fit. Decision curve analysis (DCA) was conducted to assess the practical clinical utility of the nomogram in both the training and validation group datasets (refer to Fig. 5 ). The analysis revealed that within the training group, setting threshold probabilities between 13% and 85%, and in the validation group, between 8% and 68%, could lead to net benefits when guiding clinical interventions for patients with perineural invasion (PNI) based on the nomogram predictions.These findings underscore the nomogram's practical applicability in clinical settings for estimating the risk of perineural invasion (PNI) among colorectal cancer (CRC) patients. Clinical utility of the nomogram Decision curve analysis (DCA) was systematically performed to evaluate the clinical applicability of the nomogram in both the training and validation group datasets (as displayed in Fig. 5 ). The analysis clearly suggests that when the threshold probabilities for the training group and validation group were strategically set within the ranges of 13–85% and 8–68%, respectively, clinical interventions guided by the nomogram's predictions had the potential to yield net benefits for patients dealing with perineural invasion (PNI).This unequivocally underscores the practical significance of the nomogram in real-world clinical contexts, particularly in forecasting the likelihood of PNI among colorectal cancer (CRC) patients. Discussion Given the substantial impact of perineural invasion (PNI) on tumor prognosis, both the NCCN guidelines and the Chinese Society of Clinical Oncology (CSCO) guidelines have underscored the imperative inclusion of PNI status in the pathological reports of individuals afflicted with colorectal cancer (CRC) [ 16 , 17 ] . The emergence of PNI not only introduces discomfort and functional impairment to the affected organs but also escalates the susceptibility to postoperative local recurrence, metastasis, and infiltration, driven by the presence of residual tumor cells in proximity to or enveloping the nerves. This particular factor constitutes a pivotal contributor to unfavorable outcomes [ 18 , 19 ] . Nomograms, adept at amalgamating a plethora of pivotal factors, offer personalized risk assessment and have gained extensive traction for prognostic evaluations among colorectal cancer patients [ 20 – 23 ] . In the current study, we harnessed the potency of multivariate logistic regression analysis to pinpoint independent predictive factors intricately linked to PNI among CRC patients. Subsequently, these critical factors were meticulously employed in the formulation of the nomogram. In our investigation, we have discerned that the platelet-to-lymphocyte ratio (PLR) (p = 0.028) serves as an independent predictive factor for perineural invasion (PNI). The presence of malignant tumors can incite an escalation in platelet (PLT) counts through the release of thrombopoietin (TPO) and interleukin-6 (IL-6). Consequently, heightened platelet activation can foster tumor growth and metastasis, a phenomenon recognized as the "positive feedback loop" in paraneoplastic thrombocytosis [ 24 ] . When peripheral blood lymphocyte counts diminish, the tumor burden tends to rise, thus promoting tumor dissemination and metastasis [ 25 ] . PLR, as a composite biomarker reflecting both inflammatory and immune status within the body, indicates an augmentation in the inflammatory response or a reduction in immune response. This suggests a decline in the body's anti-tumor capacity, which, in turn, results in an unfavorable prognosis and distant metastasis of tumors [ 26 , 27 ] . Although an extensive body of research has illuminated the significance of increased local lymphocyte infiltration within tumors and elevated systemic inflammatory responses as pivotal clinical indicators influencing patient prognosis [ 28 – 32 ] , studies elucidating the impact of PLR on PNI among CRC patients remain scarce. Consequently, further investigations are warranted to delve into the intricate relationship between these factors. Serum tumor markers primarily encompass a group of substances secreted either directly by tumor cells or in response to corresponding stimuli within the body. They play a multifaceted role, aiding not only in the diagnosis of colorectal cancer (CRC) but also in guiding treatment decisions, assessing treatment efficacy, and predicting prognosis. Research has elucidated that elevated preoperative carcinoembryonic antigen (CEA) stands as an independent predictive factor for perineural invasion (PNI) in CRC patients [ 33 ] . Our study's findings corroborate this perspective. Nonetheless, the diagnostic efficacy of CEA on its own remains relatively limited, and it is recommended to be employed in conjunction with other indicators such as PLR.Tumor tissue differentiation (grading) and lymph node stage (N stage) as ascertained through CT scans are pivotal prognostic factors for colorectal cancer (CRC) patients. Within our nomogram, a deterioration in tumor tissue differentiation and an increase in N stage correspond to higher total scores, signifying an elevated likelihood of PNI development in CRC patients. This observation aligns with findings in relevant studies [ 34 , 35 ] . Among the common macroscopic classifications of CRC, including ulcerative, infiltrative, and protruding types, previous research has identified a correlation between macroscopic classification and PNI [ 36 ] . Our study outcomes reinforce this by indicating that the ulcerative type is more predisposed to PNI compared to the protruding type, likely attributed to the tumor's disruption of normal tissues. Our research findings indicate that the incidence of perineural invasion (PNI) is higher in rectal cancer compared to colon cancer, aligning with previously reported results. Specifically, the occurrence rate of PNI in rectal cancer stands at 20.6%, whereas in colon cancer, it is 14.1%. 3 Presently, there is a dearth of research elucidating the impact of left-sided colon cancer versus right-sided colon cancer on PNI. Although our study hints at left-sided colon cancer being more susceptible to perineural invasion (PNI) than its right-sided counterpart, it's essential to acknowledge that our research data are derived from a single-center source and entail a relatively modest sample size, potentially introducing bias. Consequently, further investigations through multi-center studies with larger sample sizes are imperative to gain deeper insights into the influence of the tumor's primary location on perineural invasion (PNI). A previous study employed CT radiomics features to construct a nomogram for predicting perineural invasion (PNI) in colorectal cancer (CRC) patients [ 37 ] . However, the influence of subjectivity on semantic features within regions of interest (ROI) in CT images, such as tumor diameter or size measurements, may lead to substantial disparities in results when assessed by clinical practitioners with varying levels of experience. This limitation hinders its broad applicability.In our research, we amalgamated a selection of frequently employed clinical and pathological features, encompassing serum inflammatory markers, pathological indicators, etc., all grounded in CT images. The outcomes underscored the intimate association between tumor differentiation (grading), primary tumor location, tumor gross classification, N stage in CT, carcinoembryonic antigen (CEA), and platelet-to-lymphocyte ratio (PLR) with the occurrence of perineural invasion (PNI). Notably, these clinical and pathological features not only facilitate a more comprehensive evaluation of CRC but are also readily obtainable.Leveraging these six clinical and pathological features, we meticulously constructed the nomogram, and internal validation corroborated its robustness and precision. Furthermore, the decision curve analysis (DCA) results accentuated the clinical utility of our model in guiding medical practitioners' clinical decisions. While this study boasts the aforementioned advantages, it is important to acknowledge the following limitations. Firstly, this study adopts a retrospective approach and exclusively enrolls patients who underwent curative surgery, potentially introducing selection bias. Thus, further prospective investigations are warranted to corroborate these findings. Secondly, PNI status assessment in this study relied on HE staining rather than the more precise immunohistochemical technique involving anti-s100 antibody, which is favored by pathologists for accurate diagnosis of PNI-positive CRC cases [ 38 , 39 ] . Consequently, our forthcoming research endeavors will integrate this advantageous technique.Thirdly, the model employed in this study encompasses a limited number of risk factors. Therefore, in future validation studies, the incorporation of additional risk factors is imperative to further bolster the model's predictive capability. Lastly, all study participants were derived from a single hospital, and the validation was conducted internally. Hence, it is imperative to validate this nomogram utilizing data from other healthcare institutions to enhance its credibility and robustness. Conclusions In summary, our study furnishes a nomogram that enables clinicians to anticipate the likelihood of perineural invasion (PNI) in colorectal cancer (CRC) patients prior to surgery. This tool can assist medical practitioners in foreseeing CRC metastasis and recurrence, thereby potentially enhancing tumor staging strategies and treatment modalities. Declarations Institutional Review Board Statement The study was approved by the Medical Ethics Committee of the First People’s Hospital in Changde, Hunan Province, China (Protocol Code 2023-197-01). Before collecting any data, each participant provided written, informed consent. Every step was carried out in conformity with the Helsinki Declaration. Acknowledgments We are grateful to all the participants in this study. We acknowledge the help of the First People’s Hospital of Changde City for this study. Disclosure The authors report no conflicts of interest in this work. Authors’ contributions Study concept and design: JL-L, YQ. Drafting of the manuscript: YQ, RP-W. Acquisition of data, analysis, and interpretation of data: YQ,HL. Critical revision of the manuscript: JL-L. Statistical analysis: YQ. Study supervision: JL-L. All authors read and approved the final manuscript. Funding Not Applicable. Data Availability The work dataset supports the findings of this study are available on reasonable request from the corresponding author, and the data are not publicly available due to privacy or ethical restrictions. Consent for publication Not Applicable. References Siegel RL, Miller KD, Wagle NS, et al. Cancer statistics. Cancer J Clin. 2023;73:17–48. Siegel RL, Wagle NS, Cercek A, et al. Colorectal cancer statistics. Cancer J Clin. 2023;73:233–54. Knijn N, Mogk SC, Teerenstra S, et al. 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Clin Transl Oncol. 2022;24:34–47. Bai J, Zhang X, Shi D, et al. Exosomal miR-128-3p Promotes Epithelial-to-Mesenchymal Transition in Colorectal Cancer Cells by Targeting FOXO4 via TGF-β/SMAD and JAK/STAT3 Signaling. Front Cell Dev Biology. 2021;9:568738. Benson AB, Venook AP, Al-Hawary MM, et al. Rectal Cancer, Version 2.2022, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2022;20:1139–67. Colorectal Cancer Working Group C, Chinese Society of Clinical Oncology (CSCO). Chinese Society of Clinical Oncology (CSCO) diagnosis and treatment guidelines for colorectal cancer 2018 (English version). Chin J Cancer Res. 2019;31:117–34. Al-Sukhni E, Attwood K, Gabriel EM, et al. Lymphovascular and perineural invasion are associated with poor prognostic features and outcomes in colorectal cancer: A retrospective cohort study. Int J Surg. 2017;37:42–9. Amit M, Na’ara S, Gil Z. Mechanisms of cancer dissemination along nerves. Nat Rev Cancer. 2016;16:399–408. Yang Z, Li Y, Qin X, et al. Development and Validation of a Prognostic Nomogram for Colorectal Cancer Patients With Synchronous Peritoneal Metastasis. Front Oncol. 2021;11:615321. Ashktorab H, Brim H. Blood-Based Liquid Biopsies: A Noninvasive and Cost-Effective Tool for Improved Risk Assessment and Identification of Lymph Node Metastasis in Patients With Submucosal T1 Colorectal Cancer. Gastroenterology. 2021;161:29–31. Hu DY, Cao B, Li SH, et al. Incidence, risk factors, and a predictive model for lymph node metastasis of submucosal (T1) colon cancer: A population-based study. J Dig Dis. 2019;20:288–93. Xu Y, Chen Y, Long C, et al. Preoperative Predictors of Lymph Node Metastasis in Colon Cancer. Front Oncol. 2021;11:667477. Lin RJ, Afshar-Kharghan V, Schafer AI. Paraneoplastic thrombocytosis: the secrets of tumor self-promotion. Blood. 2014;124:184–7. Nøst TH, Alcala K, Urbarova I, et al. Systemic inflammation markers and cancer incidence in the UK Biobank. Eur J Epidemiol. 2021;36:841–8. Zhang L, Wei Z, Xu A, et al. Can the neutrophil-lymphocyte ratio and platelet-lymphocyte ratio be beneficial in predicting lymph node metastasis and promising prognostic markers of gastric cancer patients? Tumor maker retrospective study. Int J Surg. 2018;56:320–7. Acikgoz O, Cakan B, Demir T, et al. Platelet to lymphocyte ratio is associated with tumor localization and outcomes in metastatic colorectal cancer. Medicine. 2021;100:e27712. Wagner J, Rapsomaniki MA, Chevrier S, et al. A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer. Cell. 2019;177:1330–1345e18. Hao D, Liu J, Chen M, et al. Immunogenomic Analyses of Advanced Serous Ovarian Cancer Reveal Immune Score is a Strong Prognostic Factor and an Indicator of Chemosensitivity. Clin Cancer Res. 2018;24:3560–71. Zhang D, He W, Wu C, et al. Scoring System for Tumor-Infiltrating Lymphocytes and Its Prognostic Value for Gastric Cancer. Front Immunol. 2019;10:71. Donnem T, Kilvaer TK, Andersen S, et al. Strategies for clinical implementation of TNM-Immunoscore in resected nonsmall-cell lung cancer. Ann Oncol. 2016;27:225–32. Dolan RD, McSorley ST, Horgan PG, et al. The role of the systemic inflammatory response in predicting outcomes in patients with advanced inoperable cancer: Systematic review and meta -analysis. Crit Rev Oncol/Hematol. 2017;116:134–46. Gao Y, Wang J, Zhou Y, et al. Evaluation of Serum CEA, CA19-9, CA72-4, CA125 and Ferritin as Diagnostic Markers and Factors of Clinical Parameters for Colorectal Cancer. Sci Rep. 2018;8:2732. Cienfuegos JA, Martínez P, Baixauli J, et al. Perineural Invasion is a Major Prognostic and Predictive Factor of Response to Adjuvant Chemotherapy in Stage I–II Colon Cancer. Ann Surg Oncol. 2017;24:1077–84. Huang X, Liu J, Wu G, et al. Development and Validation of a Nomogram for Preoperative Prediction of Perineural Invasion in Colorectal Cancer. Med Sci Monit. 2019;25:1709–17. Zhou Y, Wang H, Gong H, et al. Clinical significance of perineural invasion in stages II and III colorectal cancer. Pathol - Res Pract. 2015;211:839–44. Huang Y, He L, Dong D, et al. Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model. Chin J Cancer Res. 2018;30:40–50. Shimada Y, Kido T, Kameyama H, et al. Retraction Note to: Clinical significance of perineural invasion diagnosed by immunohistochemistry with anti-S100 antibody in Stage I-III colorectal cancer. Surg Today. 2022;52:519–9. Van Wyk HC, Going J, Horgan P, et al. The role of perineural invasion in predicting survival in patients with primary operable colorectal cancer: A systematic review. Crit Rev Oncol/Hematol. 2017;112:11–20. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Mar, 2024 Read the published version in BMC Surgery → Version 1 posted Editorial decision: Revision requested 17 Jan, 2024 Reviews received at journal 08 Jan, 2024 Reviewers agreed at journal 04 Jan, 2024 Reviewers agreed at journal 04 Jan, 2024 Reviewers agreed at journal 04 Jan, 2024 Reviewers agreed at journal 31 Dec, 2023 Reviewers invited by journal 31 Dec, 2023 Editor assigned by journal 31 Dec, 2023 Editor invited by journal 01 Nov, 2023 Submission checks completed at journal 01 Nov, 2023 First submitted to journal 31 Oct, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3524524","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":244675960,"identity":"76276df7-79ce-4156-b181-208fd090c5b8","order_by":0,"name":"Yao Que","email":"","orcid":"","institution":"The University of South China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Que","suffix":""},{"id":244675961,"identity":"3e1ce2c6-3b95-4e0f-a104-2bac1f04c874","order_by":1,"name":"Ruiping Wu","email":"","orcid":"","institution":"The First People’s Hospital of Changde City","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ruiping","middleName":"","lastName":"Wu","suffix":""},{"id":244675962,"identity":"47e88714-eb93-4ce7-85af-7c7d7a24751f","order_by":2,"name":"Hong Li","email":"","orcid":"","institution":"The First People’s Hospital of Changde City","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Li","suffix":""},{"id":244675963,"identity":"df929422-864a-4347-a688-8a7416298641","order_by":3,"name":"Jinli Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAs0lEQVRIiWNgGAWjYBACPgaGBBAtx8befoA4LWxQLcZ8PGcSiNYCBonzJBwMiNQikfD4w8cdteltEkDrflRsI0pLguHMM8dz26QbDzD2nLlNnJZk3rZjuW0yBxKYGduI1HIYqCUdyDAgWktiM29bTQIJWngeJDPObDtg2AYM5INE+YWfPSf5w8e2Onn59vaDD35UEKGFgYEnAUgcBjMPEKMeCNhBCuuIVDwKRsEoGAUjEgAAIMc54RpNoIEAAAAASUVORK5CYII=","orcid":"","institution":"The First People’s Hospital of Changde City","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jinli","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2023-10-31 12:29:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3524524/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3524524/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12893-024-02364-9","type":"published","date":"2024-03-05T15:01:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":45766738,"identity":"317220b6-ccf3-48f3-ad62-ad1841a4653c","added_by":"auto","created_at":"2023-11-02 19:58:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":442870,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of patient selection.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3524524/v1/ecf9b289cd9b0b46f7729e18.png"},{"id":45766740,"identity":"60b76d43-23d6-4b0d-8e19-36136e3e5645","added_by":"auto","created_at":"2023-11-02 19:58:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154010,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram predicting the risk of perineural invasion (PNI) in patients with colorectal cancer.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3524524/v1/7d23093d50001e44def477b6.png"},{"id":45767527,"identity":"eea9bf28-49b0-4a89-b51e-102bc4a04fa1","added_by":"auto","created_at":"2023-11-02 20:06:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":138452,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve. (A). Training group; (B). Validation group. ROC = Receiver Operating Characteristic; AUC = Area Under the ROC Curve.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3524524/v1/24e7f11b854c7acbc92ba029.png"},{"id":45766736,"identity":"2abb4fa3-3542-4c0f-b050-a4e0363d4c4d","added_by":"auto","created_at":"2023-11-02 19:58:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":365042,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve for predicting the possibility of PNI in colorectal cancer patients. (A). Training group; (B). Validation group. PNI = Perineural Invasion.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3524524/v1/c547201bdac857f847dbcd17.png"},{"id":45766739,"identity":"bc911c68-766b-49b2-9d97-e5a8aa9ca4fd","added_by":"auto","created_at":"2023-11-02 19:58:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":126288,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis for predicting PNI in colorectal cancer patients. (A). Training group; (B). Validation group. PNI = Perineural Invasion.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3524524/v1/c82db4ba91632c3340267b4d.png"},{"id":52432175,"identity":"24e26026-e83c-4d08-8e60-f90f4b268385","added_by":"auto","created_at":"2024-03-11 15:11:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":901998,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3524524/v1/1d75a0f1-718e-49ad-973b-accb28aa1016.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A prediction nomogram for neural perineural invasion in colorectal cancer patients: A retrospective study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) stands as a prominent driver of cancer-related occurrences and fatalities globally, holding the distinction of being the second most prevalent malignancy in adults and a substantial contributor to cancer-related mortality\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. According to projections from the American Cancer Society, the year 2023 is expected to witness an estimated 153,020 new CRC diagnoses alongside 52,550 unfortunate fatalities attributed to the disease\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Despite the pivotal roles played by tumor histological grading and clinical staging in shaping patient prognoses, recent investigations have spotlighted an interconnectedness between perineural invasion (PNI) and unfavorable clinical outcomes among individuals affected by CRC\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe term \"perineural invasion\" (PNI) refers to the infiltration of tumor cells into various layers of nerve walls, nerve bundle sheaths, or the encirclement of nerves by tumor cells exceeding 33%\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Within the colorectal cancer (CRC) patient population, the prevalence of perineural invasion (PNI) ranges between 20% and 57%\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Several pertinent studies have established a significant correlation between PNI and reduced patient survival rates, augmented tumor recurrence, and heightened metastasis rates\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. PNI has emerged as a predictive factor for CRC progression or recurrence, potentially offering valuable insights for clinical practitioners when devising patient-specific treatment strategies\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, in accordance with the American Joint Committee on Cancer (AJCC) staging manual, perineural invasion (PNI) stands out as a specific and significant prognostic indicator for colorectal cancer\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. The clinical practice guidelines outlined by the National Comprehensive Cancer Network (NCCN) similarly recognize PNI as a high-risk factor for postoperative recurrence in colorectal cancer and advocate for adjuvant therapy in cases of stage II colorectal cancer combined with PNI\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Prior investigations have established a connection between PNI status and the secretion of extracellular vesicles (EVs), as well as the expression levels of plasma miR-21 and nerve growth factor (NGF) \u003csup\u003e[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, though their clinical utility as biomarkers remains somewhat limited.\u003c/p\u003e \u003cp\u003eWhile perineural invasion (PNI) holds significant prognostic value, its assessment is currently reliant on postoperative pathological biopsies. The preoperative identification of PNI could serve as a valuable tool for clinicians, aiding in the optimization of clinical decisions such as the necessity of neoadjuvant therapy and the adequacy of surgical resection. As such, the primary objective of this study is to construct and validate a nomogram grounded in the preoperative clinical and pathological attributes of colorectal cancer (CRC) patients. This nomogram is designed to predict the likelihood of PNI occurrence.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient Selection\u003c/h2\u003e \u003cp\u003e This retrospective study protocol received approval from the Ethics Committee of the First People's Hospital of Changde, affiliated with Xiangya Medical College, Central South University. All experiments and methodologies were executed in strict accordance with applicable guidelines and regulations. The analysis encompassed a cohort of 388 patients diagnosed with colorectal cancer (CRC) who underwent surgical procedures at the Department of General Surgery of the First People's Hospital of Changde, affiliated with Xiangya Medical College, Central South University, spanning from June 2021 to June 2023.\u003c/p\u003e \u003cp\u003eInclusion Criteria:1. Patients with a verified diagnosis of colorectal cancer (CRC) and tumor staging conducted in accordance with the guidelines outlined in the American Joint Committee on Cancer (AJCC-TNM Staging Manual, 8th edition).2. Availability of postoperative histopathological reports providing details regarding the perineural invasion (PNI) status.3. Completion of both CT plain scan and enhanced examination conducted within 7 days preceding the surgical intervention. Exclusion Criteria:1. Patients presenting with an inflammatory response or infection.2. Patients exhibiting signs of liver or kidney dysfunction.3. Patients who underwent preoperative treatments, including radiotherapy, chemotherapy, or chemoradiotherapy.4. Patients diagnosed with metastatic cancer or malignancies in other anatomical sites.5. Patients with incomplete medical records or insufficient examination data.\u003c/p\u003e \u003cp\u003eFollowing adherence to the defined inclusion and exclusion criteria, a total of 335 patients were ultimately included in this study. These patients possessed comprehensive data and were subsequently allocated, in a 2:1 ratio, to two distinct datasets: the training group, comprising 221 cases, and the validation group, comprising 114 cases. This allocation was executed through the utilization of computer-generated random numbers, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe collected dataset encompassed a range of variables, including age, gender, body mass index (BMI), smoking history, serum tumor markers, complete blood count results, T-stage and N-stage determined via computerized tomography (CT) scans, preoperative histological type, histological differentiation (grading), and tumor macroscopic type. The staging of T-stage and N-stage adhered to the guidelines outlined in the 8th edition of the American Joint Committee on Cancer (AJCC) staging manual.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDefinitions\u003c/h2\u003e \u003cp\u003eBased on the complete blood count results, we computed the following ratios: neutrophils to lymphocytes (NLR), platelets to lymphocytes (PLR), and lymphocytes to monocytes (LMR). Utilizing ROC curve analysis and the determination of optimal cutoff values, we categorized the patients into two distinct groups for each ratio: NLR\u0026thinsp;\u0026le;\u0026thinsp;2.52 and NLR\u0026thinsp;\u0026gt;\u0026thinsp;2.52 groups, PLR\u0026thinsp;\u0026le;\u0026thinsp;136.98 and PLR\u0026thinsp;\u0026gt;\u0026thinsp;136.98 groups, LMR\u0026thinsp;\u0026le;\u0026thinsp;2.76 and LMR\u0026thinsp;\u0026gt;\u0026thinsp;2.76 groups.Furthermore, employing the reference values of tumor markers, we stratified the patients into groups based on each marker: CEA\u0026thinsp;\u0026le;\u0026thinsp;5 (ng/ml) and CEA\u0026thinsp;\u0026gt;\u0026thinsp;5 (ng/ml) groups, CA199\u0026thinsp;\u0026le;\u0026thinsp;35 (KU/L) and CA199\u0026thinsp;\u0026gt;\u0026thinsp;35 (KU/L) groups, CA125\u0026thinsp;\u0026le;\u0026thinsp;35 (KU/L) and CA125\u0026thinsp;\u0026gt;\u0026thinsp;35 (KU/L) groups, TAP\u0026thinsp;\u0026le;\u0026thinsp;121 (um\u0026sup2;) and TAP\u0026thinsp;\u0026gt;\u0026thinsp;121 (um\u0026sup2;) groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eNomogram construction and performance assessment\u003c/h2\u003e \u003cp\u003eWe conducted a thorough assessment of independent predictive factors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) within the training group through multivariate logistic regression analysis. This data was then utilized to formulate a nomogram designed for predicting the occurrence of perineural invasion (PNI) among colorectal cancer (CRC) patients. To gauge the goodness of fit between observed and predicted values in both the training and validation groups, we employed calibration curves and conducted the Hosmer-Lemeshow test.For an in-depth evaluation of the nomogram's performance in both groups, we utilized Receiver Operating Characteristic (ROC) curves and calculated the Area Under the Curve (AUC). Additionally, we carried out Decision Curve Analysis (DCA) to assess the clinical utility of the nomogram, evaluating the net benefit at various threshold probabilities within both the training and validation datasets. To enhance the robustness of our analysis, we employed the bootstrap method with 1000 iterations for sample resampling, focusing on discrimination and calibration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted utilizing R software (version 4.3.0). Logistic regression analysis, nomogram construction and calibration were executed with the rms package. Calibration curves were generated using the calibrate and val.prob functions from the rms package, while decision curve analysis (DCA) was facilitated with the rmda package.Numerical variables were reported as the median and interquartile range (IQR), and group comparisons were conducted employing the Mann-Whitney U test. Categorical variables were expressed as counts (percentages), and group comparisons were performed using \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\chi 2\\)\u003c/span\u003e\u003c/span\u003e tests or Fisher's exact tests. All statistical tests were two-tailed, with a significance level set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eClinicopathological characteristics of the patients with colorectal cancer (CRC)\u003c/h2\u003e \u003cp\u003eThis study encompassed a total of 335 patients, and an overview of the patients' demographic characteristics is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The training dataset consisted of 221 cases, with 110 males and 111 females, while the validation dataset comprised 114 cases, consisting of 54 males and 60 females. Within this cohort, there were 146 cases of rectal cancer, 105 cases of left-sided colon cancer, and 84 cases of right-sided colon cancer. As per the postoperative pathological reports, 148 patients were diagnosed with perineural invasion (PNI).\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\u003eClinicopathologic characteristics of the patients with colorectal cancer (CRC)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTraining set (n\u0026thinsp;=\u0026thinsp;221)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eValidation set (n\u0026thinsp;=\u0026thinsp;114)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePNI-negative n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePNI-positive n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePNI-negative n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePNI-positive n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (61)\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\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36 (53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18 (39)\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 (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR) (year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (53, 69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.5 (54, 68.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (55.25, 73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.5 (56.25, 68.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (Kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (IQR)(Kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.63 (20.24, 23.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.52 (19.82, 23.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.6 (20.07, 23.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.05 (20.19, 22.56)\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\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (61)\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\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18 (39)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23 (50)\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\u003e༞5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23 (50)\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\u003eCA199(KU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108 (91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63 (93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40 (87)\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\u003e༞35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (13)\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\u003eCA125(KU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113 (95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43 (93)\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\u003e༞35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (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\u003eTAP(um\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (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\u003e༞121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 (94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44 (96)\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\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (22)\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\u003e༞2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36 (78)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;136.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16 (35)\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\u003e༞136.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 (65)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (15)\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\u003e༞2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44 (65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39 (85)\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\u003eCT N-stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (30)\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\u003eN1-N2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32 (70)\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\u003eCT T-stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1-T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3-T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (63)\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\u003ePrimary site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft colon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight colon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (26)\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\u003eRectum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21 (31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27 (59)\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\u003eEndoscopic biopsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 (76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83 (81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51 (75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39 (85)\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\u003eWell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (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\u003ePathological type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eadenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 (94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44 (96)\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\u003enon-adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (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\u003eTumor gross type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUlceration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22 (32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25 (54)\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\u003eInfiltrative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (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\u003eProtruded\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40 (59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17 (37)\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\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNotes\u003c/b\u003e:BMI: Body Mass Index; Primary site: Primary tumor site; Smoking: Smoking history; CT T-stage: Tumor stage determined by CT scan; CT N-stage: Lymph node stage determined by CT scan; Endoscopic biopsy: Histological differentiation (grading); Pathological type: Histological type; Tumor gross type: Gross classification of the tumor; CEA: Carcinoembryonic Antigen; CA199: Carbohydrate Antigen 199; CA125: Carbohydrate Antigen 125; TAP: Aberrant Glycosylation Protein Detection; NLR: Neutrophil-to-Lymphocyte Ratio; PLR: Platelet-to-Lymphocyte Ratio; LMR: Lymphocyte-to-Monocyte Ratio; IQR: Interquartile Range.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo statistically significant differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) were observed between the PNI-positive and PNI-negative groups concerning age, gender, smoking history, body mass index (BMI), abnormal glycoprotein TAP, carbohydrate antigen CA125 (CA125), lymphocyte-to-monocyte ratio (LMR), and histological classification. However, noteworthy distinctions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) emerged between these two groups in terms of T-stage and N-stage in CT, tumor histological differentiation (grading), tumor macroscopic type, tumor primary location, carcinoembryonic antigen (CEA), carbohydrate antigen CA199 (CA199), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR).Multivariate logistic regression analysis identified six clinical and pathological characteristics as independent risk factors for perineural invasion (PNI) in patients with colorectal cancer (CRC). These findings are summarized as follows: tumor histological differentiation (grading) (OR\u0026thinsp;=\u0026thinsp;0.15, 95% CI\u0026thinsp;=\u0026thinsp;0.03\u0026ndash;0.74, p\u0026thinsp;=\u0026thinsp;0.02), primary tumor location (OR\u0026thinsp;=\u0026thinsp;2.49, 95% CI\u0026thinsp;=\u0026thinsp;1.21\u0026ndash;5.12, p\u0026thinsp;=\u0026thinsp;0.013), tumor macroscopic type (OR\u0026thinsp;=\u0026thinsp;0.42, 95% CI\u0026thinsp;=\u0026thinsp;0.22\u0026ndash;0.81, p\u0026thinsp;=\u0026thinsp;0.01), N staging in CT (OR\u0026thinsp;=\u0026thinsp;3.44, 95% CI\u0026thinsp;=\u0026thinsp;1.74\u0026ndash;6.80, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), CEA (OR\u0026thinsp;=\u0026thinsp;3.13, 95% CI\u0026thinsp;=\u0026thinsp;1.60\u0026ndash;6.13, p\u0026thinsp;=\u0026thinsp;0.001), and PLR (OR\u0026thinsp;=\u0026thinsp;2.07, 95% CI\u0026thinsp;=\u0026thinsp;1.08\u0026ndash;3.96, p\u0026thinsp;=\u0026thinsp;0.028) (as depicted in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and Multivariate logistic regression analysis of the patients with colorectal cancer (CRC)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.00(0.97\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (Kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.93(0.83\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender Female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.95(0.56\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking NO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.48(0.82\u0026ndash;2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA125(KU/L) \u0026le;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e༞35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.19(0.02\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA199(KU/L) \u0026le;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e༞35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.39(1.09\u0026ndash;5.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEA(ng/ml) \u0026le;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e༞5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.25(1.29\u0026ndash;3.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.13(1.60\u0026ndash;6.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTAP(um\u003csup\u003e2\u003c/sup\u003e) \u0026le;121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e༞121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.84(0.34\u0026ndash;2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMR \u0026le;2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e༞2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.35(0.76\u0026ndash;2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR \u0026le;136.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e༞136.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.12(1.20\u0026ndash;3.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.07(1.08\u0026ndash;3.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR \u0026le;2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e༞2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.01(1.12\u0026ndash;3.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT N-stage N0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1-N2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.55(1.46\u0026ndash;4.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.44(1.74\u0026ndash;6.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT T-stage T1-T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3-T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.81(1.01\u0026ndash;3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndoscopic biopsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoorly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerately\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.57(0.24\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69(0.26\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.10(0.02\u0026ndash;0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15(0.03\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathological type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eadenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon-adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.57(0.17\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft colon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight colon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.98(0.46\u0026ndash;2.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79(0.34\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRectum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2.79(1.49\u0026ndash;5.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.49(1.21\u0026ndash;5.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor gross type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUlceration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfiltrative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.81(0.19\u0026ndash;3.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65(0.14\u0026ndash;2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtruded\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.47(0.27\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42(0.22\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.63(0.28\u0026ndash;9.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.02(0.16\u0026ndash;6.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eNomogram construction and performance\u003c/h2\u003e \u003cp\u003eA nomogram (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was developed employing the six clinical and pathological characteristics derived from the multivariate logistic regression analysis. In this nomogram, a higher total score denotes an increased risk of perineural invasion (PNI) for each colorectal cancer (CRC) patient. To illustrate, consider a specific CRC patient with the following attribute scores: primary tumor location in the rectum (60 points), N stage in CT as N0 (0 points), ulcerative-type tumor macroscopic classification (22 points), poorly differentiated tumor histological differentiation (100 points), positive carcinoembryonic antigen (CEA) (60 points), and platelet-to-lymphocyte ratio (PLR)\u0026thinsp;\u0026gt;\u0026thinsp;136.98 (39 points). These values sum up to a total score of 281 points, corresponding to an estimated PNI occurrence probability of approximately 70%. Based on the 50% threshold, this patient falls into the high-risk category and may be considered for neoadjuvant chemotherapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe receiver operating characteristic (ROC) curve analysis revealed an area under the curve (AUC) of 0.772 (95% CI, 0.712\u0026ndash;0.833) for the predicted nomogram within the training group dataset and 0.752 (95% CI, 0.664\u0026ndash;0.839) within the validation group dataset (as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Moreover, the calibration curves exhibited a favorable agreement between the predicted probabilities and the actual outcomes for both the training and validation group datasets (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The p-values obtained from the Hosmer-Lemeshow test were 0.47 and 0.72, respectively, indicating no significant deviation and confirming the model's robust fit.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDecision curve analysis (DCA) was conducted to assess the practical clinical utility of the nomogram in both the training and validation group datasets (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The analysis revealed that within the training group, setting threshold probabilities between 13% and 85%, and in the validation group, between 8% and 68%, could lead to net benefits when guiding clinical interventions for patients with perineural invasion (PNI) based on the nomogram predictions.These findings underscore the nomogram's practical applicability in clinical settings for estimating the risk of perineural invasion (PNI) among colorectal cancer (CRC) patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eClinical utility of the nomogram\u003c/h2\u003e \u003cp\u003eDecision curve analysis (DCA) was systematically performed to evaluate the clinical applicability of the nomogram in both the training and validation group datasets (as displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The analysis clearly suggests that when the threshold probabilities for the training group and validation group were strategically set within the ranges of 13\u0026ndash;85% and 8\u0026ndash;68%, respectively, clinical interventions guided by the nomogram's predictions had the potential to yield net benefits for patients dealing with perineural invasion (PNI).This unequivocally underscores the practical significance of the nomogram in real-world clinical contexts, particularly in forecasting the likelihood of PNI among colorectal cancer (CRC) patients.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGiven the substantial impact of perineural invasion (PNI) on tumor prognosis, both the NCCN guidelines and the Chinese Society of Clinical Oncology (CSCO) guidelines have underscored the imperative inclusion of PNI status in the pathological reports of individuals afflicted with colorectal cancer (CRC) \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. The emergence of PNI not only introduces discomfort and functional impairment to the affected organs but also escalates the susceptibility to postoperative local recurrence, metastasis, and infiltration, driven by the presence of residual tumor cells in proximity to or enveloping the nerves. This particular factor constitutes a pivotal contributor to unfavorable outcomes\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Nomograms, adept at amalgamating a plethora of pivotal factors, offer personalized risk assessment and have gained extensive traction for prognostic evaluations among colorectal cancer patients\u003csup\u003e[\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. In the current study, we harnessed the potency of multivariate logistic regression analysis to pinpoint independent predictive factors intricately linked to PNI among CRC patients. Subsequently, these critical factors were meticulously employed in the formulation of the nomogram.\u003c/p\u003e \u003cp\u003eIn our investigation, we have discerned that the platelet-to-lymphocyte ratio (PLR) (p\u0026thinsp;=\u0026thinsp;0.028) serves as an independent predictive factor for perineural invasion (PNI). The presence of malignant tumors can incite an escalation in platelet (PLT) counts through the release of thrombopoietin (TPO) and interleukin-6 (IL-6). Consequently, heightened platelet activation can foster tumor growth and metastasis, a phenomenon recognized as the \"positive feedback loop\" in paraneoplastic thrombocytosis\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. When peripheral blood lymphocyte counts diminish, the tumor burden tends to rise, thus promoting tumor dissemination and metastasis\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. PLR, as a composite biomarker reflecting both inflammatory and immune status within the body, indicates an augmentation in the inflammatory response or a reduction in immune response. This suggests a decline in the body's anti-tumor capacity, which, in turn, results in an unfavorable prognosis and distant metastasis of tumors\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Although an extensive body of research has illuminated the significance of increased local lymphocyte infiltration within tumors and elevated systemic inflammatory responses as pivotal clinical indicators influencing patient prognosis\u003csup\u003e[\u003cspan additionalcitationids=\"CR29 CR30 CR31\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, studies elucidating the impact of PLR on PNI among CRC patients remain scarce. Consequently, further investigations are warranted to delve into the intricate relationship between these factors.\u003c/p\u003e \u003cp\u003eSerum tumor markers primarily encompass a group of substances secreted either directly by tumor cells or in response to corresponding stimuli within the body. They play a multifaceted role, aiding not only in the diagnosis of colorectal cancer (CRC) but also in guiding treatment decisions, assessing treatment efficacy, and predicting prognosis. Research has elucidated that elevated preoperative carcinoembryonic antigen (CEA) stands as an independent predictive factor for perineural invasion (PNI) in CRC patients\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Our study's findings corroborate this perspective. Nonetheless, the diagnostic efficacy of CEA on its own remains relatively limited, and it is recommended to be employed in conjunction with other indicators such as PLR.Tumor tissue differentiation (grading) and lymph node stage (N stage) as ascertained through CT scans are pivotal prognostic factors for colorectal cancer (CRC) patients. Within our nomogram, a deterioration in tumor tissue differentiation and an increase in N stage correspond to higher total scores, signifying an elevated likelihood of PNI development in CRC patients. This observation aligns with findings in relevant studies\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Among the common macroscopic classifications of CRC, including ulcerative, infiltrative, and protruding types, previous research has identified a correlation between macroscopic classification and PNI\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Our study outcomes reinforce this by indicating that the ulcerative type is more predisposed to PNI compared to the protruding type, likely attributed to the tumor's disruption of normal tissues.\u003c/p\u003e \u003cp\u003eOur research findings indicate that the incidence of perineural invasion (PNI) is higher in rectal cancer compared to colon cancer, aligning with previously reported results. Specifically, the occurrence rate of PNI in rectal cancer stands at 20.6%, whereas in colon cancer, it is 14.1%.\u003csup\u003e3\u003c/sup\u003e Presently, there is a dearth of research elucidating the impact of left-sided colon cancer versus right-sided colon cancer on PNI. Although our study hints at left-sided colon cancer being more susceptible to perineural invasion (PNI) than its right-sided counterpart, it's essential to acknowledge that our research data are derived from a single-center source and entail a relatively modest sample size, potentially introducing bias. Consequently, further investigations through multi-center studies with larger sample sizes are imperative to gain deeper insights into the influence of the tumor's primary location on perineural invasion (PNI).\u003c/p\u003e \u003cp\u003eA previous study employed CT radiomics features to construct a nomogram for predicting perineural invasion (PNI) in colorectal cancer (CRC) patients\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. However, the influence of subjectivity on semantic features within regions of interest (ROI) in CT images, such as tumor diameter or size measurements, may lead to substantial disparities in results when assessed by clinical practitioners with varying levels of experience. This limitation hinders its broad applicability.In our research, we amalgamated a selection of frequently employed clinical and pathological features, encompassing serum inflammatory markers, pathological indicators, etc., all grounded in CT images. The outcomes underscored the intimate association between tumor differentiation (grading), primary tumor location, tumor gross classification, N stage in CT, carcinoembryonic antigen (CEA), and platelet-to-lymphocyte ratio (PLR) with the occurrence of perineural invasion (PNI). Notably, these clinical and pathological features not only facilitate a more comprehensive evaluation of CRC but are also readily obtainable.Leveraging these six clinical and pathological features, we meticulously constructed the nomogram, and internal validation corroborated its robustness and precision. Furthermore, the decision curve analysis (DCA) results accentuated the clinical utility of our model in guiding medical practitioners' clinical decisions.\u003c/p\u003e \u003cp\u003eWhile this study boasts the aforementioned advantages, it is important to acknowledge the following limitations. Firstly, this study adopts a retrospective approach and exclusively enrolls patients who underwent curative surgery, potentially introducing selection bias. Thus, further prospective investigations are warranted to corroborate these findings. Secondly, PNI status assessment in this study relied on HE staining rather than the more precise immunohistochemical technique involving anti-s100 antibody, which is favored by pathologists for accurate diagnosis of PNI-positive CRC cases\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Consequently, our forthcoming research endeavors will integrate this advantageous technique.Thirdly, the model employed in this study encompasses a limited number of risk factors. Therefore, in future validation studies, the incorporation of additional risk factors is imperative to further bolster the model's predictive capability. Lastly, all study participants were derived from a single hospital, and the validation was conducted internally. Hence, it is imperative to validate this nomogram utilizing data from other healthcare institutions to enhance its credibility and robustness.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our study furnishes a nomogram that enables clinicians to anticipate the likelihood of perineural invasion (PNI) in colorectal cancer (CRC) patients prior to surgery. This tool can assist medical practitioners in foreseeing CRC metastasis and recurrence, thereby potentially enhancing tumor staging strategies and treatment modalities.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003eInstitutional Review Board Statement\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Medical Ethics Committee of the First People\u0026rsquo;s Hospital in Changde, Hunan Province, China (Protocol Code 2023-197-01). Before collecting any data, each participant provided written, informed consent. Every step was carried out in conformity with the Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe are grateful to all the participants in this study. We acknowledge the help of the First People\u0026rsquo;s Hospital of Changde City for this study.\u003c/p\u003e\n\u003cp\u003eDisclosure\u003c/p\u003e\n\u003cp\u003eThe authors report no conflicts of interest in this work.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eStudy concept and design: JL-L, YQ. Drafting of the manuscript: YQ, RP-W. Acquisition of data, analysis, and interpretation of data: YQ,HL. Critical revision of the manuscript: JL-L. Statistical analysis: YQ. Study supervision: JL-L. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eThe work dataset supports the findings of this study are available on reasonable request from the corresponding author, and the data are not publicly available due to privacy or ethical restrictions.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Wagle NS, et al. Cancer statistics. Cancer J Clin. 2023;73:17\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiegel RL, Wagle NS, Cercek A, et al. Colorectal cancer statistics. Cancer J Clin. 2023;73:233\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnijn N, Mogk SC, Teerenstra S, et al. 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Med Sci Monit. 2019;25:1709\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Y, Wang H, Gong H, et al. Clinical significance of perineural invasion in stages II and III colorectal cancer. Pathol - Res Pract. 2015;211:839\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Y, He L, Dong D, et al. Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model. Chin J Cancer Res. 2018;30:40\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimada Y, Kido T, Kameyama H, et al. Retraction Note to: Clinical significance of perineural invasion diagnosed by immunohistochemistry with anti-S100 antibody in Stage I-III colorectal cancer. Surg Today. 2022;52:519\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Wyk HC, Going J, Horgan P, et al. The role of perineural invasion in predicting survival in patients with primary operable colorectal cancer: A systematic review. Crit Rev Oncol/Hematol. 2017;112:11\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bsur","sideBox":"Learn more about [BMC Surgery](http://bmcsurg.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bsur/default.aspx","title":"BMC Surgery","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Colorectal cancer, Perineural invasion, Nomogram, Surgery, Systemic inflammatory markers","lastPublishedDoi":"10.21203/rs.3.rs-3524524/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3524524/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePerineural invasion (PNI), as the fifth recognized pathway for the spread and metastasis of colorectal cancer (CRC), has increasingly garnered widespread attention. The preoperative identification of whether colorectal cancer (CRC) patients exhibit PNI can assist clinical practitioners in enhancing preoperative decision-making, including determining the necessity of neoadjuvant therapy and the appropriateness of surgical resection. The primary objective of this study is to construct and validate a preoperative predictive model for assessing the risk of perineural invasion (PNI) in patients diagnosed with colorectal cancer (CRC).\u003c/p\u003e\u003ch2\u003eMaterials and methods\u003c/h2\u003e \u003cp\u003eA total of 335 patients diagnosed with colorectal cancer (CRC) at a single medical center were subject to random allocation, with 221 individuals assigned to a training dataset and 114 to a validation dataset, maintaining a ratio of 2:1. Comprehensive preoperative clinical and pathological data were meticulously gathered for analysis. Initial exploration involved conducting univariate logistic regression analysis, with subsequent inclusion of variables demonstrating a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 into the multivariate logistic regression analysis, aiming to ascertain independent predictive factors, all while maintaining a p-value threshold of less than 0.05. From the culmination of these factors, a nomogram was meticulously devised. Rigorous evaluation of this nomogram's precision and reliability encompassed Receiver Operating Characteristic (ROC) curve analysis, calibration curve assessment, and Decision Curve Analysis (DCA). The robustness and accuracy were further fortified through application of the bootstrap method, which entailed 1000 independent dataset samplings to perform discrimination and calibration procedures.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results of multivariate logistic regression analysis unveiled independent risk factors for perineural invasion (PNI) in patients diagnosed with colorectal cancer (CRC). These factors included tumor histological differentiation (grade) (OR\u0026thinsp;=\u0026thinsp;0.15, 95% CI\u0026thinsp;=\u0026thinsp;0.03\u0026ndash;0.74, p\u0026thinsp;=\u0026thinsp;0.02), primary tumor location (OR\u0026thinsp;=\u0026thinsp;2.49, 95% CI\u0026thinsp;=\u0026thinsp;1.21\u0026ndash;5.12, p\u0026thinsp;=\u0026thinsp;0.013), gross tumor type (OR\u0026thinsp;=\u0026thinsp;0.42, 95% CI\u0026thinsp;=\u0026thinsp;0.22\u0026ndash;0.81, p\u0026thinsp;=\u0026thinsp;0.01), N staging in CT (OR\u0026thinsp;=\u0026thinsp;3.44, 95% CI\u0026thinsp;=\u0026thinsp;1.74\u0026ndash;6.80, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), carcinoembryonic antigen (CEA) level (OR\u0026thinsp;=\u0026thinsp;3.13, 95% CI\u0026thinsp;=\u0026thinsp;1.60\u0026ndash;6.13, p\u0026thinsp;=\u0026thinsp;0.001), and platelet-to-lymphocyte ratio (PLR) (OR\u0026thinsp;=\u0026thinsp;2.07, 95% CI\u0026thinsp;=\u0026thinsp;1.08\u0026ndash;3.96, p\u0026thinsp;=\u0026thinsp;0.028).These findings formed the basis for constructing a predictive nomogram, which exhibited an impressive area under the receiver operating characteristic (ROC) curve (AUC) of 0.772 (95% CI, 0.712\u0026ndash;0.833). The Hosmer-Lemeshow test confirmed the model's excellent fit (p\u0026thinsp;=\u0026thinsp;0.47), and the calibration curve demonstrated consistent performance. Furthermore, decision curve analysis (DCA) underscored a substantial net benefit across the risk range of 13\u0026ndash;85%, reaffirming the nomogram's reliability through rigorous internal validation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe have formulated a highly reliable nomogram that provides valuable assistance to clinical practitioners in preoperatively assessing the likelihood of perineural invasion (PNI) among colorectal cancer (CRC) patients. This tool holds significant potential in offering guidance for treatment strategy formulation.\u003c/p\u003e","manuscriptTitle":"A prediction nomogram for neural perineural invasion in colorectal cancer patients: A retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-02 19:58:10","doi":"10.21203/rs.3.rs-3524524/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-01-17T05:58:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-01-08T13:41:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"b45c731e-33dc-4563-a8bd-0f53def5bb1f","date":"2024-01-04T21:48:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"f311a5bb-3d24-4adc-938d-7209ff4e42f8","date":"2024-01-04T17:20:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3126b3d2-a3db-46c0-94d7-280930890a59","date":"2024-01-04T09:11:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6e40b9ab-75b9-470d-aadb-74bb328c607b_SNPRID","date":"2024-01-01T00:09:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-12-31T21:22:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-31T21:20:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-11-01T04:33:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-11-01T04:29:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Surgery","date":"2023-10-31T12:17:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bsur","sideBox":"Learn more about [BMC Surgery](http://bmcsurg.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bsur/default.aspx","title":"BMC Surgery","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e4f0fa7c-0402-4273-ac30-a05cbee67b62","owner":[],"postedDate":"November 2nd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-03-11T15:09:02+00:00","versionOfRecord":{"articleIdentity":"rs-3524524","link":"https://doi.org/10.1186/s12893-024-02364-9","journal":{"identity":"bmc-surgery","isVorOnly":false,"title":"BMC Surgery"},"publishedOn":"2024-03-05 15:01:08","publishedOnDateReadable":"March 5th, 2024"},"versionCreatedAt":"2023-11-02 19:58:10","video":"","vorDoi":"10.1186/s12893-024-02364-9","vorDoiUrl":"https://doi.org/10.1186/s12893-024-02364-9","workflowStages":[]},"version":"v1","identity":"rs-3524524","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3524524","identity":"rs-3524524","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}