The Impact of Nano-Carbon Suspension Lymph Node Tracing on Rectal Cancer Surgery Post- Neoadjuvant Therapy

The Impact of Nano-Carbon Suspension Lymph Node Tracing on Rectal Cancer Surgery Post- Neoadjuvant Therapy | 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 The Impact of Nano-Carbon Suspension Lymph Node Tracing on Rectal Cancer Surgery Post- Neoadjuvant Therapy Shuai Shen, Hui Gao, Zhongzheng Cao, Wenlong Xia, Chengren Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6066404/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Aug, 2025 Read the published version in International Journal of Colorectal Disease → Version 1 posted 12 You are reading this latest preprint version Abstract Background To explore the impact of using nano-carbon suspension for lymph node tracing on the number of detected lymph nodes and short-term clinical outcomes in patients undergoing laparoscopic radical resection for rectal cancer following neoadjuvant therapy. Methods This study retrospectively analyzed clinical data from 109 patients who underwent neoadjuvant therapy and laparoscopic radical resection for rectal cancer at Weifang People's Hospital from January 2020 to December 2022. Of these, 43 patients received an endoscopic submucosal injection of nano-carbon suspension (experimental group), with 22 patients receiving the injection before neoadjuvant therapy and 21 patients receiving it 24 hours before surgery. The remaining 66 patients did not receive the nano-carbon injection (control group). All patients received neoadjuvant therapy according to guidelines and were operated on by the same surgical team. By comparing the number of detected lymph nodes and short-term clinical outcomes among the three groups, the study aimed to investigate the impact of the endoscopic submucosal injection of nano-carbon and the timing of injection on the surgical quality for patients with rectal cancer undergoing neoadjuvant therapy. Results The number of detected lymph nodes in the groups injected with nano-carbon before neoadjuvant therapy and 24 hours before surgery was significantly higher than that in the non-injected group, with a significant increase in the proportion of detecting ≥ 12 lymph nodes, showing statistical significance (P < 0.05). There is no statistically significant difference in the number of detected lymph nodes between the group injected with nano-carbon before neoadjuvant therapy and the group injected 24 hours before surgery (P > 0.05). Conclusions Endoscopic submucosal injection of nano-carbon suspension for lymph node tracing can increase the number of detected lymph nodes in rectal cancer surgery following neoadjuvant therapy, enabling more precise postoperative tumor staging. Although injecting nano-carbon before neoadjuvant therapy may have potential advantages in lymph node detection, the optimal timing of injection requires further investigation. Rectal cancer neoadjuvant therapy nano-carbon lymph node dissection Figures Figure 1 Figure 2 Figure 3 Background Colorectal cancer is one of the most prevalent malignancies globally, ranking third in incidence and second in mortality among all cancers [ 1 ]. Due to the rectum's unique anatomical position and the absence of obvious clinical symptoms, most patients are diagnosed at an advanced stage. The standard treatment for locally advanced rectal cancer is neoadjuvant chemoradiotherapy followed by total mesorectal excision [ 2 ]. Studies have demonstrated that neoadjuvant therapy, compared to adjuvant therapy after surgery, can enhance the rate of intraoperative tumor resection and the preservation of the anal sphincter, while also improving local tumor control and reducing adverse effects [ 3 – 5 ]. Lymph node dissection is a crucial component of radical surgery for rectal cancer. Standardized lymph node dissection and obtaining an adequate number of lymph nodes are vital for effective tumor eradication. Insufficient lymph node retrieval not only leads to inaccurate staging but also reduces postoperative survival [ 6 ]. After neoadjuvant therapy, regional lymph nodes undergo regressive changes, increasing the difficulty of detection [ 7 ]. Radiotherapy can cause lymphocyte depletion within the lymph nodes, along with interstitial proliferation and fibrosis, resulting in lymph node shrinkage. This makes distinguishing lymph nodes morphologically challenging, thereby complicating their detection through visual inspection and sampling [ 8 ]. Several international studies have shown that under the same surgical and pathological examination standards, neoadjuvant chemoradiotherapy significantly reduces the number of lymph nodes retrieved [ 9 – 11 ]. One study even found that up to 72% of patients had fewer than 12 lymph nodes retrieved in their surgical specimens, and 23% had fewer than 5 lymph nodes retrieved [ 8 ]. The use of effective lymph node tracers during surgery can increase the number of lymph nodes dissected and the detection of micrometastases. In laparoscopic surgery, effective lymph node tracers can compensate for the inability to palpate lymph nodes, further highlighting regional lymphatic tissue and avoiding damage to surrounding structures. However, for patients undergoing neoadjuvant therapy, chemoradiotherapy may cause fibrosis in lymphatic vessels and lymph nodes, potentially affecting the uptake of nano-carbon by lymph nodes [ 12 ]. Currently, most studies inject nano-carbon into the submucosa 24 hours before surgery, which may result in some regional lymph nodes not being stained. Therefore, our center has conducted a study on the impact of nano-carbon submucosal injection on radical surgery for rectal cancer post-neoadjuvant therapy. This study aims to explore the effects of nano-carbon submucosal injection and its timing on the number of lymph nodes retrieved and postoperative complications. Patients and Methods 1. General data A retrospective analysis was conducted on the clinical and pathological data of 109 rectal cancer patients who underwent surgical treatment at the Department of Colorectal and Anal Surgery, Weifang People's Hospital, from January 2020 to December 2022. All patients underwent laparoscopic radical resection for rectal cancer following neoadjuvant therapy. Among the patients, 43 received a nano-carbon injection; 22 of these patients received an endoscopic submucosal injection of nano-carbon before neoadjuvant therapy, and 21 received a nano-carbon injection 24 hours before surgery. The remaining 66 patients did not receive a nano-carbon submucosal injection for lymph node tracing. The inclusion and exclusion criteria were as follows: Inclusion criteria: 1. Preoperative pathological diagnosis of adenocarcinoma 2. Preoperative pelvic MRI or CT assessment indicating tumor T stage as T3 or T4, or suggesting lymph node metastasis 3. No evidence of distant metastasis, such as in the lungs or liver, as determined by chest and abdominal CT; radical surgery is feasible Exclusion criteria: 1. Distant metastasis to the liver, lungs, or other organs during neoadjuvant therapy 2. Symptoms such as intestinal obstruction or bleeding during neoadjuvant therapy requiring emergency surgery 3. Patients unable to tolerate chemoradiotherapy and who did not complete the full course of neoadjuvant therapy 4. Patients with severe underlying diseases who could not tolerate surgery 5. Patients considered to have complete clinical response (cCR) after neoadjuvant therapy and requested watch-and-wait (W&W) management 6. Patients with other malignant tumors or a history of malignant tumors This study obtained approval from the Ethics Committee of Weifang people’s Hospital, and due to its retrospective nature, the need for obtaining written informed consent was waived. 2. Methods We confirmed that all methods were carried out in accordance with relevant guidelines and regulations. Patients undergoing lymph node tracing received an endoscopic submucosal injection of nano-carbon suspension under intravenous general anesthesia either before neoadjuvant therapy or 24 hours before surgery. This procedure was performed by experienced gastroenterologists. The injection sites varied depending on the location of the tumor. Typically, nano-carbon was injected submucosally at three sites on both the posterior and lateral walls of the rectum. The anterior rectal wall was not used as an injection site. The injection sites were approximately 0.5 cm away from the tumor, starting with the intestinal wall at the same plane as the tumor, followed by the proximal end of the tumor. For tumors that could not be accessed by colonoscopy, the distal end was chosen for injection. Prior to the nano-carbon injection, normal saline was injected into the submucosal layer to create a transparent submucosal bulge. Subsequently, the nano-carbon suspension was drawn and injected at each site. This is shown in Figure 1. All patients received preoperative neoadjuvant therapy according to the following regimen: a total radiotherapy dose of 45–50 Gy, administered at 1.8–2.0 Gy per fraction, five fractions per week, completed within five weeks; concurrent chemotherapy with capecitabine at a dose of 1250 mg/m2, taken orally twice daily. Preoperative assessment was performed eight weeks after the completion of neoadjuvant radiotherapy, followed by laparoscopic radical resection for rectal cancer. All surgeries adhered to the principles of TME (total mesorectal excision) and mesorectal dissection. As shown in Figure 2, lymph nodes in non-surgical areas without suspected metastases were not routinely dissected. Post-surgery, the specimens were sent to the pathology department, where pathologists retrieved the lymph nodes and processed the tumor specimens. 3. Observation indicators (1) The primary observation indicators included the number of lymph nodes retrieved and the proportion of patients from whom ≥12 lymph nodes were retrieved. (2) The secondary indicators recorded were operative time, blood loss, tumor regression grade, postoperative complication rate, postoperative length of stay, and hospitalization costs. 4. Statistical analysis Data were analyzed using SPSS 24.0 statistical software. Categorical data are presented as frequencies (n) and were compared using the χ2 test, the corrected χ2 test, or Fisher's exact test. For continuous variables, the normality is tested using the Shapiro-Wilk test. Normally distributed continuous data are presented as mean ± standard deviation (x̄ ± s) and were compared using one-way analysis of variance (ANOVA) or the Brown-Forsythe/Welch test. Non-normally distributed continuous data are presented as median (interquartile range, IQR) and were compared using the Kruskal-Wallis test (a non-parametric test for independent samples). Using the number of harvested lymph nodes as the dependent variable, univariate analysis was conducted on the relevant clinical and pathological data. Indicators with a P-value less than 0.1 were selected for multivariate analysis using a generalized linear model. A P-value < 0.05 was considered statistically significant. Results 1. Clinical and Pathological Data Based on the inclusion and exclusion criteria, a total of 109 patients were included in this study, with 66 patients in the Non-injection group, 22 patients in the Pre-neoadjuvant injection group, and 21 patients in the Preoperative injection group. The general clinical and pathological data for these three groups are presented in Table 1 . No significant differences were observed in gender, age, BMI, comorbidity, pre-treatment cT or cN stage, tumor distance from anal verge, surgical approach, procedure, or ASA classification (P > 0.05). Table 1 Clinical and pathological data Non-injection group n = 66 Pre-neoadjuvant injection group n = 22 Preoperative injection group n = 21 Statistical value P value Gende, n(%) χ ²=1.149 0.563 Male 43(65.2%) 16(72.7%) 12(57.1%) Female 23(34.8%) 6(27.3%) 9(42.9%) Age (year),M(IQR) 61.5(16) 62.0(20) 58.0(15) H = 1.097 0.578 BMI(kg/m 2 ),M(IQR) 23.10(3.20) 22.73(6.23) 23.44(4.14) H = 0.469 0.791 Comorbidity, n(%) χ ²=0.563 0.755 Yes 23(34.8%) 9(40.9%) 9(42.9%) No 43(65.2%) 13(59.1%) 12(57.1%) Pre-treatment cT stage, n(%) χ ²=4.865 0.255 T2 1(1.5%) 1(4.5%) 1(4.8%) T3 50(75.8%) 12(54.5%) 15(71.4%) T4 15(22.7%) 9(40.9%) 5(23.8%) Pre-treatment cN stage, n(%) χ ²=7.907 0.108 N0 1(1.5%) 1(4.5%) 3(14.3%) N1 25(37.9%) 4(18.2%) 7(33.3%) N2 40(60.6%) 17(77.3%) 11(52.4%) Tumor distance from anal verge (cm),M(IQR) 5.0(4.0) 4.0(4.0) 5.0(3.5) H = 1.260 0.533 Surgical approach χ ²=4.373 0.075 Laparotomy, n(%) 2(3.0%) 0(0) 3(14.3%) Laparoscopy 64(97.0%) 22(100%) 18(85.7%) Surgical procedure, n(%) χ ²=4.028 0.741 Dixon 7(10.6%) 0(0) 1(4.8%) Dixon + colostomy 44(66.7%) 16(72.7%) 14(66.7%) Harrtmann 1(1.5%) 0(0) 0(0) Miles 14(21.2%) 6(27.3%) 6(28.6%) ASA classification, n(%) χ ²=1.192 0.551 II 55(83.3%) 20(90.9%) 19(90.5%) III 11(16.7%) 2(9.1%) 2(9.5%) 2. Intraoperative Data and Short-term Clinical Outcomes The intraoperative conditions and short-term clinical outcomes for patients in each group are shown in Table 2 . No significant differences were observed in pT stage, pN stage, yPTNM stage, tumor differentiation, or tumor regression grade among the groups (P > 0.05).The number of dissected lymph nodes and the proportion of patients with ≥ 12 lymph nodes in the pre-neoadjuvant injection group and the preoperative 24-hour injection group were significantly higher than those in the non-injection group (P < 0.05). There is no statistically significant difference in the number of detected lymph nodes between the group injected with nano-carbon before neoadjuvant therapy and the group injected 24 hours before surgery (P > 0.05).However, there were no significant differences in tumor size, proximal margin, surgery duration, blood loss, postoperative hospital stay, hospital costs, or overall complication rates among the groups (P > 0.05). Specifically, intra-abdominal infection, anastomotic leakage, urinary tract infection, and hemorrhage rates did not differ significantly between the groups. Nano-carbon injection did not affect tumor-related indicators such as tumor staging and tumor regression grade after chemoradiotherapy, nor did it increase postoperative complications. Table 2 Intraoperative data and short-term clinical outcomes Non-injection group n = 66 Pre-neoadjuvant injection group n = 22 Preoperative injection group n = 21 Statistical value P value pT stage, n(%) χ ²=6.677 0.527 T0 8(12.1%) 2(9.1%) 2(9.5%) T1 3(4.5%) 0(0) 2(9.5%) T2 15(22.7%) 5(22.7%) 1(4.8%) T3 38(57.6%) 15(68.2%) 15(71.4%) T4 2(3.0%) 0(0) 1(4.8) pN stage, n(%) χ ²=0.770 0.964 N0 43(65.2%) 16(72.7%) 14(66.7%) N1 18(27.3%) 5(22.7%) 5(23.8%) N2 5(7.6%) 1(4.5%) 2(9.5%) yPTNM stage, n(%) χ ²=3.564 0.752 0 7(10.6%) 2(9.1%) 2(9.5%) I 16(24.2%) 3(13.6%) 3(14.3%) II 20(30.3%) 11(50.0%) 9(42.9%) III 23(34.8%) 6(27.3%) 7(33.3%) Differentiation, n(%) χ ²=0.801 0.953 Low 12(18.2%) 3(13.6%) 3(14.3%) Middle 38(57.6%) 15(68.2%) 13(61.9%) High 0(0) 0(0) 0(0) Unknown 16(24.2%) 4(18.2%) 5(23.8%) Tumor regression grade, n(%) χ ²=2.618 0.876 0 7(10.6%) 2(9.1%) 2(9.5%) 1 22(33.3%) 7(31.8%) 5(23.8%) 2 29(43.9%) 11(50.0%) 9(42.9%) 3 8(12.1%) 2(9.1%) 5(23.8%) Tumor size (cm),M(IQR) 2.0(2.0) 3.0(1.9) 2.5(2.0) H = 3.999 0.135 Proximal margin (cm),M(IQR) 1.5(1.6) 1.5(2.1) 1.5(1.9) H = 0.653 0.721 Total lymph nodes harvested,M(IQR) 14.0(5.0) 20.0(7.0) a 16.0(5.0) a H = 28.733 0.000 ≥12 lymph nodes harvested, n(%) χ ²=7.247 0.016 Yes 55(83.3%) 22(100%) 21(100%) No 11(16.7%) 0(0) 0(0) Surgery duration (min),M(IQR) 221.0(79.0) 230.5(115.0) 225.0(117.0) H = 0.507 0.776 Blood loss (ml),M(IQR) 100.0(103) 100.0(113) 100.0(150) H = 0.902 0.637 Postoperative hospital stay (day),M(IQR) 11.0(5) 8.5(4) 10.0(3) H = 5.144 0.076 Hospital costs(RMB),M(IQR) 61311.29(12116.71) 55391.29(14543.13) 62335.23(9603.36) H = 3.539 0.170 Complication, n(%) χ ²=1.155 0.623 Yes 16(24.2%) 6(27.3%) 3(14.3%) No 50(75.8%) 16(72.7%) 18(85.7%) Intra-abdominal infection, n(%) 8(12.1%) 2(9.1%) 3(14.3%) χ ²=0.365 0.844 Anastomotic leakage, n(%) 6(9.1%) 2(9.1%) 0(0) χ ²=1.859 0.458 Urinary tract infection, n(%) 2(3.0%) 1(4.5%) 0(0) χ ²=0.917 1.000 Hemorrhage, n(%) 1(1.5%) 1(4.5%) 0(0) χ ²=1.523 0.636 "a" indicates a statistically significant difference compared to the non-injected group. All comparisons were corrected using the Bonferroni method. 3. Univariate and Multivariate Analysis of Lymph Node Harvested Table 3 presents the univariate analysis of factors influencing the total number of lymph nodes harvested. The group factor showed a significant difference, with the Pre-neoadjuvant injection group having the highest median lymph node count (20.0) compared to the Non-lymph node injection group (14.0) and Preoperative injection group (16.0) (P < 0.001). Additionally, tumor size was found to be significantly associated with the total number of lymph nodes harvested (P = 0.049), with tumors ≤ 2 cm having a lower median lymph node count than those > 2 cm. Other variables such as gender, age, BMI, comorbidity, pre-treatment cT and cN stage, tumor distance from anal verge, surgical approach, surgical procedure, ASA classification, pT stage, pN stage, ypTNM stage, differentiation, tumor regression grade, proximal margin, surgery duration, and blood loss did not show significant associations with the total number of lymph nodes harvested (P > 0.05). Table 3 Univariate analysis of lymph node harvested Variable N(%) Total lymph nodes harvested, M (IQR) Statistical value P value Group H = 28.733 0.000 Non-injection 66(60.6%) 14.0(5) Pre-neoadjuvant injection 22(20.2%) 20.0(7) Preoperative injection 21(19.3%) 16.0(5) Gender Z = 0.472 0.637 Female 38(34.9%) 15.5(6) Male 71(65.1%) 15.0(6) Age Z =-0.445 0.656 ≤ 50 28(25.7%) 16.0(7) >50 81(74.3%) 15.0(7) BMI H = 0.691 0.875 <18.5 kg/m 2 2(1.8%) 18.0(-) 18.5–24.9 kg/m 2 81(74.3%) 15.0(7) 25.0-29.9 kg/m 2 23(21.1%) 15.0(4) ≥ 30 kg/m 2 3(2.8%) 18.0(-) Comorbidity Z = 0.317 0.751 No 68(62.4%) 15.0(6) Yes 41(37.6%) 16.0(7) Pre-treatment cT stage H = 0.752 0.687 T2 3(2.8%) 14.0(-) T3 77(70.6%) 15.0(6) T4 29(26.6%) 15.0(7) Pre-treatment cN stage H = 2.830 0.243 N0 5(4.6%) 16.0(9) N1 36(33.0%) 14.0(7) N2 68(62.4%) 15.5(6) Tumor distance from anal verge Z = 0.951 0.342 ≤ 5cm 74(67.9%) 15.0(5) >5cm 35(32.1%) 18.0(8) Surgical approach Z = 0.327 0.744 Laparoscopy 104(95.4%) 15.0(6) Laparotomy 5(4.6%) 14.0(17) Surgical procedure H = 2.611 0.455 Dixon 8(7.3%) 15.0(7) Dixon + colostomy 74(67.9%) 15.5(7) Harrtmann 1(0.9%) - Miles 26(23.9%) 15.0(5) ASA classification Z =-0.446 0.656 II 94(86.2%) 15.0(7) III 15(13.8%) 14.0(5) pT stage H = 3.682 0.451 T0 12(11.0%) 15.5(9) T1 5(4.6%) 14.0(5) T2 21(19.3%) 15.0(8) T3 68(62.4%) 15.0(6) T4 3(2.8%) 19.0(-) pN stage H = 0.383 0.826 N0 73(67.0%) 15.0(7) N1 28(25.7%) 15.0(6) N2 8(7.3%) 15.0(6) yp TNM stage H = 2.293 0.514 0 11(10.1%) 14.0(9) I 22(20.2%) 14.5(6) II 40(36.7%) 16.0(7) III 36(33.0%) 15.0(6) Differentiation H = 2.648 0.266 Low 18(16.5%) 14.0(2) Middle 66(60.6%) 16.0(6) Unknown 25(22.9%) 17.0(8) Tumor regression grade H = 0.743 0.863 0 11(10.1%) 14.0(9) 1 34(31.2%) 15.5(9) 2 49(45.0%) 15.0(6) 3 15(13.8%) 15.0(6) Tumor size Z = 1.965 0.049 ≤ 2cm 50(45.9%) 14.0(6) >2cm 59(54.1%) 17.0(7) Proximal margin Z = 1.854 0.064 ≤ 1.5cm 63(57.8%) 15.0(7) >1.5cm 46(42.2%) 17.0(5) Surgery duration Z = 0.316 0.752 ≤ 225min 55(50.5%) 15.0(6) >225min 54(49.5%) 16.0(7) Blood loss Z =-0.960 0.337 <100ml 36(33.0%) 15.5(5) ≥ 100ml 73(67.0%) 15.0(7) Table 4 presents the multivariate analysis of factors influencing the total number of lymph nodes harvested. The group factor showed significant differences, with the Non-injection group having a coefficient of -2.913 (P = 0.013) and the Pre-neoadjuvant injection group having a coefficient of 3.837 (P = 0.007), indicating a lower and higher number of lymph nodes harvested compared to the Preoperative injection group, respectively. Other variables such as tumor size (≤ 2 cm) and proximal margin (≤ 1.5 cm) were not found to be significant predictors in the multivariate model(P > 0.05). Table 4 Multivariate analysis of lymph node harvested Variable Coefficient Standard Error 95% confidence interval P value Lower Limit Upper Limit Intercept 18.651 1.1618 16.374 20.929 0.000 Group Non-injection -2.913 1.1687 -5.203 -0.622 0.013 Pre-neoadjuvant injection 3.837 1.4123 1.069 6.605 0.007 Tumor size ≤ 2cm -0.681 0.9046 -2.454 1.092 0.452 Proximal margin ≤ 1.5cm -1.749 0.8997 -3.512 0.015 0.052 Scale 21.390 2.8974 16.402 27.894 Discussion Neoadjuvant chemoradiotherapy combined with total mesorectal excision is widely recognized as the standard treatment for locally advanced rectal cancer due to its ability to reduce local tumor recurrence [ 2 ]. However, while neoadjuvant chemoradiotherapy is effective at killing tumor cells, it also leads to lymphocyte depletion in lymph nodes and causes stromal proliferation and fibrosis within them. This results in the shrinkage of lymph nodes, complicating their detection and reducing the number of detectable lymph nodes [ 8 ]. Nano-carbon has been utilized as a tumor localization marker and lymph node tracer in colorectal cancer surgeries that do not involve chemoradiotherapy, and studies have confirmed its efficacy in increasing the number of detectable lymph nodes [ 13 , 14 ]. Nevertheless, there is a scarcity of research on its application in patients undergoing neoadjuvant therapy for rectal cancer. Research indicates that injecting nano-carbon suspension 24 hours before surgery in patients receiving neoadjuvant therapy can enhance both the number of detectable lymph nodes and the proportion of patients with ≥ 12 detectable lymph nodes, aligning with our study results [ 15 – 17 ]. At present, there is a lack of studies comparing the effect of injection before neoadjuvant treatment and 24 hours before surgery of nano-carbon suspension. Our study found that the number of lymph nodes detected after nano-carbon injection before neoadjuvant therapy, and 24 hours before surgery, was significantly higher than in the group that did not receive nano-carbon injection. However, there is no statistically significant difference in the number of detected lymph nodes between the group injected with nano-carbon before neoadjuvant therapy and the group injected 24 hours before surgery (20 vs.16, P > 0.05). Nevertheless, while we observed a trend towards higher lymph node detection in the pre-neoadjuvant injection group, we acknowledge that this finding is not definitive and requires further investigation with a larger sample size. Multivariate analysis confirmed that nano-carbon injection independently enhanced lymph node detection, with the pre-neoadjuvant injection group showing the highest lymph node harvested (coefficient = 3.837, P = 0.007). Tumor size (≤ 2 cm) was marginally significant in univariate analysis (P = 0.049) but not in multivariate models(P = 0.452), suggesting its association might be confounded by injection strategies rather than reflecting independent biological behavior. A separate study on gastric cancer patients who underwent surgery after neoadjuvant therapy showed that lymph node detection was higher when tracing was performed before neoadjuvant therapy than 24 hours before surgery [ 18 ]. Therefore, regarding the timing of nano-carbon injection, we prefer injection before neoadjuvant therapy. First, neoadjuvant therapy can cause lymphatic vessel fibrosis, obstructing normal lymphatic drainage. If nano-carbon is injected before surgery, the obstructed lymph nodes may not be adequately stained, increasing the risk of missed lymph nodes, particularly in patients with suspected lymph node metastasis prior to surgery. Second, injecting nano-carbon before neoadjuvant therapy can provide sufficient time for dispersion, allowing the lymph nodes in the drainage area to be stained as much as possible, which may help improve the accuracy of lymph node detection. Additionally, our study lacked pathological confirmation, such as data on the staining rate of lymph nodes and evidence of lymphatic vessel obstruction, which are critical for fully evaluating the effectiveness of the injection timing. Further studies with larger sample sizes, more detailed analyses, and comprehensive pathological assessments are needed to confirm the optimal timing of nano-carbon injection. One of the problems with nano-carbon injection is the staining of the surrounding tissues and organs of the rectum. To minimize the adverse effects of nano-carbon on the surrounding tissues and organs of the rectum caused by staining, we strive to optimize the injection site and injection method in addition to having the procedure performed by experienced and professional endoscopists. Injecting saline into the submucosal layer separates the mucosa from the muscular layer, forming a natural cavity that completely encloses the nano-carbon suspension between the intestinal walls. This approach helps to avoid the injection needle from penetrating the intestinal wall to the greatest extent, thereby reducing the staining of surrounding tissues. This is shown in Fig. 3 . Furthermore, due to the relatively weak mesorectum of the anterior rectal wall, the likelihood of nano-carbon staining surrounding organs (such as the prostate and vaginal wall) is significantly increased, which may complicate the dissection of the anterior rectal wall and increase surgical risk. Therefore, we chose the injection sites on the posterior and lateral walls of the rectum, where the mesorectum is relatively thick. Even if nano-carbon leaks, it will be contained within the mesorectum, minimizing the impact on the surgical plane and enabling better completion of total mesorectal excision. Staining near the proximal end of the tumor can further minimize the impact of nano-carbon on the assessment of the safe surgical margin for tumor resection. This study has its limitations. First, the lymph nodes in this study were retrieved by the pathology department, and no data were collected on the staining rate of lymph nodes, the staining rate of positive lymph nodes, and other related metrics. Second, while the results indicate that lymph node tracing (whether by pre-neoadjuvant nano-carbon injection or preoperative nano-carbon injection) can increase the detection rate of lymph nodes in patients who have undergone neoadjuvant therapy, its positive impact on long-term prognosis remains unverified and requires further examination through 3-year and 5-year overall survival outcomes. Additionally, this study was retrospective and had a relatively small sample size, which may affect the accuracy of the results to some extent. Therefore, the findings of this study need to be validated further through a well-designed, large-scale randomized controlled trial. Conclusion Endoscopic submucosal injection of nano-carbon suspension for lymph node tracing can increase the number of lymph nodes detected during rectal cancer surgery after neoadjuvant therapy, thereby allowing for more accurate postoperative tumor staging. While our study suggests a potential advantage of injecting nano-carbon before neoadjuvant therapy compared to injecting it 24 hours before surgery, this finding is not statistically significant and requires further validation through larger studies. Future research should focus on optimizing the timing of nano-carbon injection and exploring its long-term impact on patient outcomes. Declarations The authors declare that they have no conflict of interest. The authors declare no competing interests. Grant support: There was no funding for this study. Funding There was no funding for this study. Author Contribution Shuai Shen: Research design, data collection and analysis, and drafting the initial manuscript Hui Gao: Research design, data collection and analysis, and drafting the initial manuscriptZhongzheng Cao: Assisted in research design, data collection and analysis, and manuscript revisionWenlong Xia: Assisted in data collection and analysis, manuscript revisionChengren Li: Research design and guidance, manuscript review and revision Data availability The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. References Sung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71(3):209–249 Benson AB 3rd, Venook AP, Cederquist L et al (2017) Colon Cancer, Version 1.2017, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 15(3):370–398 Sauer R, Becker H, Hohenberger W et al (2004) Preoperative versus postoperative chemoradiotherapy for rectal cancer. N Engl J Med 351(17):1731–1740 Sauer R, Liersch T, Merkel S et al (2012) Preoperative versus postoperative chemoradiotherapy for locally advanced rectal cancer: results of the German CAO/ARO/AIO-94 randomized phase III trial after a median follow-up of 11 years. J Clin Oncol 30(16):1926–1933 Roh MS, Colangelo LH, O'Connell MJ et al (2009) Preoperative multimodality therapy improves disease-free survival in patients with carcinoma of the rectum: NSABP R-03. J Clin Oncol 27(31):5124–5130 Norwood MG, Sutton AJ, West K et al (2010) Lymph node retrieval in colorectal cancer resection specimens: national standards are achievable, and low numbers are associated with reduced survival. Colorectal Dis 12(4):304–309 Mechera R, Schuster T, Rosenberg R et al (2017) Lymph node yield after rectal resection in patients treated with neoadjuvant radiation for rectal cancer: A systematic review and meta-analysis. Eur J Cancer 72:84–94 Marks JH, Valsdottir EB, Rather AA et al (2010) Fewer than 12 lymph nodes can be expected in a surgical specimen after high-dose chemoradiation therapy for rectal cancer. Dis Colon Rectum 53(7):1023–1029 Morcos B, Baker B, Al Masri M et al (2010) Lymph node yield in rectal cancer surgery: effect of preoperative chemoradiotherapy. Eur J Surg Oncol 36(4):345–349 Miller ED, Robb BW, Cummings OW et al (2012) The effects of preoperative chemoradiotherapy on lymph node sampling in rectal cancer. Dis Colon Rectum 55(9):1002–1007 Damin DC, Rosito MA, Contu PC et al (2012) Lymph node retrieval after preoperative chemoradiotherapy for rectal cancer. J Gastrointest Surg 16(8):1573–1580 Hsieh FJ, Wang YC, Hsu JT et al (2012) Clinicopathological features and prognostic factors of gastric cancer patients aged 40 years or younger. J Surg Oncol 105(3):304–309 Wei Ge G, Chen (2024) Clinical research on the value of carbon nanoparticles navigation lymphatic tracing in TNM staging of colorectal cancer. Chin J Colorectal Diseases(Electronic Edition) 13(4):288–293 Zhang XM, Liang JW, Wang Z et al (2016) Effect of preoperative injection of carbon nanoparticle suspension on the outcomes of selected patients with mid-low rectal cancer. Chin J Cancer 35:33 Xin Jin H, Tan X, Wu et al (2021) Effect of submucosal injection of carbon nanoparticles on lymph node yield and anastomosis safety after neoadjuvant therapy for mid- and low rectal cancer. J Colorectal Anal Surg 27(4):334–337 Shao F, Zhou Q, Yu F et al (2024) Clinical value of nano-carbon lymphatic tracer for regional lymph node dissections of rectal cancer after neoadjuvant chemoradiotherapy. J Appl Clin Med Phys 25(8):e14406 Wang Y, Deng H, Chen H et al (2015) Preoperative Submucosal Injection of Carbon Nanoparticles Improves Lymph Node Staging Accuracy in Rectal Cancer after Neoadjuvant Chemoradiotherapy. J Am Coll Surg 221(5):923–930 Yang P, Tian Y, Tan B et al (2021) Clinical application of nano-carbon to improve the accuracy of lymph node staging in patients with advanced gastric cancer receiving neoadjuvant chemotherapy: a prospective randomized controlled trial. J Gastrointest Oncol 12(5):2052–2060 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Aug, 2025 Read the published version in International Journal of Colorectal Disease → Version 1 posted Editorial decision: Revision requested 06 Jul, 2025 Reviews received at journal 03 Jul, 2025 Reviews received at journal 23 Jun, 2025 Reviewers agreed at journal 20 Jun, 2025 Reviewers agreed at journal 20 Jun, 2025 Reviewers agreed at journal 20 Jun, 2025 Reviewers agreed at journal 19 Jun, 2025 Reviews received at journal 25 Apr, 2025 Reviewers agreed at journal 16 Apr, 2025 Reviewers invited by journal 01 Apr, 2025 Submission checks completed at journal 30 Mar, 2025 First submitted to journal 29 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-6066404","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436671423,"identity":"39a6fe05-78ce-4200-946a-bfcb8e68865c","order_by":0,"name":"Shuai Shen","email":"","orcid":"","institution":"Weifang People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Shen","suffix":""},{"id":436671424,"identity":"68278f7d-f4c1-4e46-ada5-06f320395cfc","order_by":1,"name":"Hui Gao","email":"","orcid":"","institution":"Weifang People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Gao","suffix":""},{"id":436671425,"identity":"cf204c77-4808-448d-99cd-4b8d70a90a76","order_by":2,"name":"Zhongzheng Cao","email":"","orcid":"","institution":"Weifang People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhongzheng","middleName":"","lastName":"Cao","suffix":""},{"id":436671426,"identity":"293f9e1b-4700-4c17-86ec-b4b1418d81de","order_by":3,"name":"Wenlong Xia","email":"","orcid":"","institution":"Weifang People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenlong","middleName":"","lastName":"Xia","suffix":""},{"id":436671427,"identity":"4ee31686-ce61-42ae-b972-0af73bcd9a69","order_by":4,"name":"Chengren Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIie3PoQvCQBTH8SeClhtrcuLQf2FD0CL4r+yKJpPl4h2KRqviP2E0vrFgeWAdCLLhPzCxGhQFg4g7m+G++ffh8QBstr+spEACeABlxFyaEa0IgAFURLQk0zNPwtqxMzXY+7t4nqI8su6ccnQUtNwaFhASWiGNmUejDda3EKzW4XfSQaEn51nIOHc2GBCE/qGI7DOtogdhKYqZCUnEiwBGJqSfZHqJdCds4EeKePEv9cUwzVGGTV6NT5er7LXcRgF5j/82t9lsNtvnbqG+TJxABCu/AAAAAElFTkSuQmCC","orcid":"","institution":"Weifang People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Chengren","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-02-19 18:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6066404/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6066404/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00384-025-04982-y","type":"published","date":"2025-08-19T16:29:25+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79833879,"identity":"d5491e8c-f99c-4632-974c-491957bc9338","added_by":"auto","created_at":"2025-04-03 11:05:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":648557,"visible":true,"origin":"","legend":"\u003cp\u003eEndoscopically injected carbon nanosuspension submucosa\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6066404/v1/aead2377b3dfb9c1713f9ecc.png"},{"id":79833392,"identity":"566437a9-7a7a-4e4d-b515-9c4bb0fafd2e","added_by":"auto","created_at":"2025-04-03 10:57:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":869735,"visible":true,"origin":"","legend":"\u003cp\u003eLymph nodes in non-operative areas without suspected metastasis were not routinely dissected\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6066404/v1/d571db7a69f2227cc28cc273.png"},{"id":79833880,"identity":"49f9bdec-a5c6-4949-96c7-320ffda1f682","added_by":"auto","created_at":"2025-04-03 11:05:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1524534,"visible":true,"origin":"","legend":"\u003cp\u003eThe nano-carbon was wrapped in the mesorectum and did not affect the judgment of tissue space\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6066404/v1/91b953ed621e8a936ff2478a.png"},{"id":89847194,"identity":"0fe0d907-5840-47ef-8154-f5b07abe5c1a","added_by":"auto","created_at":"2025-08-25 16:41:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4204124,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6066404/v1/26ad2cd7-dbc0-483f-bc72-84642a2b4153.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Nano-Carbon Suspension Lymph Node Tracing on Rectal Cancer Surgery Post- Neoadjuvant Therapy","fulltext":[{"header":"Background","content":"\u003cp\u003eColorectal cancer is one of the most prevalent malignancies globally, ranking third in incidence and second in mortality among all cancers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Due to the rectum's unique anatomical position and the absence of obvious clinical symptoms, most patients are diagnosed at an advanced stage. The standard treatment for locally advanced rectal cancer is neoadjuvant chemoradiotherapy followed by total mesorectal excision [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Studies have demonstrated that neoadjuvant therapy, compared to adjuvant therapy after surgery, can enhance the rate of intraoperative tumor resection and the preservation of the anal sphincter, while also improving local tumor control and reducing adverse effects [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLymph node dissection is a crucial component of radical surgery for rectal cancer. Standardized lymph node dissection and obtaining an adequate number of lymph nodes are vital for effective tumor eradication. Insufficient lymph node retrieval not only leads to inaccurate staging but also reduces postoperative survival [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. After neoadjuvant therapy, regional lymph nodes undergo regressive changes, increasing the difficulty of detection [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Radiotherapy can cause lymphocyte depletion within the lymph nodes, along with interstitial proliferation and fibrosis, resulting in lymph node shrinkage. This makes distinguishing lymph nodes morphologically challenging, thereby complicating their detection through visual inspection and sampling [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Several international studies have shown that under the same surgical and pathological examination standards, neoadjuvant chemoradiotherapy significantly reduces the number of lymph nodes retrieved [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e–\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. One study even found that up to 72% of patients had fewer than 12 lymph nodes retrieved in their surgical specimens, and 23% had fewer than 5 lymph nodes retrieved [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe use of effective lymph node tracers during surgery can increase the number of lymph nodes dissected and the detection of micrometastases. In laparoscopic surgery, effective lymph node tracers can compensate for the inability to palpate lymph nodes, further highlighting regional lymphatic tissue and avoiding damage to surrounding structures. However, for patients undergoing neoadjuvant therapy, chemoradiotherapy may cause fibrosis in lymphatic vessels and lymph nodes, potentially affecting the uptake of nano-carbon by lymph nodes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Currently, most studies inject nano-carbon into the submucosa 24 hours before surgery, which may result in some regional lymph nodes not being stained. Therefore, our center has conducted a study on the impact of nano-carbon submucosal injection on radical surgery for rectal cancer post-neoadjuvant therapy. This study aims to explore the effects of nano-carbon submucosal injection and its timing on the number of lymph nodes retrieved and postoperative complications.\u003c/p\u003e"},{"header":"Patients and Methods","content":"\u003cp\u003e\u003cstrong\u003e1. General data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA retrospective analysis was conducted on the clinical and pathological data of 109 rectal cancer patients who underwent surgical treatment at the Department of Colorectal and Anal Surgery, Weifang People\u0026apos;s Hospital, from January 2020 to December 2022. All patients underwent laparoscopic radical resection for rectal cancer following neoadjuvant therapy. Among the patients, 43 received a nano-carbon injection; 22 of these patients received an endoscopic submucosal injection of nano-carbon before neoadjuvant therapy, and 21 received a nano-carbon injection 24 hours before surgery. The remaining 66 patients did not receive a nano-carbon submucosal injection for lymph node tracing. The inclusion and exclusion criteria were as follows:\u003c/p\u003e\n\u003cp\u003eInclusion criteria:\u003c/p\u003e\n\u003cp\u003e1. Preoperative pathological diagnosis of adenocarcinoma\u003c/p\u003e\n\u003cp\u003e2. Preoperative pelvic MRI or CT assessment indicating tumor T stage as T3 or T4, or suggesting lymph node metastasis\u003c/p\u003e\n\u003cp\u003e3. No evidence of distant metastasis, such as in the lungs or liver, as determined by chest and abdominal CT; radical surgery is feasible\u003c/p\u003e\n\u003cp\u003eExclusion criteria:\u003c/p\u003e\n\u003cp\u003e1. Distant metastasis to the liver, lungs, or other organs during neoadjuvant therapy\u003c/p\u003e\n\u003cp\u003e2. Symptoms such as intestinal obstruction or bleeding during neoadjuvant therapy requiring emergency surgery\u003c/p\u003e\n\u003cp\u003e3. Patients unable to tolerate chemoradiotherapy and who did not complete the full course of neoadjuvant therapy\u003c/p\u003e\n\u003cp\u003e4. Patients with severe underlying diseases who could not tolerate surgery\u003c/p\u003e\n\u003cp\u003e5. Patients considered to have complete clinical response (cCR) after neoadjuvant therapy and requested watch-and-wait (W\u0026amp;W) management\u003c/p\u003e\n\u003cp\u003e6. Patients with other malignant tumors or a history of malignant tumors\u003c/p\u003e\n\u003cp\u003eThis study obtained approval from the Ethics Committee of Weifang people\u0026rsquo;s Hospital, and due to its retrospective nature, the need for obtaining written informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe confirmed that all methods were carried out in accordance with relevant guidelines and regulations. Patients undergoing lymph node tracing received an endoscopic submucosal injection of nano-carbon suspension under intravenous general anesthesia either before neoadjuvant therapy or 24 hours before surgery. This procedure was performed by experienced gastroenterologists.\u003c/p\u003e\n\u003cp\u003eThe injection sites varied depending on the location of the tumor. Typically, nano-carbon was injected submucosally at three sites on both the posterior and lateral walls of the rectum. The anterior rectal wall was not used as an injection site. The injection sites were approximately 0.5 cm away from the tumor, starting with the intestinal wall at the same plane as the tumor, followed by the proximal end of the tumor. For tumors that could not be accessed by colonoscopy, the distal end was chosen for injection. Prior to the nano-carbon injection, normal saline was injected into the submucosal layer to create a transparent submucosal bulge. Subsequently, the nano-carbon suspension was drawn and injected at each site. This is shown in Figure 1.\u003c/p\u003e\n\u003cp\u003eAll patients received preoperative neoadjuvant therapy according to the following regimen: a total radiotherapy dose of 45\u0026ndash;50 Gy, administered at 1.8\u0026ndash;2.0 Gy per fraction, five fractions per week, completed within five weeks; concurrent chemotherapy with capecitabine at a dose of 1250 mg/m2, taken orally twice daily. Preoperative assessment was performed eight weeks after the completion of neoadjuvant radiotherapy, followed by laparoscopic radical resection for rectal cancer. All surgeries adhered to the principles of TME (total mesorectal excision) and mesorectal dissection. As shown in Figure 2, lymph nodes in non-surgical areas without suspected metastases were not routinely dissected. Post-surgery, the specimens were sent to the pathology department, where pathologists retrieved the lymph nodes and processed the tumor specimens.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Observation indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(1) The primary observation indicators included the number of lymph nodes retrieved and the proportion of patients from whom \u0026ge;12 lymph nodes were retrieved.\u003c/p\u003e\n\u003cp\u003e(2) The secondary indicators recorded were operative time, blood loss, tumor regression grade, postoperative complication rate, postoperative length of stay, and hospitalization costs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were analyzed using SPSS 24.0 statistical software. Categorical data are presented as frequencies (n) and were compared using the \u0026chi;2 test, the corrected \u0026chi;2 test, or Fisher\u0026apos;s exact test. For continuous variables, the normality is tested using the Shapiro-Wilk test. Normally distributed continuous data are presented as mean \u0026plusmn; standard deviation (x̄ \u0026plusmn; s) and were compared using one-way analysis of variance (ANOVA) or the Brown-Forsythe/Welch test. Non-normally distributed continuous data are presented as median (interquartile range, IQR) and were compared using the Kruskal-Wallis test (a non-parametric test for independent samples). Using the number of harvested lymph nodes as the dependent variable, univariate analysis was conducted on the relevant clinical and pathological data. Indicators with a P-value less than 0.1 were selected for multivariate analysis using a generalized linear model. A P-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e1. Clinical and Pathological Data\u003c/h2\u003e \u003cp\u003eBased on the inclusion and exclusion criteria, a total of 109 patients were included in this study, with 66 patients in the Non-injection group, 22 patients in the Pre-neoadjuvant injection group, and 21 patients in the Preoperative injection group. The general clinical and pathological data for these three groups are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. No significant differences were observed in gender, age, BMI, comorbidity, pre-treatment cT or cN stage, tumor distance from anal verge, surgical approach, procedure, or ASA classification (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eClinical and pathological data\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-injection group n\u0026thinsp;=\u0026thinsp;66\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-neoadjuvant injection group n\u0026thinsp;=\u0026thinsp;22\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePreoperative injection group n\u0026thinsp;=\u0026thinsp;21\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistical value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGende, n(%)\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 \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;=1.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43(65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(72.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(57.1%)\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23(34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(42.9%)\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\u003eAge (year),M(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.5(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.0(20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.0(15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;1.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e),M(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.10(3.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.73(6.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.44(4.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;0.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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 \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;=0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23(34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(40.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(42.9%)\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43(65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(59.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(57.1%)\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\u003ePre-treatment cT stage, n(%)\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 \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;=4.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(4.8%)\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\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50(75.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(71.4%)\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\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15(22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(40.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(23.8%)\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\u003ePre-treatment cN stage, n(%)\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 \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;=7.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.108\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(14.3%)\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\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25(37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(33.3%)\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\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40(60.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(77.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(52.4%)\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\u003eTumor distance from anal verge (cm),M(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.0(4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0(4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;1.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical approach\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 \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;=4.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaparotomy, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2(3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(14.3%)\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\u003eLaparoscopy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64(97.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(85.7%)\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\u003eSurgical procedure, n(%)\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 \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;=4.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDixon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7(10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(4.8%)\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\u003eDixon\u0026thinsp;+\u0026thinsp;colostomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44(66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(72.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(66.7%)\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\u003eHarrtmann\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\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\u003eMiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14(21.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(28.6%)\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\u003eASA classification, n(%)\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 \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;=1.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.551\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55(83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(90.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(90.5%)\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\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11(16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(9.5%)\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2. Intraoperative Data and Short-term Clinical Outcomes\u003c/h2\u003e \u003cp\u003eThe intraoperative conditions and short-term clinical outcomes for patients in each group are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. No significant differences were observed in pT stage, pN stage, yPTNM stage, tumor differentiation, or tumor regression grade among the groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).The number of dissected lymph nodes and the proportion of patients with \u0026ge;\u0026thinsp;12 lymph nodes in the pre-neoadjuvant injection group and the preoperative 24-hour injection group were significantly higher than those in the non-injection group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There is no statistically significant difference in the number of detected lymph nodes between the group injected with nano-carbon before neoadjuvant therapy and the group injected 24 hours before surgery (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).However, there were no significant differences in tumor size, proximal margin, surgery duration, blood loss, postoperative hospital stay, hospital costs, or overall complication rates among the groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Specifically, intra-abdominal infection, anastomotic leakage, urinary tract infection, and hemorrhage rates did not differ significantly between the groups. Nano-carbon injection did not affect tumor-related indicators such as tumor staging and tumor regression grade after chemoradiotherapy, nor did it increase postoperative complications.\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\u003eIntraoperative data and short-term clinical outcomes\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\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-injection group n\u0026thinsp;=\u0026thinsp;66\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-neoadjuvant injection group n\u0026thinsp;=\u0026thinsp;22\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePreoperative injection group n\u0026thinsp;=\u0026thinsp;21\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistical value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epT stage, n(%)\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 \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;=6.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(9.5%)\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\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(9.5%)\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\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(4.8%)\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\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(57.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(68.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(71.4%)\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\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(4.8)\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\u003epN stage, n(%)\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 \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;=0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.964\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\u003e43(65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(72.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(66.7%)\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\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(23.8%)\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\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(7.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(9.5%)\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\u003eyPTNM stage, n(%)\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 \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;=3.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(9.5%)\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\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(14.3%)\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\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(30.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(42.9%)\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\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(33.3%)\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\u003eDifferentiation, n(%)\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 \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;=0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(14.3%)\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\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38(57.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(68.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(61.9%)\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\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\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\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(23.8%)\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\u003eTumor regression grade, n(%)\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 \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;=2.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(9.5%)\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(31.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(23.8%)\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\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(42.9%)\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\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(23.8%)\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\u003eTumor size (cm),M(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0(2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0(1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5(2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;3.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProximal margin (cm),M(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.5(1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.5(1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal lymph nodes harvested,M(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.0(5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0(7.0)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.0(5.0)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;28.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;12 lymph nodes harvested, n(%)\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 \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;=7.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.016\u003c/p\u003e \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\u003e55(83.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(100%)\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\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\u003eSurgery duration (min),M(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221.0(79.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e230.5(115.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e225.0(117.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;0.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood loss (ml),M(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.0(103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.0(113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100.0(150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostoperative hospital stay (day),M(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.0(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.0(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;5.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital costs(RMB),M(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61311.29(12116.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55391.29(14543.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62335.23(9603.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;3.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplication, n(%)\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 \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;=1.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.623\u003c/p\u003e \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\u003e16(24.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(14.3%)\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50(75.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(72.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(85.7%)\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\u003eIntra-abdominal infection, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;=0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnastomotic leakage, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;=1.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary tract infection, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;=0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemorrhage, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u0026sup2;=1.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\"a\" indicates a statistically significant difference compared to the non-injected group. All comparisons were corrected using the Bonferroni method.\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\n\u003ch3\u003e3. Univariate and Multivariate Analysis of Lymph Node Harvested\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the univariate analysis of factors influencing the total number of lymph nodes harvested. The group factor showed a significant difference, with the Pre-neoadjuvant injection group having the highest median lymph node count (20.0) compared to the Non-lymph node injection group (14.0) and Preoperative injection group (16.0) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, tumor size was found to be significantly associated with the total number of lymph nodes harvested (P\u0026thinsp;=\u0026thinsp;0.049), with tumors\u0026thinsp;\u0026le;\u0026thinsp;2 cm having a lower median lymph node count than those\u0026thinsp;\u0026gt;\u0026thinsp;2 cm. Other variables such as gender, age, BMI, comorbidity, pre-treatment cT and cN stage, tumor distance from anal verge, surgical approach, surgical procedure, ASA classification, pT stage, pN stage, ypTNM stage, differentiation, tumor regression grade, proximal margin, surgery duration, and blood loss did not show significant associations with the total number of lymph nodes harvested (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis of lymph node harvested\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal lymph nodes harvested, M (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistical value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\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 \u003cp\u003eH\u0026thinsp;=\u0026thinsp;28.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-injection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66(60.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0(5)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-neoadjuvant injection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0(7)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreoperative injection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(19.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0(5)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.637\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\u003e38(34.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5(6)\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 \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\u003e71(65.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(6)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e =-0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(25.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0(7)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81(74.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(7)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\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 \u003cp\u003eH\u0026thinsp;=\u0026thinsp;0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.0(-)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5\u0026ndash;24.9 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81(74.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(7)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25.0-29.9 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23(21.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(4)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.0(-)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidity\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 \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.751\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\u003e68(62.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(6)\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 \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\u003e41(37.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0(7)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-treatment cT 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=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0(-)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77(70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(6)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(26.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(7)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-treatment cN 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=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;2.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.243\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\u003e5(4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0(9)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36(33.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0(7)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68(62.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5(6)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor distance from anal verge\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 \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74(67.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(5)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35(32.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.0(8)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical approach\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 \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaparoscopy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104(95.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(6)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaparotomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0(17)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical procedure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;2.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDixon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(7)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDixon\u0026thinsp;+\u0026thinsp;colostomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74(67.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5(7)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarrtmann\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(5)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASA classification\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 \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e =-0.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94(86.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(7)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0(5)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epT 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=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;3.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(11.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5(9)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0(5)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(19.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(8)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68(62.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(6)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.0(-)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epN 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=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.826\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\u003e73(67.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(7)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(25.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(6)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(7.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(6)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyp TNM 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=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u0026thinsp;=\u0026thinsp;2.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.514\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0(9)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22(20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.5(6)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(36.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0(7)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36(33.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(6)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferentiation\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 \u003cp\u003eH\u0026thinsp;=\u0026thinsp;2.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(16.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0(2)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66(60.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0(6)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(22.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.0(8)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor regression grade\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 \u003cp\u003eH\u0026thinsp;=\u0026thinsp;0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0(9)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(31.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5(9)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49(45.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(6)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(6)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size\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 \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50(45.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.0(6)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;2cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59(54.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.0(7)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProximal margin\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 \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63(57.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(7)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;1.5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(42.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.0(5)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery duration\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 \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;225min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(50.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(6)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;225min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54(49.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0(7)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood loss\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 \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e =-0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;100ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36(33.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.5(5)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;100ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73(67.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0(7)\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 \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the multivariate analysis of factors influencing the total number of lymph nodes harvested. The group factor showed significant differences, with the Non-injection group having a coefficient of -2.913 (P\u0026thinsp;=\u0026thinsp;0.013) and the Pre-neoadjuvant injection group having a coefficient of 3.837 (P\u0026thinsp;=\u0026thinsp;0.007), indicating a lower and higher number of lymph nodes harvested compared to the Preoperative injection group, respectively. Other variables such as tumor size (\u0026le;\u0026thinsp;2 cm) and proximal margin (\u0026le;\u0026thinsp;1.5 cm) were not found to be significant predictors in the multivariate model(P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate analysis of lymph node harvested\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\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95% confidence interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower Limit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpper Limit\u003c/p\u003e \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\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\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\u003eNon-injection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.622\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\u003ePre-neoadjuvant injection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size\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\u003e\u0026le;\u0026thinsp;2cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProximal margin\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\u003e\u0026le;\u0026thinsp;1.5cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eNeoadjuvant chemoradiotherapy combined with total mesorectal excision is widely recognized as the standard treatment for locally advanced rectal cancer due to its ability to reduce local tumor recurrence [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, while neoadjuvant chemoradiotherapy is effective at killing tumor cells, it also leads to lymphocyte depletion in lymph nodes and causes stromal proliferation and fibrosis within them. This results in the shrinkage of lymph nodes, complicating their detection and reducing the number of detectable lymph nodes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Nano-carbon has been utilized as a tumor localization marker and lymph node tracer in colorectal cancer surgeries that do not involve chemoradiotherapy, and studies have confirmed its efficacy in increasing the number of detectable lymph nodes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Nevertheless, there is a scarcity of research on its application in patients undergoing neoadjuvant therapy for rectal cancer. Research indicates that injecting nano-carbon suspension 24 hours before surgery in patients receiving neoadjuvant therapy can enhance both the number of detectable lymph nodes and the proportion of patients with \u0026ge;\u0026thinsp;12 detectable lymph nodes, aligning with our study results [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. At present, there is a lack of studies comparing the effect of injection before neoadjuvant treatment and 24 hours before surgery of nano-carbon suspension. Our study found that the number of lymph nodes detected after nano-carbon injection before neoadjuvant therapy, and 24 hours before surgery, was significantly higher than in the group that did not receive nano-carbon injection. However, there is no statistically significant difference in the number of detected lymph nodes between the group injected with nano-carbon before neoadjuvant therapy and the group injected 24 hours before surgery (20 vs.16, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Nevertheless, while we observed a trend towards higher lymph node detection in the pre-neoadjuvant injection group, we acknowledge that this finding is not definitive and requires further investigation with a larger sample size. Multivariate analysis confirmed that nano-carbon injection independently enhanced lymph node detection, with the pre-neoadjuvant injection group showing the highest lymph node harvested (coefficient\u0026thinsp;=\u0026thinsp;3.837, P\u0026thinsp;=\u0026thinsp;0.007). Tumor size (\u0026le;\u0026thinsp;2 cm) was marginally significant in univariate analysis (P\u0026thinsp;=\u0026thinsp;0.049) but not in multivariate models(P\u0026thinsp;=\u0026thinsp;0.452), suggesting its association might be confounded by injection strategies rather than reflecting independent biological behavior.\u003c/p\u003e \u003cp\u003eA separate study on gastric cancer patients who underwent surgery after neoadjuvant therapy showed that lymph node detection was higher when tracing was performed before neoadjuvant therapy than 24 hours before surgery [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, regarding the timing of nano-carbon injection, we prefer injection before neoadjuvant therapy. First, neoadjuvant therapy can cause lymphatic vessel fibrosis, obstructing normal lymphatic drainage. If nano-carbon is injected before surgery, the obstructed lymph nodes may not be adequately stained, increasing the risk of missed lymph nodes, particularly in patients with suspected lymph node metastasis prior to surgery. Second, injecting nano-carbon before neoadjuvant therapy can provide sufficient time for dispersion, allowing the lymph nodes in the drainage area to be stained as much as possible, which may help improve the accuracy of lymph node detection. Additionally, our study lacked pathological confirmation, such as data on the staining rate of lymph nodes and evidence of lymphatic vessel obstruction, which are critical for fully evaluating the effectiveness of the injection timing. Further studies with larger sample sizes, more detailed analyses, and comprehensive pathological assessments are needed to confirm the optimal timing of nano-carbon injection.\u003c/p\u003e \u003cp\u003eOne of the problems with nano-carbon injection is the staining of the surrounding tissues and organs of the rectum. To minimize the adverse effects of nano-carbon on the surrounding tissues and organs of the rectum caused by staining, we strive to optimize the injection site and injection method in addition to having the procedure performed by experienced and professional endoscopists. Injecting saline into the submucosal layer separates the mucosa from the muscular layer, forming a natural cavity that completely encloses the nano-carbon suspension between the intestinal walls. This approach helps to avoid the injection needle from penetrating the intestinal wall to the greatest extent, thereby reducing the staining of surrounding tissues. This is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Furthermore, due to the relatively weak mesorectum of the anterior rectal wall, the likelihood of nano-carbon staining surrounding organs (such as the prostate and vaginal wall) is significantly increased, which may complicate the dissection of the anterior rectal wall and increase surgical risk. Therefore, we chose the injection sites on the posterior and lateral walls of the rectum, where the mesorectum is relatively thick. Even if nano-carbon leaks, it will be contained within the mesorectum, minimizing the impact on the surgical plane and enabling better completion of total mesorectal excision. Staining near the proximal end of the tumor can further minimize the impact of nano-carbon on the assessment of the safe surgical margin for tumor resection.\u003c/p\u003e \u003cp\u003eThis study has its limitations. First, the lymph nodes in this study were retrieved by the pathology department, and no data were collected on the staining rate of lymph nodes, the staining rate of positive lymph nodes, and other related metrics. Second, while the results indicate that lymph node tracing (whether by pre-neoadjuvant nano-carbon injection or preoperative nano-carbon injection) can increase the detection rate of lymph nodes in patients who have undergone neoadjuvant therapy, its positive impact on long-term prognosis remains unverified and requires further examination through 3-year and 5-year overall survival outcomes. Additionally, this study was retrospective and had a relatively small sample size, which may affect the accuracy of the results to some extent. Therefore, the findings of this study need to be validated further through a well-designed, large-scale randomized controlled trial.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eEndoscopic submucosal injection of nano-carbon suspension for lymph node tracing can increase the number of lymph nodes detected during rectal cancer surgery after neoadjuvant therapy, thereby allowing for more accurate postoperative tumor staging. While our study suggests a potential advantage of injecting nano-carbon before neoadjuvant therapy compared to injecting it 24 hours before surgery, this finding is not statistically significant and requires further validation through larger studies. Future research should focus on optimizing the timing of nano-carbon injection and exploring its long-term impact on patient outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGrant support:\u0026nbsp;\u003c/strong\u003eThere was no funding for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no funding for this study.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eShuai Shen: Research design, data collection and analysis, and drafting the initial manuscript Hui Gao: Research design, data collection and analysis, and drafting the initial manuscriptZhongzheng Cao: Assisted in research design, data collection and analysis, and manuscript revisionWenlong Xia: Assisted in data collection and analysis, manuscript revisionChengren Li: Research design and guidance, manuscript review and revision\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71(3):209\u0026ndash;249\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenson AB 3rd, Venook AP, Cederquist L et al (2017) Colon Cancer, Version 1.2017, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 15(3):370\u0026ndash;398\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSauer R, Becker H, Hohenberger W et al (2004) Preoperative versus postoperative chemoradiotherapy for rectal cancer. N Engl J Med 351(17):1731\u0026ndash;1740\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSauer R, Liersch T, Merkel S et al (2012) Preoperative versus postoperative chemoradiotherapy for locally advanced rectal cancer: results of the German CAO/ARO/AIO-94 randomized phase III trial after a median follow-up of 11 years. J Clin Oncol 30(16):1926\u0026ndash;1933\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoh MS, Colangelo LH, O'Connell MJ et al (2009) Preoperative multimodality therapy improves disease-free survival in patients with carcinoma of the rectum: NSABP R-03. J Clin Oncol 27(31):5124\u0026ndash;5130\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorwood MG, Sutton AJ, West K et al (2010) Lymph node retrieval in colorectal cancer resection specimens: national standards are achievable, and low numbers are associated with reduced survival. Colorectal Dis 12(4):304\u0026ndash;309\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMechera R, Schuster T, Rosenberg R et al (2017) Lymph node yield after rectal resection in patients treated with neoadjuvant radiation for rectal cancer: A systematic review and meta-analysis. Eur J Cancer 72:84\u0026ndash;94\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarks JH, Valsdottir EB, Rather AA et al (2010) Fewer than 12 lymph nodes can be expected in a surgical specimen after high-dose chemoradiation therapy for rectal cancer. Dis Colon Rectum 53(7):1023\u0026ndash;1029\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorcos B, Baker B, Al Masri M et al (2010) Lymph node yield in rectal cancer surgery: effect of preoperative chemoradiotherapy. Eur J Surg Oncol 36(4):345\u0026ndash;349\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiller ED, Robb BW, Cummings OW et al (2012) The effects of preoperative chemoradiotherapy on lymph node sampling in rectal cancer. Dis Colon Rectum 55(9):1002\u0026ndash;1007\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDamin DC, Rosito MA, Contu PC et al (2012) Lymph node retrieval after preoperative chemoradiotherapy for rectal cancer. J Gastrointest Surg 16(8):1573\u0026ndash;1580\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsieh FJ, Wang YC, Hsu JT et al (2012) Clinicopathological features and prognostic factors of gastric cancer patients aged 40 years or younger. J Surg Oncol 105(3):304\u0026ndash;309\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei Ge G, Chen (2024) Clinical research on the value of carbon nanoparticles navigation lymphatic tracing in TNM staging of colorectal cancer. Chin J Colorectal Diseases(Electronic Edition) 13(4):288\u0026ndash;293\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang XM, Liang JW, Wang Z et al (2016) Effect of preoperative injection of carbon nanoparticle suspension on the outcomes of selected patients with mid-low rectal cancer. Chin J Cancer 35:33\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXin Jin H, Tan X, Wu et al (2021) Effect of submucosal injection of carbon nanoparticles on lymph node yield and anastomosis safety after neoadjuvant therapy for mid- and low rectal cancer. J Colorectal Anal Surg 27(4):334\u0026ndash;337\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShao F, Zhou Q, Yu F et al (2024) Clinical value of nano-carbon lymphatic tracer for regional lymph node dissections of rectal cancer after neoadjuvant chemoradiotherapy. J Appl Clin Med Phys 25(8):e14406\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Deng H, Chen H et al (2015) Preoperative Submucosal Injection of Carbon Nanoparticles Improves Lymph Node Staging Accuracy in Rectal Cancer after Neoadjuvant Chemoradiotherapy. J Am Coll Surg 221(5):923\u0026ndash;930\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang P, Tian Y, Tan B et al (2021) Clinical application of nano-carbon to improve the accuracy of lymph node staging in patients with advanced gastric cancer receiving neoadjuvant chemotherapy: a prospective randomized controlled trial. J Gastrointest Oncol 12(5):2052\u0026ndash;2060\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":"international-journal-of-colorectal-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijcd","sideBox":"Learn more about [International Journal of Colorectal Disease](http://link.springer.com/journal/384)","snPcode":"384","submissionUrl":"https://submission.nature.com/new-submission/384/3","title":"International Journal of Colorectal Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Rectal cancer, neoadjuvant therapy, nano-carbon, lymph node dissection","lastPublishedDoi":"10.21203/rs.3.rs-6066404/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6066404/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTo explore the impact of using nano-carbon suspension for lymph node tracing on the number of detected lymph nodes and short-term clinical outcomes in patients undergoing laparoscopic radical resection for rectal cancer following neoadjuvant therapy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study retrospectively analyzed clinical data from 109 patients who underwent neoadjuvant therapy and laparoscopic radical resection for rectal cancer at Weifang People's Hospital from January 2020 to December 2022. Of these, 43 patients received an endoscopic submucosal injection of nano-carbon suspension (experimental group), with 22 patients receiving the injection before neoadjuvant therapy and 21 patients receiving it 24 hours before surgery. The remaining 66 patients did not receive the nano-carbon injection (control group). All patients received neoadjuvant therapy according to guidelines and were operated on by the same surgical team. By comparing the number of detected lymph nodes and short-term clinical outcomes among the three groups, the study aimed to investigate the impact of the endoscopic submucosal injection of nano-carbon and the timing of injection on the surgical quality for patients with rectal cancer undergoing neoadjuvant therapy.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe number of detected lymph nodes in the groups injected with nano-carbon before neoadjuvant therapy and 24 hours before surgery was significantly higher than that in the non-injected group, with a significant increase in the proportion of detecting\u0026thinsp;\u0026ge;\u0026thinsp;12 lymph nodes, showing statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There is no statistically significant difference in the number of detected lymph nodes between the group injected with nano-carbon before neoadjuvant therapy and the group injected 24 hours before surgery (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eEndoscopic submucosal injection of nano-carbon suspension for lymph node tracing can increase the number of detected lymph nodes in rectal cancer surgery following neoadjuvant therapy, enabling more precise postoperative tumor staging. Although injecting nano-carbon before neoadjuvant therapy may have potential advantages in lymph node detection, the optimal timing of injection requires further investigation.\u003c/p\u003e","manuscriptTitle":"The Impact of Nano-Carbon Suspension Lymph Node Tracing on Rectal Cancer Surgery Post- Neoadjuvant Therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 10:57:31","doi":"10.21203/rs.3.rs-6066404/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-06T16:25:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-03T23:49:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-23T10:45:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330597442196462687387260497677609844307","date":"2025-06-20T21:47:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233106125082942890321850014951093996230","date":"2025-06-20T07:39:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"302739544432355210260786931430956095985","date":"2025-06-20T04:04:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181299554267476030744193027659813677204","date":"2025-06-20T03:44:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-25T21:51:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"192760371585343452906687560888663666765","date":"2025-04-16T11:44:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T05:58:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-31T02:54:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Colorectal Disease","date":"2025-03-29T12:10:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-colorectal-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijcd","sideBox":"Learn more about [International Journal of Colorectal Disease](http://link.springer.com/journal/384)","snPcode":"384","submissionUrl":"https://submission.nature.com/new-submission/384/3","title":"International Journal of Colorectal Disease","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2eb15033-cc4f-4a56-a96c-f4ff2f94e286","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-25T16:35:11+00:00","versionOfRecord":{"articleIdentity":"rs-6066404","link":"https://doi.org/10.1007/s00384-025-04982-y","journal":{"identity":"international-journal-of-colorectal-disease","isVorOnly":false,"title":"International Journal of Colorectal Disease"},"publishedOn":"2025-08-19 16:29:25","publishedOnDateReadable":"August 19th, 2025"},"versionCreatedAt":"2025-04-03 10:57:31","video":"","vorDoi":"10.1007/s00384-025-04982-y","vorDoiUrl":"https://doi.org/10.1007/s00384-025-04982-y","workflowStages":[]},"version":"v1","identity":"rs-6066404","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6066404","identity":"rs-6066404","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}