Optimized Sentinel Node Detection in Endometrial Cancer: Intraoperative Indocyanine Green Mapping with Postoperative Bread-Loaf Slicing Ultrastaging

Optimized Sentinel Node Detection in Endometrial Cancer: Intraoperative Indocyanine Green Mapping with Postoperative Bread-Loaf Slicing Ultrastaging | 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 Optimized Sentinel Node Detection in Endometrial Cancer: Intraoperative Indocyanine Green Mapping with Postoperative Bread-Loaf Slicing Ultrastaging Yu-Sheng Huang, Hao Lin, Yu-Che Ou, Chao-Cheng Huang, Hung-Chun Fu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7661891/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Apr, 2026 Read the published version in World Journal of Surgical Oncology → Version 1 posted 13 You are reading this latest preprint version Abstract Background: Accurate lymph node assessment is crucial in early-stage endometrial cancer staging, but traditional lymphadenectomy carries significant morbidity risks. This study evaluates whether indocyanine green (ICG)-based sentinel lymph node (SLN) mapping combined with bread-loaf slicing ultrastaging optimizes lymph node metastasis detection in uterine-confined endometrial cancer. Methods: We retrospectively analyzed patients with early-stage endometrial cancer who underwent surgery with SLN mapping at Kaohsiung Chang Gung Memorial Hospital from November 2021 to December 2024. SLN mapping was performed using either ICG fluorescence imaging or patent blue dye during minimally invasive and open surgical approaches. All retrieved lymph nodes underwent ultrastaging examination using the bread-loaf slicing method. Results: Among 131 patients, the overall SLN mapping success rate was 93.9%, with bilateral detection rate achieved 95.9%. ICG demonstrated superior bilateral mapping success compared to patent blue dye (79.2% vs. 27.3%, p < 0.001). While minimally invasive surgery showed higher mapping rates than open surgery (79.1% vs. 43.8%, p = 0.002), ICG effectively mitigated this difference by maintaining high detection rates even in open surgical cases. Bread-loaf slicing ultrastaging identified lymph node metastases in 4.0% of patients. Zero recurrence occurred in pathological stage I patients during follow-up. Conclusions: The combination of intraoperative ICG-based SLN mapping with bread-loaf slicing ultrastaging represents an optimal strategy for lymph node assessment in early-stage endometrial cancer, achieving superior detection rates while minimizing surgical morbidity and maintaining oncologic safety. Endometrial Neoplasms Indocyanine Green Lymphatic Metastasis / pathology (Ultrastaging) Sentinel Lymph Node Figures Figure 1 Figure 2 Figure 3 Introduction Endometrial cancer (EC) is a significant public health concern, ranking as the fifth most common cancer among women in Taiwan and the sixth globally[ 1 , 2 ]. Its rising incidence underscores the importance of advancing diagnostic and treatment strategies to improve patient outcomes. Accurate staging is critical for guiding treatment and predicting prognosis, particularly in patients with early-stage disease[ 3 ]. Traditionally, pelvic lymph nodes dissection has been performed to assess lymph node metastases, yet this approach is associated with significant surgical morbidity without a clear survival benefit in early-stage, low-risk patients[ 4 , 5 ]. Notably, for early-stage uterine-confined EC, the risk of lymph node metastasis is relatively low, with reported rates of only 3.5% in uterine-confined low-grade endometrioid EC [ 6 ]. Sentinel lymph node (SLN) mapping has emerged as a minimally invasive alternative to full lymphadenectomy, offering reduced surgical morbidity while maintaining diagnostic accuracy, particularly when combined with ultrastaging techniques that enhance the detection of micrometastases often missed by conventional pathology [ 7 ]. Various microscopic slicing methods have been employed in different protocols for lymph node analysis[ 8 ]. The bread-loaf slicing method involves cutting the lymph node perpendicular to its longest axis, whereas longitudinal slicing involves sectioning along the equatorial plane. Studies suggest that bread-loaf slicing yields a higher metastasis detection rate compared to longitudinal slicing, yet no universally accepted standard for lymph node slicing exists, leading to significant variations in practice across institutions[ 9 ]. Indocyanine green (ICG) fluorescence imaging has become widely adopted for intraoperative SLN mapping, offering high detection rates and enabling precise lymphatic mapping during minimally invasive surgery (MIS)[ 10 , 11 ]. Collectively, differences in SLN mapping approaches and ultrastaging protocols may influence metastasis detection rates and impact the overall accuracy of disease staging. Establishing standardized methodologies could enhance the reliability and reproducibility of SLN-based strategies in early-stage EC management. This study aims to evaluate the clinical and oncologic outcomes of combining the two most reliable components of SLN mapping in uterine-confined EC: ICG-based mapping and postoperative ultrastaging using the bread-loaf slicing method. By assessing SLN detection rates and the efficacy of this combined approach, we seek to determine whether implementing these established best practices can optimize lymph node metastasis detection while minimizing surgical morbidity in early-stage EC patients, ultimately providing evidence for standardizing nodal assessment strategies. Materials and methods Study design and patient selection This retrospective cohort study was conducted at Kaohsiung Chang Gung Memorial Hospital and included patients diagnosed with EC between November 2021 and December 2024. Eligible patients were those with a confirmed diagnosis of EC who underwent primary staging surgery incorporating SLN mapping, followed by post-operative pathological ultrastaging. Cases were excluded if they did not undergo primary staging surgery or if they underwent primary staging surgery without SLN assessment and mapping. The complete study workflow is illustrated in Fig. 1 . Demographic and clinicopathological data, including age, body mass index (BMI), tumor size, stage, histological subtype, and mismatch repair (MMR) status, were extracted from electronic medical records for analysis. This study was approved by the Institutional Review Board of Chang Gung Memorial Hospital, Taiwan (approval number: 202500410B0) and patient confidentiality was strictly maintained, and informed consent was waived due to the retrospective nature of the study. Surgical Procedure and SLN Mapping Protocol Surgical Procedure and SLN Mapping Protocol All surgeries were performed by experienced gynecologic oncologic surgeons. The decision to perform SLN mapping was at the discretion of the attending surgeon, based on individual patient factors, tumor characteristics, and surgical considerations. As shown in Fig. 2 (a) to (c), after appropriate patient positioning, the tracer agent was injected into the cervix using a 1 mL syringe. For patients receiving ICG, the tracer was administered into two quadrants (3 and 9 o’clock positions), while for those receiving patent blue, it was injected into four quadrants (12, 3, 6, and 9 o’clock positions). The concentrations of ICG and patent blue were 2.5 mg/mL and 25 mg/mL, respectively, with 1 mL of the tracer agent injected per quadrant, consisting of 0.5 mL superficial (1–3 mm) and 0.5 mL deep (1–2 cm) cervical injections. After a waiting period of 10 to 15 minutes, bilateral pelvic lymphatic tract visualization and SLNs identification were assessed. If one side failed to show tracer uptake, an additional injection was given to that side. If both sides failed, bilateral reinjections were performed. After repeat injection, if a SLN was still not identified on a particular side, it was classified as a mapping failure, necessitating complete lymph node dissection on that side. Additionally, any lymph nodes suspected of metastasis during the procedure were removed, regardless of their mapping status. The harvested lymph nodes were labeled as SLNs or non-SLNs and sent for pathological examination. Success mapping refers to the intraoperative detection of SLNs. Overall success mapping requires SLN detection on at least one side, while bilateral success mapping requires detection on both sides. Pathological Examination and Outcome Measures Pathological Examination and Outcome Measures SLNs were classified according to size by the pathologist: the size smaller than 4 mm were directly placed into cassettes and submitted for sectioning; the size between 4–8 mm were bisected and all submitted for sectioning, and those larger than 8 mm were sliced perpendicular to the long axis to be 2–4 mm in thickness each by bread-loaf slicing method and all submitted for sectioning. The first section was stained using hematoxylin and eosin (H&E). If no definite tumor cells were identified on the initial H&E section, two subsequent 5-µm sections were cut from a second level, about 0.5 mm apart from the first section. One of these sections was stained with H&E, and the other was submitted to immunohistochemistry (IHC) using the anti-cytokeratin AE1/AE3 antibody (clone: AE1/AE3, 1:300, Genemed, South San Francisco). Non-SLNs were processed and examined according to the standard pathological protocol for lymph node evaluation (Fig. 2 (d) to (f)). Detection rates refer to the proportion of lymph nodes identified through mapping that are confirmed to be true lymph nodes upon pathological examination. Statistical Analysis Statistical analysis was performed to compare the factors influencing bilateral SLNs success mapping. Continuous variables were compared using the Mann-Whitney U test and presented as medians with ranges. Categorical variables were analyzed using either the chi-squared test or Fisher’s exact test, depending on the expected frequencies, and were expressed as frequencies and percentages. All statistical analyses were performed using SPSS software for Windows, version 26 (IBM, Armonk, NY, USA). A p -value < 0.05 was considered statistically significant. Results Patient Demographics and Tumor Characteristics A total of 488 patients were diagnosed with EC between November 2021 to December 2024, of which 131 uterine-confined cases underwent primary staging surgery with intraoperative SLN assessment and mapping. As shown in Table 1 A, most patients were diagnosed at an early clinical stage, with 76.3% (n = 100) classified as FIGO stage IA and 22.9% (n = 30) as stage IB with only one case as stage II. Postoperative pathologic evaluation confirmed stage I disease in 88.5% of cases (n = 116), while the remaining 11.4% (n = 15) were diagnosed with more advanced stages. Notably, two cases were initially staged as IA but were later found to have stage IVB disease postoperatively—one due to omental metastasis and the other due to lung metastasis. Histopathological analysis revealed endometrioid adenocarcinoma as the predominant subtype (90.8%, n = 119), with serous, clear cell, carcinosarcoma, and mixed histology accounting for the remaining cases. Low-grade histology was identified in 85.5% (n = 112) of patients. Medium tumor size was 25 mm (range from 1-108 mm). Most tumors demonstrated positive estrogen receptor (ER) and progesterone receptor (PR) expression, with ER positivity in 87.0% (n = 114) and PR positivity in 75.6% (n = 99) of cases. Molecular profiling revealed MMR deficiency in 26.7% of tumors (n = 35), while 61.1% (n = 80) demonstrated MMR proficiency. Surgical Approach and SLN Mapping Results Table 1 B showed analysis of surgical characteristics revealing that MIS was the preferred approach, with 47.3% (n = 62) undergoing laparoscopic surgery and 40.5% (n = 53) undergoing robotic-assisted surgery, while only 12.2% (n = 16) underwent open surgery. SLN mapping was predominantly performed using ICG (91.6%, n = 120), with patent blue dye used in 8.4% (n = 11). The overall SLN mapping success rate was 93.9%, with bilateral success in 74.8% of cases. Regarding SLN mapping sites, in the right hemipelvis, single-site mapping was achieved in 57.3% of cases, while multiple sites were identified in 24.4%. The most common SLN locations in the right hemipelvis were the internal iliac (29.3%), external iliac (28.0%), and obturator (24.0%) areas. In the left hemipelvis, SLNs were most frequently identified in the external iliac (36.1%), obturator (26.5%), and internal iliac (26.5%) regions, with single-site mapping being more common (63.4%) compared to multiple sites (19.8%). The overall detection rates of SLN were impressive at 95.9%, with bilateral detection reaching 87.8%. Factors Influencing Bilateral SLN Mapping Success Table 2 presents an analysis of factors influencing bilateral SLN mapping success. Across the entire cohort (N = 131) and the ICG subgroup (N = 120), age and preoperative CA-125 levels did not significantly differ between patients with bilateral mapping failure and those with successful mapping. Obesity, defined as BMI ≥ 30kg/m², was also not significantly associated with mapping success among entire cohort and the ICG subgroup. The surgical route was significantly related to bilateral mapping outcomes observed in the entire cohort; patients who underwent laparotomy had a higher failure rate compared to those who underwent MIS (56.2% vs. 20.9%, p = 0.002). However, this association was not observed within the ICG-only subgroup (28.6% vs. 20.4%, p = 0.635). Additionally, the choice of tracer agent significantly impacted mapping success, with patent blue dye associated with a higher failure rate compared to ICG (72.7% vs. 20.8%, p < 0.001). Other clinical and pathological factors, including histologic subtype (endometrioid versus non-endometrioid), tumor size, depth of myometrial invasion, and H-score of ER and PR, were not statistically significantly different. Table 1 Clinicopathological and Surgical Characteristics of Endometrial Cancer Patients Undergoing Sentinel Lymph Node Mapping A. Clinicopathological Characteristics B. Surgical Approaches, SLNs Mapping Results, and Detection Characteristics Patients (N = 131) Patients (N = 131) Median age (year), range 56 (22–80) Surgical route Median BMI (kg/m 2 ), range 26.3 (18.0-40.2) Laparotomy 16 (12.2%) < 30 kg/m 2 95 (72.5%) Laparoscopy 62 (47.3%) ≧ 30 kg/m 2 (Obesity) 36 (27.5%) Robotic surgery 53 (40.5%) Median parity, range 2 (0–6) Tracer agent 0 43 (32.8%) Patent blue 11 (8.4%) ≧ 1 88 (67.2%) Indocyanine green (ICG) 120 (91.6%) Median preoperative CA-125 (U/mL), range 17.5 (4.5-132.7) Mapping results Preoperative clinical FIGO stage Overall success 123 (93.9%) ⅠA 100 (76.3%) Bilateral success 98 (74.8%) ⅠB 30 (22.9%) Unilateral success Ⅱ 1 (0.8%) Right hemipelvis 11 (8.4%) Postoperative pathologic FIGO stage Left hemipelvis 14 (10.7%) ⅠA 95 (72.5%) Bilateral failure 8 (6.1%) ⅠB 21 (16.0) Mapping area Ⅱ 7 (5.3%) Right hemipelvis ⅢC1 6 (4.6%) Single site 75 (57.3%) ⅣB 2 (1.5%) Multiple sites 32 (24.4%) Histology Failure 22 (16.8%) Endometrioid 119 (90.8%) Unknown 2 (1.5%) Serous 6 (4.6%) Left hemipelvis Clear cell 1 (0.8%) Single site 83 (63.4%) Carcinosarcoma 2 (1.5%) Multiple sites 26 (19.8%) Mixed endometrioid / clear cell 2 (1.5%) Failure 19 (14.5%) EIN 1 (0.8%) Unknown 3 (2.3%) Grade a Location of SLNs in single site Low-grade 112 (85.5%) Right hemipelvis (n = 75) High-grade 16 (12.2%) Obturator area 18 (24.0%) Not applicable 3 (2.3%) External iliac area 21 (28.0%) Medium Tumor size (mm), range 25 (1-108) Internal iliac area 22 (29.3%) < 20 mm 42 (32.1%) Common iliac area 10 (13.3%) ≧ 20 mm 78 (59.5%) Presacral area 3 (4.0%) No residual 11 (8.4%) Parametrical area 1 (1.3%) Depth of myometrium invasion Paraaortic area 0 < 50% 104 (79.4%) Left hemipelvis (n = 83) ≧ 50% 27 (20.6%) Obturator area 22 (26.5%) LVSI External iliac area 30 (36.1%) Negative 108 (82.4%) Internal iliac area 22 (26.5%) Focal 12 (9.2%) Common iliac area 9 (10.8%) Substantial 11 (8.4%) Presacral area 0 MMR status Parametrical area 0 Proficient 80 (61.1%) Paraaortic area 0 Deficient 35 (26.7%) Number of removed SLNs Unknown b 16 (12.2%) Right hemipelvis 2 (1–13) ER Left hemipelvis 2 (1–22) Positive 114 (87.0%) Detection rate of SLNs (%) H-score 160 (0-285) Overall 95.9% PR Bilateral 87.8% Positive 99 (75.6%) Right 94.5% H-score 150 (0-288) Left 90.2% BMI, body mass index; CA-125, carbohydrate antigen-125; SLNs, sentinel lymph nodes; LVSI, lymphovascular space invasion; EIN, endometrial intraepithelial neoplasia; MMR, mismatch repair; ER, estrogen receptor; PR, progesterone receptor. a. Low-grade: endometrioid grade 1–2; High-grade: endometrioid grade 3, serous, clear cell, and carcinosarcoma; Not applicable: mixed histology and endometrial intraepithelial neoplasia. b. Unknown MMR status: 9 cases with no residual tumor after surgery (6 diagnosed externally, 3 internally) and 7 cases without MMR documentation Table 2 Analysis of Factors Influencing Sentinel Lymph Node Bilateral Success Mapping, Including ICG Subgroup Patients with bilateral success mapping All patients (N = 131) p-value Patients with ICG as tracer agent (N = 120) p-value Failure (n = 33, 25.2%) Success (n = 98, 74.8%) Failure (n = 25, 20.8%) Success (n = 95, 79.2%) Age (year) 57 (29 ~ 75) 55 (22 ~ 80) 0.994 57 (31 ~ 75) 55 (22 ~ 80) 0.630 Preoperative CA-125 (U/mL) 17.5 (6.1 ~ 63.2) 17.6 (4.5 ~ 132.7) 0.672 15.9 (6.1 ~ 63.2) 17.5 (4.5 ~ 132.7) 0.348 Obesity 0.186 0.498 BMI < 30 kg/m 2 21 (22.1%) 74 (77.9%) 17 (19.3%) 71 (80.7%) BMI ≧ 30 kg/m 2 12 (33.3%) 24 (66.7%) 8 (25.0%) 24 (75.0%) Surgical route 0.002 0.635 Laparotomy 9 (56.2%) 7 (43.8%) 2 (28.6%) 5 (71.4%) MIS 24 (20.9%) 91 (79.1%) 23 (20.4%) 90 (79.6%) Tracer agent < 0.001 Patent blue 8 (72.7%) 3 (27.3%) ICG 25 (20.8%) 95 (79.2%) Histology 1.000 1.000 Non-endometrioid 3 (25.0%) 9 (75.0%) 2 (18.2%) 9 (76.5%) Endometrioid 30 (25.2%) 89 (74.8%) 23 (21.1%) 86 (78.9%) Tumor size 0.074 0.406 < 20 mm 9 (17.0%) 44 (83.0%) 9 (17.3%) 43 (82.7%) ≧ 20 mm 24 (30.8%) 54 (69.2%) 16 (23.5%) 52 (76.5%) Myoinvasion 0.921 1.000 < 50% 26 (25.0%) 78 (75.0%) 20 (20.8%) 76 (79.2%) ≧ 50% 7 (26.0%) 20 (74.0%) 5 (20.8%) 19 (79.2%) LVSI 0.467 0.238 No or focal 29 (24.2%) 91 (75.8%) 21 (19.3%) 88 (80.7%) Substantial 4 (36.4%) 7 (63.6%) 4 (36.4%) 7 (63.6%) ER H-score 150 (0 ~ 285) 160 (0 ~ 285) 0.655 180 (0 ~ 285) 160 (0 ~ 285) 0.204 PR H- score 150 (0 ~ 288) 150 (0 ~ 285) 0.789 150 (0 ~ 288) 150 (0 ~ 285) 0.840 CA-125, carbohydrate antigen-125; BMI, body mass index; MIS, minimally invasive surgery; ICG, indocyanine green; LVSI, lymphovascular space invasion; ER, estrogen receptor; PR, progesterone receptor. Characteristics of Patients with Lymph Node Metastases Among the study population, eight cases (6.1%) were pathologically identified with positive lymph nodes (Table 3 ). Two cases (25%) were classified as pathologic stage IB with isolated tumor cells (ITCs) in observed SLNs, and the remaining 6 cases (75%) were classified as stage IIIC1 due to micrometastasis or macrometastasis of SLNs. Most tumors (n = 7, 87.5%) were low-grade endometrioid carcinoma, except for one case of serous carcinoma. Median tumor size was 41mm (range from 18-108mm), with most cases (n = 6, 75%) exhibited deep myometrial invasion (myoinvasion ≧ 50%). Figure 3 shows a representative case of ITCs identified within a SLN, confirmed by positive AE1/AE3 immunohistochemical staining. Table 3 Clinicopathologic Features of Patients with Lymph Node Metastases Case Age (years) Preoperative Clinical FIGO stage Postoperative pathologic FIGO stage Histology a Grade Tumor size (mm) MI depth (%) LVSI Area of node-positive Metastasis status b 1 46 ⅠB ⅠB E 2 56 ≧ 50 Focal SLN ITCs 2 61 ⅠB ⅠB E 2 45 ≧ 50 Focal SLN ITCs 3 58 ⅠA ⅢC1 E 2 18 ≧ 50 Substantial SLN m 4 58 ⅠB ⅢC1 E 1 108 ≧ 50 Negative Non-SLN m 5 65 ⅠB ⅢC1 E 1 37 ≧ 50 Substantial SLN m 6 52 ⅠB ⅢC1 E 2 55 < 50 Focal Non-SLN M 7 62 ⅠA ⅢC1 S High-grade 13 < 50 Negative Non-SLN M 8 57 ⅠA ⅢC1 E 1 22 ≧ 50 Substantial SLN m Non-SLN M LVSI, lymphovascular space invasion; SLNs, sentinel lymph nodes; MI, myometrium invasion. a. E: endometrioid; S: serous b. ITCs: isolated tumor cells; m: micrometastasis; M: macrometastasis Discussion The main findings of this study revealed that postoperative lymph node ultrastaging with the bread-loaf slicing method, following intraoperative SLN mapping with ICG tracer dye, optimizes the detection of lymph node metastases in uterine-confined EC. Furthermore, the use of ICG as a tracer agent mitigates common factors contributing to mapping failure and significantly improves SLN detection rates, even in open surgery cases. Impact of Surgical Approach and ICG Fluorescence on SLN mapping The efficacy of MIS, particularly when combined with ICG fluorescence imaging, has been demonstrated in SLN mapping for EC[ 12 , 13 ]. MIS was utilized in 87.8% of our cohort, reinforcing its well-established benefits in reducing morbidity while maintaining oncologic outcomes. Our study achieved a high SLN success mapping rate, with an overall success rate of 95.9% and a bilateral success rate of 74.8%. Compared to the FIRES trial, which reported an overall mapping success rate of 86% and a bilateral success rate of 52%, our findings suggest improved SLN mapping efficiency with the incorporation of standardized techniques[ 14 ]. Compared with patent blue, the use of ICG significantly improved bilateral SLN mapping success (27.3% vs. 79.2%, p < 0.001), reinforcing its superiority as the preferred tracer for SLN detection. These findings are consistent with the FILM trial, which reported that ICG with near-infrared fluorescence imaging identified more SLNs than isosulfan blue dye in uterine cancers[ 10 ]. Eriksson et al. also demonstrated that ICG improves SLN detection, even in obese patients, compared to blue dye[ 15 ]. Additionally, our study confirmed that MIS was associated with significantly higher SLN detection rates than open surgery (79.1% vs. 43.8%, p = 0.002), consistent with findings from the LAP2 and SENTI-ENDO trials[ 13 , 16 ]. However, within the subgroup of patients who received ICG as the tracer agent, no statistically significant difference in success rates was observed between MIS and laparotomy (79.6% vs. 71.4%, p = 0.635). This finding suggests that while MIS generally enhances mapping success, the use of ICG as a tracer may mitigate differences in outcomes between surgical approaches, potentially due to the superior efficacy of ICG in facilitating lymphatic mapping regardless of surgical technique. Taken together, these findings strongly support the routine use of ICG to optimize SLN detection and ensure accurate staging Clinical and Pathological Factors Affecting SLN Mapping Success The SAGE multicenter study by Cianci et al. identified advanced age and higher BMI as independent risk factors for bilateral SLN mapping failure in EC, attributing these associations to age-related lymphatic capillary rarefaction and the technical challenges posed by obesity[ 17 ]. In contrast, our retrospective cohort analysis found that neither age nor BMI was significantly associated with bilateral SLN mapping success—both in the overall cohort ( p = 0.994 and p = 0.186, respectively) and in the ICG subgroup ( p = 0.630 and p = 0.498, respectively). This discrepancy may be explained by differences in sample size and age distribution, as our study population was smaller and had a younger median age (56 years) compared to the SAGE cohort (64 years). Furthermore, the predominant use of ICG as the tracer and adherence to standardized mapping protocols in our study may have minimized the impact of age and BMI on mapping outcomes. Other contributing factors may include high surgeon experience and a lower proportion of patients with extreme obesity, which could have enhanced mapping success in our cohort. Cianci et al. also identified LVSI as a significant negative predictor of successful SLN mapping, suggesting that the presence of LVSI may disrupt effective tracer migration through lymphatic channels, thereby reducing mapping success[ 17 ]. However, in our cohort, LVSI was not significantly associated with SLN detection rates. This discrepancy may reflect differences in methodology, such as the predominant use of ICG as a tracer in our study, or variations in patient populations, including a higher proportion of early-stage and low-grade tumors, which could diminish the impact of LVSI on mapping outcomes. Additionally, our definition of LVSI positivity was limited to cases of substantial LVSI, excluding focal involvement, which may differ from the criteria used by Cianci et al. and thus contribute to the observed differences. Anatomical Distribution of SLNs Regarding SLN detection rates, we observed an overall detection rate of 95.9%, with detection rates of 94.5% in the right hemipelvis and 90.2% in the left hemipelvis. These results are consistent with the SENTI-ENDO trial, which reported SLN detection rates of 89% (overall), 77% (right), and 76% (left)[ 16 ]. Recent studies have demonstrated consistently high SLN detection rates in EC. Fierro et al. reported an overall detection rate of 96.7% using ICG, with 97.3% in laparoscopic surgery and 95.5% in robotic surgery, supporting the reliability of SLN mapping across different minimally invasive surgical approaches[ 18 ]. These findings are consistent with meta-analyses by Lin et al. showing bilateral detection rates of 82–86% for both laparoscopic and robotic approaches when ICG is used as the tracer[ 19 ]. Additionally, SLNs were most frequently identified in the external iliac, obturator, and internal iliac regions, whereas para-aortic SLN detection was rare, aligning with previous literature. Recent data from studies that performed SLN mapping with reference lymphadenectomy has shown the risk of isolated para-aortic metastasis to be as low as 0.8-1%[ 12 , 14 ]. These findings further support that the lower paracervical pathway is the predominant drainage route, with the majority of mapped SLNs located in the pelvic region, while para-aortic SLN detection remains limited (~ 5–10%)[ 16 ]. The localization of SLNs in the pelvic basin further supports SLN mapping as a reliable alternative to full lymphadenectomy, particularly for low- and intermediate-risk EC patients. Bread-loaf Slicing Ultrastaging Protocol and Its Advantages Several methods were employed for slicing lymph nodes during ultrastaging in EC, each with distinct protocols, advantages, and limitations. Commonly used macroscopic slicing techniques include longitudinal slicing section and bread-loaf slicing section[ 8 ]. Longitudinal sectioning, on the other hand, slices the lymph node along its longest axis, which is technically simpler, faster to perform, and preserves nodal architecture[ 20 ]. Bread-loaf slicing involves cutting the lymph node into multiple thin sections, typically 2–3 mm thick, perpendicular to the long axis. This approach ensures comprehensive sampling of the entire node, increasing the likelihood of detecting low-volume metastases, including micrometastases and ITCs[ 20 , 21 ]. In our study, bread-loaf slicing was adopted as the ultrastaging protocol for SLN assessment, following the methodology of the FIRES trial[ 14 ]. Specifically, SLNs larger than 8 mm were sliced perpendicular to the long axis into 2–4 mm sections, with subsequent histological and immunohistochemical evaluation. This protocol was chosen based on robust evidence from a systematic review by Burg et al., which demonstrated that bread-loaf slicing achieved a significantly higher SLN metastasis detection rate (53%) compared to longitudinal slicing (33%, p < 0.05)[ 9 ]. The enhanced sensitivity of bread-loaf slicing is attributed to its cross-sectional approach, which increases the probability of detecting metastatic deposits that might be missed with longitudinal slicing. Furthermore, the review found that increasing the number of IHC slides did not significantly improve detection rates, highlighting that the slicing method itself is the most influential factor in ultrastaging sensitivity. Despite the absence of standardized international guidelines for SLN ultrastaging, our findings align with previous studies supporting the bread-loaf slicing technique as an optimal approach. Given its higher diagnostic accuracy, our study further supports the need for standardizing SLN ultrastaging with the bread-loaf technique to optimize metastatic detection and improve staging accuracy. Comparison of Lymph Node Metastasis Detection Rates and Clinical Outcomes According to Katsoulakis et al., an analysis using the SEER registry evaluated the incidence of lymph node metastasis in clinical stage I, low-grade, endometrioid EC and reported a 3.5% risk of pelvic lymph node metastasis based on systematic pelvic lymphadenectomy[ 22 ]. Similarly, a recent study reported by Praiss et al. using pelvic lymphadenectomy for nodal assessment found a comparable incidence of 4.0% LN metastasis in low-grade, stage I ECs, further confirming the low but notable risk of nodal involvement[ 6 ]. In our study, we employed a different methodology, utilizing intraoperative ICG for SLN mapping and postoperative ultrastaging with the bread-loaf slicing method, yielding a comparable lymph node metastasis detection rate of 4.0%. This suggests that our combined approach of ICG dye for mapping and bread-loaf slicing for ultrastaging may detect micrometastases missed by conventional lymphadenectomy, highlighting the diagnostic advantage of this method while reducing surgical morbidity in early-stage, low-risk EC patients. As of this manuscript submission, none of the patients diagnosed with pathological stage I disease—whether through successful SLN mapping or conventional lymph node dissection following mapping failure—have experienced regional or distant recurrences. This suggests a favorable prognosis for patients treated at an early stage. We will maintain ongoing surveillance to ensure early detection and timely intervention if needed. Limitations This study has several important limitations that should be acknowledged. First, its retrospective design inherently introduces selection bias and limits the ability to control for confounding variables that may influence SLN detection rates and metastasis identification. Second, as a single-institution study, the findings may not be fully generalizable to other clinical settings with different patient populations and levels of surgical expertise. Lastly, the study lacked a direct comparison group using longitudinal slicing within the same patient population, which would have provided stronger evidence for the superiority of the bread-loaf technique. Despite these limitations, this study offers valuable insights into optimizing lymph node metastasis detection in uterine-confined EC. Addressing these methodological issues in future prospective, multi-institutional research could enhance the reliability and clinical applicability of the findings. Conclusions This study demonstrates that SLN mapping with ICG fluorescence imaging combined with bread-loaf slicing ultrastaging optimizes lymph node assessment in early-stage EC. Our approach achieved superior detection rates (95.9% overall, 74.8% bilateral) compared to previous studies, with ICG significantly outperforming blue dye. The combined methodology enhanced micrometastasis detection while maintaining oncologic safety, as evidenced by zero recurrences in pathological stage I patients to date. These findings support this combined approach as an effective strategy that balances accurate staging with reduced surgical morbidity in EC management. Declarations Ethics approval and consent to participate: This study was approved by the Institutional Review Board of Chang Gung Memorial Hospital, Taiwan (approval number: 202500410B0) and patient informed consent was waived due to the retrospective nature of the study. Consent for publication: All data and materials used in this study were obtained from the electronic medical records of Chang Gung Memorial Hospital, Taiwan. Funding: The research described in this study was conducted without any external funding. Author Contribution YS performed all the experiments and drafted the manuscript. H and CH provided the conception and design of study. HC, YW, and YY provided the acquisition of data. SW contributed to the data analysis and interpretation. YC, CC, and CH revised the manuscript critically for important intellectual content. All authors read and approved the final version of the manuscript. Acknowledgements: Not applicable Availability of data and materials: Not applicable Competing interests: The authors declare that they have no competing interests. References Health Promotion Administration Ministry Of Health And Welfare Taiwan. Cancer Registry Annual Report. 2022 Taiwan. 2024. World Cancer Research Fund. Endometrial Cancer Statistics: World Cancer Research Fund; [Available from: https://www.wcrf.org/preventing-cancer/cancer-statistics/endometrial-cancer-statistics/#endometrial-cancer-incidence-cases Dobrzycka B, Terlikowska KM, Kowalczuk O, Niklinski J, Kinalski M, Terlikowski SJ. Prognosis of Stage I Endometrial Cancer According to the FIGO 2023 Classification Taking into Account Molecular Changes. Cancers (Basel). 2024;16(2). Frost JA, Webster KE, Bryant A, Morrison J. Lymphadenectomy for the management of endometrial cancer. Cochrane Database Syst Rev. 2017;10(10):Cd007585. Kitchener H, Swart AM, Qian Q, Amos C, Parmar MK. Efficacy of systematic pelvic lymphadenectomy in endometrial cancer (MRC ASTEC trial): a randomised study. Lancet. 2009;373(9658):125–36. Praiss AM, Huang Y, St Clair CM, Tergas AI, Melamed A, Khoury-Collado F, et al. A modern assessment of the surgical pathologic spread and nodal dissemination of endometrial cancer. Gynecol Oncol. 2020;157(2):329–34. Kim CH, Soslow RA, Park KJ, Barber EL, Khoury-Collado F, Barlin JN, et al. Pathologic ultrastaging improves micrometastasis detection in sentinel lymph nodes during endometrial cancer staging. Int J Gynecol Cancer. 2013;23(5):964–70. Rau TT, Deppeler MV, Christe L, Siegenthaler F, Imboden S, Papadia A, et al. Pathological processing of sentinel lymph nodes in endometrial carcinoma - routine aspects of grossing, ultra-staging, and surgico-pathological parameters in a series of 833 lymph nodes. Virchows Arch. 2022;481(3):421–32. Burg LC, 't Hengeveld EM. Hout J, Bulten J, Bult P, Zusterzeel PLM. Ultrastaging methods of sentinel lymph nodes in endometrial cancer - a systematic review. Int J Gynecol Cancer. 2021;31(5):744 – 53. Frumovitz M, Plante M, Lee PS, Sandadi S, Lilja JF, Escobar PF, et al. Near-infrared fluorescence for detection of sentinel lymph nodes in women with cervical and uterine cancers (FILM): a randomised, phase 3, multicentre, non-inferiority trial. Lancet Oncol. 2018;19(10):1394–403. Buda A, Crivellaro C, Elisei F, Di Martino G, Guerra L, De Ponti E, et al. Impact of Indocyanine Green for Sentinel Lymph Node Mapping in Early Stage Endometrial and Cervical Cancer: Comparison with Conventional Radiotracer (99m)Tc and/or Blue Dye. Ann Surg Oncol. 2016;23(7):2183–91. Persson J, Salehi S, Bollino M, Lönnerfors C, Falconer H, Geppert B. Pelvic Sentinel lymph node detection in High-Risk Endometrial Cancer (SHREC-trial)-the final step towards a paradigm shift in surgical staging. Eur J Cancer. 2019;116:77–85. Walker JL, Piedmonte MR, Spirtos NM, Eisenkop SM, Schlaerth JB, Mannel RS, et al. Recurrence and survival after random assignment to laparoscopy versus laparotomy for comprehensive surgical staging of uterine cancer: Gynecologic Oncology Group LAP2 Study. J Clin Oncol. 2012;30(7):695–700. Rossi EC, Kowalski LD, Scalici J, Cantrell L, Schuler K, Hanna RK, et al. A comparison of sentinel lymph node biopsy to lymphadenectomy for endometrial cancer staging (FIRES trial): a multicentre, prospective, cohort study. Lancet Oncol. 2017;18(3):384–92. Eriksson AG, Montovano M, Beavis A, Soslow RA, Zhou Q, Abu-Rustum NR, et al. Impact of Obesity on Sentinel Lymph Node Mapping in Patients with Newly Diagnosed Uterine Cancer Undergoing Robotic Surgery. Ann Surg Oncol. 2016;23(8):2522–8. Ballester M, Dubernard G, Lecuru F, Heitz D, Mathevet P, Marret H, et al. Detection rate and diagnostic accuracy of sentinel-node biopsy in early stage endometrial cancer: a prospective multicentre study (SENTI-ENDO). Lancet Oncol. 2011;12(5):469–76. Cianci S, Rosati A, Vargiu V, Capozzi VA, Sozzi G, Gioè A, et al. Sentinel Lymph Node in Aged Endometrial Cancer Patients The SAGE Study: A Multicenter Experience. Front Oncol. 2021;11:737096. Fierro A, Flores I, Pellicer I, Alonso-Espias M, Garcia-Pineda V, Zapardiel I et al. Route of Surgery for Sentinel Node Biopsy in Endometrial Cancer: Laparoscopy Versus Robotics. J Clin Med. 2025;14(12). Lin H, Ding Z, Kota VG, Zhang X, Zhou J. Sentinel lymph node mapping in endometrial cancer: a systematic review and meta-analysis. Oncotarget. 2017;8(28):46601–10. Dheur A, Kakkos A, Danthine D, Delbecque K, Goffin F, Gonne E, et al. Lymph node assessment in cervical cancer: current approaches. Front Oncol. 2024;14:1435532. Dundr P, Cibula D, Němejcová K, Tichá I, Bártů M, Jakša R. Pathologic Protocols for Sentinel Lymph Nodes Ultrastaging in Cervical Cancer. Arch Pathol Lab Med. 2019. Katsoulakis E, Mattes MD, Rineer JM, Nabhani T, Mourad WF, Choi K, et al. Contemporary analysis of pelvic and para-aortic metastasis in endometrial cancer using the SEER registry. Int J Gynaecol Obstet. 2014;127(3):293–6. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Apr, 2026 Read the published version in World Journal of Surgical Oncology → Version 1 posted Editorial decision: Revision requested 13 Nov, 2025 Reviews received at journal 02 Nov, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviews received at journal 19 Oct, 2025 Reviews received at journal 05 Oct, 2025 Reviewers agreed at journal 01 Oct, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviewers invited by journal 23 Sep, 2025 Editor assigned by journal 22 Sep, 2025 Submission checks completed at journal 21 Sep, 2025 First submitted to journal 19 Sep, 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-7661891","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524969477,"identity":"a442345b-1242-4260-9986-161387a417bf","order_by":0,"name":"Yu-Sheng Huang","email":"","orcid":"","institution":"Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yu-Sheng","middleName":"","lastName":"Huang","suffix":""},{"id":524969478,"identity":"1c3872dc-48fa-4327-854e-1ba28401cb2e","order_by":1,"name":"Hao Lin","email":"","orcid":"","institution":"Kaohsiung 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1","display":"","copyAsset":false,"role":"figure","size":302251,"visible":true,"origin":"","legend":"\u003cp\u003eStudy selection workflow\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7661891/v1/adc5ffedadc5a65bd0edb311.jpg"},{"id":92857246,"identity":"a735eb19-348e-4fbc-9b7a-ef31cd7cb9b1","added_by":"auto","created_at":"2025-10-06 11:42:24","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":679856,"visible":true,"origin":"","legend":"\u003cp\u003eIntraoperative Sentinel Lymph Node Mapping Procedure Using Tracer Agents\u003c/p\u003e\n\u003cp\u003e(a) Cervical injection of ICG (2.5 mg/mL) into two quadrants or patent blue (25 mg/mL) into four quadrants, administered 10-15 minutes before mapping.\u003c/p\u003e\n\u003cp\u003e(b) Intraoperative visualization using near-infrared fluorescence.\u003c/p\u003e\n\u003cp\u003e(c) Mapping algorithm: successful mapping → SLN sampling; failed mapping → reinjection; persistent failure → complete lymph node dissection.\u003c/p\u003e\n\u003cp\u003e(d) SLN categorization by size: \u0026lt;4 mm (whole), 4-8 mm (bisected), ≥8 mm (bread-loaf slicing at 2-4 mm intervals).\u003c/p\u003e\n\u003cp\u003e(e) Initial H\u0026amp;E staining for evaluation.\u003c/p\u003e\n\u003cp\u003e(f) If H\u0026amp;E negative, two 5-µm sections taken 0.05 cm apart: one H\u0026amp;E, one AE1/AE3 IHC.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7661891/v1/adef994489e138f60d4728b1.jpg"},{"id":92857255,"identity":"0430be88-fc63-4984-8e29-01b95e19da36","added_by":"auto","created_at":"2025-10-06 11:42:24","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3748094,"visible":true,"origin":"","legend":"\u003cp\u003eA representative case of isolated tumor cells identified within a sentinel lymph node. (a) H\u0026amp;E stain (x100) (b) H\u0026amp;E stain (x400) (c) Positive AE1/AE3 immunostaining (x400).\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7661891/v1/c816451829f97f08f9b764c7.jpg"},{"id":108437683,"identity":"105043e8-dc21-4d0c-baa3-f5cd56d9b06c","added_by":"auto","created_at":"2026-05-04 16:02:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5383970,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7661891/v1/d5b3f361-122b-401f-ac2a-37612b0d35f4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimized Sentinel Node Detection in Endometrial Cancer: Intraoperative Indocyanine Green Mapping with Postoperative Bread-Loaf Slicing Ultrastaging","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometrial cancer (EC) is a significant public health concern, ranking as the fifth most common cancer among women in Taiwan and the sixth globally[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Its rising incidence underscores the importance of advancing diagnostic and treatment strategies to improve patient outcomes. Accurate staging is critical for guiding treatment and predicting prognosis, particularly in patients with early-stage disease[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Traditionally, pelvic lymph nodes dissection has been performed to assess lymph node metastases, yet this approach is associated with significant surgical morbidity without a clear survival benefit in early-stage, low-risk patients[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Notably, for early-stage uterine-confined EC, the risk of lymph node metastasis is relatively low, with reported rates of only 3.5% in uterine-confined low-grade endometrioid EC [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSentinel lymph node (SLN) mapping has emerged as a minimally invasive alternative to full lymphadenectomy, offering reduced surgical morbidity while maintaining diagnostic accuracy, particularly when combined with ultrastaging techniques that enhance the detection of micrometastases often missed by conventional pathology [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Various microscopic slicing methods have been employed in different protocols for lymph node analysis[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The bread-loaf slicing method involves cutting the lymph node perpendicular to its longest axis, whereas longitudinal slicing involves sectioning along the equatorial plane. Studies suggest that bread-loaf slicing yields a higher metastasis detection rate compared to longitudinal slicing, yet no universally accepted standard for lymph node slicing exists, leading to significant variations in practice across institutions[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Indocyanine green (ICG) fluorescence imaging has become widely adopted for intraoperative SLN mapping, offering high detection rates and enabling precise lymphatic mapping during minimally invasive surgery (MIS)[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Collectively, differences in SLN mapping approaches and ultrastaging protocols may influence metastasis detection rates and impact the overall accuracy of disease staging. Establishing standardized methodologies could enhance the reliability and reproducibility of SLN-based strategies in early-stage EC management.\u003c/p\u003e\u003cp\u003eThis study aims to evaluate the clinical and oncologic outcomes of combining the two most reliable components of SLN mapping in uterine-confined EC: ICG-based mapping and postoperative ultrastaging using the bread-loaf slicing method. By assessing SLN detection rates and the efficacy of this combined approach, we seek to determine whether implementing these established best practices can optimize lymph node metastasis detection while minimizing surgical morbidity in early-stage EC patients, ultimately providing evidence for standardizing nodal assessment strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and patient selection\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study was conducted at Kaohsiung Chang Gung Memorial Hospital and included patients diagnosed with EC between November 2021 and December 2024. Eligible patients were those with a confirmed diagnosis of EC who underwent primary staging surgery incorporating SLN mapping, followed by post-operative pathological ultrastaging. Cases were excluded if they did not undergo primary staging surgery or if they underwent primary staging surgery without SLN assessment and mapping. The complete study workflow is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Demographic and clinicopathological data, including age, body mass index (BMI), tumor size, stage, histological subtype, and mismatch repair (MMR) status, were extracted from electronic medical records for analysis. This study was approved by the Institutional Review Board of Chang Gung Memorial Hospital, Taiwan (approval number: 202500410B0) and patient confidentiality was strictly maintained, and informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSurgical Procedure and SLN Mapping Protocol\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eSurgical Procedure and SLN Mapping Protocol\u003c/div\u003e\u003cp\u003eAll surgeries were performed by experienced gynecologic oncologic surgeons. The decision to perform SLN mapping was at the discretion of the attending surgeon, based on individual patient factors, tumor characteristics, and surgical considerations. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (a) to (c), after appropriate patient positioning, the tracer agent was injected into the cervix using a 1 mL syringe. For patients receiving ICG, the tracer was administered into two quadrants (3 and 9 o\u0026rsquo;clock positions), while for those receiving patent blue, it was injected into four quadrants (12, 3, 6, and 9 o\u0026rsquo;clock positions). The concentrations of ICG and patent blue were 2.5 mg/mL and 25 mg/mL, respectively, with 1 mL of the tracer agent injected per quadrant, consisting of 0.5 mL superficial (1\u0026ndash;3 mm) and 0.5 mL deep (1\u0026ndash;2 cm) cervical injections. After a waiting period of 10 to 15 minutes, bilateral pelvic lymphatic tract visualization and SLNs identification were assessed. If one side failed to show tracer uptake, an additional injection was given to that side. If both sides failed, bilateral reinjections were performed. After repeat injection, if a SLN was still not identified on a particular side, it was classified as a mapping failure, necessitating complete lymph node dissection on that side. Additionally, any lymph nodes suspected of metastasis during the procedure were removed, regardless of their mapping status. The harvested lymph nodes were labeled as SLNs or non-SLNs and sent for pathological examination. Success mapping refers to the intraoperative detection of SLNs. Overall success mapping requires SLN detection on at least one side, while bilateral success mapping requires detection on both sides.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003ePathological Examination and Outcome Measures\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003ePathological Examination and Outcome Measures\u003c/div\u003e\u003cp\u003eSLNs were classified according to size by the pathologist: the size smaller than 4 mm were directly placed into cassettes and submitted for sectioning; the size between 4\u0026ndash;8 mm were bisected and all submitted for sectioning, and those larger than 8 mm were sliced perpendicular to the long axis to be 2\u0026ndash;4 mm in thickness each by bread-loaf slicing method and all submitted for sectioning. The first section was stained using hematoxylin and eosin (H\u0026amp;E). If no definite tumor cells were identified on the initial H\u0026amp;E section, two subsequent 5-\u0026micro;m sections were cut from a second level, about 0.5 mm apart from the first section. One of these sections was stained with H\u0026amp;E, and the other was submitted to immunohistochemistry (IHC) using the anti-cytokeratin AE1/AE3 antibody (clone: AE1/AE3, 1:300, Genemed, South San Francisco). Non-SLNs were processed and examined according to the standard pathological protocol for lymph node evaluation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (d) to (f)). Detection rates refer to the proportion of lymph nodes identified through mapping that are confirmed to be true lymph nodes upon pathological examination.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was performed to compare the factors influencing bilateral SLNs success mapping. Continuous variables were compared using the Mann-Whitney U test and presented as medians with ranges. Categorical variables were analyzed using either the chi-squared test or Fisher\u0026rsquo;s exact test, depending on the expected frequencies, and were expressed as frequencies and percentages. All statistical analyses were performed using SPSS software for Windows, version 26 (IBM, Armonk, NY, USA). A \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePatient Demographics and Tumor Characteristics\u003c/h2\u003e\u003cp\u003eA total of 488 patients were diagnosed with EC between November 2021 to December 2024, of which 131 uterine-confined cases underwent primary staging surgery with intraoperative SLN assessment and mapping. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, most patients were diagnosed at an early clinical stage, with 76.3% (n\u0026thinsp;=\u0026thinsp;100) classified as FIGO stage IA and 22.9% (n\u0026thinsp;=\u0026thinsp;30) as stage IB with only one case as stage II. Postoperative pathologic evaluation confirmed stage I disease in 88.5% of cases (n\u0026thinsp;=\u0026thinsp;116), while the remaining 11.4% (n\u0026thinsp;=\u0026thinsp;15) were diagnosed with more advanced stages. Notably, two cases were initially staged as IA but were later found to have stage IVB disease postoperatively\u0026mdash;one due to omental metastasis and the other due to lung metastasis. Histopathological analysis revealed endometrioid adenocarcinoma as the predominant subtype (90.8%, n\u0026thinsp;=\u0026thinsp;119), with serous, clear cell, carcinosarcoma, and mixed histology accounting for the remaining cases. Low-grade histology was identified in 85.5% (n\u0026thinsp;=\u0026thinsp;112) of patients. Medium tumor size was 25 mm (range from 1-108 mm). Most tumors demonstrated positive estrogen receptor (ER) and progesterone receptor (PR) expression, with ER positivity in 87.0% (n\u0026thinsp;=\u0026thinsp;114) and PR positivity in 75.6% (n\u0026thinsp;=\u0026thinsp;99) of cases. Molecular profiling revealed MMR deficiency in 26.7% of tumors (n\u0026thinsp;=\u0026thinsp;35), while 61.1% (n\u0026thinsp;=\u0026thinsp;80) demonstrated MMR proficiency.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSurgical Approach and SLN Mapping Results\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB showed analysis of surgical characteristics revealing that MIS was the preferred approach, with 47.3% (n\u0026thinsp;=\u0026thinsp;62) undergoing laparoscopic surgery and 40.5% (n\u0026thinsp;=\u0026thinsp;53) undergoing robotic-assisted surgery, while only 12.2% (n\u0026thinsp;=\u0026thinsp;16) underwent open surgery. SLN mapping was predominantly performed using ICG (91.6%, n\u0026thinsp;=\u0026thinsp;120), with patent blue dye used in 8.4% (n\u0026thinsp;=\u0026thinsp;11). The overall SLN mapping success rate was 93.9%, with bilateral success in 74.8% of cases. Regarding SLN mapping sites, in the right hemipelvis, single-site mapping was achieved in 57.3% of cases, while multiple sites were identified in 24.4%. The most common SLN locations in the right hemipelvis were the internal iliac (29.3%), external iliac (28.0%), and obturator (24.0%) areas. In the left hemipelvis, SLNs were most frequently identified in the external iliac (36.1%), obturator (26.5%), and internal iliac (26.5%) regions, with single-site mapping being more common (63.4%) compared to multiple sites (19.8%). The overall detection rates of SLN were impressive at 95.9%, with bilateral detection reaching 87.8%.\u003c/p\u003e\n\u003ch3\u003eFactors Influencing Bilateral SLN Mapping Success\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents an analysis of factors influencing bilateral SLN mapping success. Across the entire cohort (N\u0026thinsp;=\u0026thinsp;131) and the ICG subgroup (N\u0026thinsp;=\u0026thinsp;120), age and preoperative CA-125 levels did not significantly differ between patients with bilateral mapping failure and those with successful mapping. Obesity, defined as BMI\u0026thinsp;\u0026ge;\u0026thinsp;30kg/m\u0026sup2;, was also not significantly associated with mapping success among entire cohort and the ICG subgroup. The surgical route was significantly related to bilateral mapping outcomes observed in the entire cohort; patients who underwent laparotomy had a higher failure rate compared to those who underwent MIS (56.2% vs. 20.9%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). However, this association was not observed within the ICG-only subgroup (28.6% vs. 20.4%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.635). Additionally, the choice of tracer agent significantly impacted mapping success, with patent blue dye associated with a higher failure rate compared to ICG (72.7% vs. 20.8%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Other clinical and pathological factors, including histologic subtype (endometrioid versus non-endometrioid), tumor size, depth of myometrial invasion, and H-score of ER and PR, were not statistically significantly different.\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\u003eClinicopathological and Surgical Characteristics of Endometrial Cancer Patients Undergoing Sentinel Lymph Node Mapping\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"15\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eA. Clinicopathological Characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c15\" namest=\"c9\"\u003e\u003cp\u003eB. Surgical Approaches, SLNs Mapping Results, and Detection Characteristics\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003ePatients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;131)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003ePatients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;131)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eMedian age (year), range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(22\u0026ndash;80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e\u003cp\u003eSurgical route\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eMedian BMI (kg/m\u003csup\u003e2\u003c/sup\u003e), range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e26.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(18.0-40.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e\u003cp\u003eLaparotomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(12.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(72.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e\u003cp\u003eLaparoscopy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(47.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e≧\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e (Obesity)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(27.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e\u003cp\u003eRobotic surgery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(40.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eMedian parity, range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0\u0026ndash;6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e\u003cp\u003eTracer agent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(32.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e\u003cp\u003ePatent blue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(8.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e≧\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(67.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e\u003cp\u003eIndocyanine green (ICG)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(91.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eMedian preoperative\u003c/p\u003e\u003cp\u003eCA-125 (U/mL), range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e17.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(4.5-132.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e\u003cp\u003eMapping results\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003ePreoperative clinical FIGO stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eOverall success\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(93.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eⅠA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(76.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eBilateral success\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(74.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eⅠB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(22.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eUnilateral success\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eⅡ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eRight hemipelvis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(8.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003ePostoperative pathologic FIGO stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eLeft hemipelvis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(10.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eⅠA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(72.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eBilateral failure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(6.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eⅠB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(16.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e\u003cp\u003eMapping area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eⅡ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(5.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eRight hemipelvis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eⅢC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(4.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eSingle site\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(57.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eⅣB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eMultiple sites\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(24.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eHistology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eFailure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(16.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eEndometrioid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(90.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(1.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eSerous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(4.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eLeft hemipelvis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eClear cell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eSingle site\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(63.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eCarcinosarcoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eMultiple sites\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(19.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eMixed endometrioid / clear cell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eFailure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(14.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eEIN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(2.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eGrade\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c14\" namest=\"c9\"\u003e\u003cp\u003eLocation of SLNs in single site\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eLow-grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(85.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eRight hemipelvis (n\u0026thinsp;=\u0026thinsp;75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eHigh-grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(12.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eObturator area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(24.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eExternal iliac area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(28.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eMedium Tumor size (mm), range\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(1-108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eInternal iliac area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(29.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;20 mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(32.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eCommon iliac area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(13.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e≧\u0026thinsp;20 mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(59.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003ePresacral area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(4.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eNo residual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(8.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eParametrical area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(1.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eDepth of myometrium invasion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eParaaortic area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(79.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eLeft hemipelvis (n\u0026thinsp;=\u0026thinsp;83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e≧\u0026thinsp;50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(20.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eObturator area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(26.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eLVSI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eExternal iliac area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(36.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(82.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eInternal iliac area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(26.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eFocal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(9.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eCommon iliac area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(10.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eSubstantial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(8.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003ePresacral area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eMMR status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eParametrical area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eProficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(61.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eParaaortic area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eDeficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(26.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c14\" namest=\"c9\"\u003e\u003cp\u003eNumber of removed SLNs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eUnknown\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(12.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eRight hemipelvis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(1\u0026ndash;13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eLeft hemipelvis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e(1\u0026ndash;22)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(87.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c14\" namest=\"c9\"\u003e\u003cp\u003eDetection rate of SLNs (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eH-score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0-285)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e95.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003ePR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eBilateral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e87.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003ePositive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(75.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eRight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e94.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eH-score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0-288)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e\u003cp\u003eLeft\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e90.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"15\"\u003eBMI, body mass index; CA-125, carbohydrate antigen-125; SLNs, sentinel lymph nodes; LVSI, lymphovascular space invasion; EIN, endometrial intraepithelial neoplasia; MMR, mismatch repair; ER, estrogen receptor; PR, progesterone receptor.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"15\"\u003ea. Low-grade: endometrioid grade 1\u0026ndash;2; High-grade: endometrioid grade 3, serous, clear cell, and carcinosarcoma; Not applicable: mixed histology and endometrial intraepithelial neoplasia.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"15\"\u003eb. Unknown MMR status: 9 cases with no residual tumor after surgery (6 diagnosed externally, 3 internally) and 7 cases without MMR documentation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnalysis of Factors Influencing Sentinel Lymph Node Bilateral Success Mapping, Including ICG Subgroup\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003ePatients with\u003c/p\u003e\u003cp\u003ebilateral success mapping\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eAll patients (N\u0026thinsp;=\u0026thinsp;131)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e\u003cp\u003ePatients with ICG as tracer agent (N\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eFailure\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;33, 25.2%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eSuccess\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;98, 74.8%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eFailure\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;25, 20.8%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eSuccess\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;95, 79.2%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge (year)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(29\u0026thinsp;~\u0026thinsp;75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(22\u0026thinsp;~\u0026thinsp;80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(31\u0026thinsp;~\u0026thinsp;75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(22\u0026thinsp;~\u0026thinsp;80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.630\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePreoperative CA-125 (U/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(6.1\u0026thinsp;~\u0026thinsp;63.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(4.5\u0026thinsp;~\u0026thinsp;132.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(6.1\u0026thinsp;~\u0026thinsp;63.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e17.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(4.5\u0026thinsp;~\u0026thinsp;132.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.348\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eObesity\u003c/p\u003e\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.498\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(22.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(77.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(19.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(80.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI\u0026thinsp;≧\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(66.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(25.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(75.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSurgical route\u003c/p\u003e\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.635\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLaparotomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(56.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(43.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(28.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(71.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMIS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(20.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(79.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(20.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(79.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTracer agent\u003c/p\u003e\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"5\" morerows=\"2\" nameend=\"c12\" namest=\"c8\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePatent blue\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(72.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(27.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eICG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(20.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(79.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHistology\u003c/p\u003e\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-endometrioid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(25.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(75.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(18.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(76.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEndometrioid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(25.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(74.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(21.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(78.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTumor size\u003c/p\u003e\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;20 mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(17.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(83.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(17.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(82.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e≧\u0026thinsp;20 mm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(30.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(69.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(23.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(76.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMyoinvasion\u003c/p\u003e\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(25.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(75.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(20.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(79.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e≧\u0026thinsp;50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(26.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(74.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(20.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(79.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eLVSI\u003c/p\u003e\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\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo or focal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(24.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(75.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(19.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(80.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSubstantial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(36.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(63.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(36.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(63.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eER H-score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0\u0026thinsp;~\u0026thinsp;285)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0\u0026thinsp;~\u0026thinsp;285)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(0\u0026thinsp;~\u0026thinsp;285)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(0\u0026thinsp;~\u0026thinsp;285)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ePR H- score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0\u0026thinsp;~\u0026thinsp;288)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0\u0026thinsp;~\u0026thinsp;285)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(0\u0026thinsp;~\u0026thinsp;288)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(0\u0026thinsp;~\u0026thinsp;285)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.840\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003eCA-125, carbohydrate antigen-125; BMI, body mass index; MIS, minimally invasive surgery; ICG, indocyanine green; LVSI, lymphovascular space invasion; ER, estrogen receptor; PR, progesterone receptor.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCharacteristics of Patients with Lymph Node Metastases\u003c/h2\u003e\u003cp\u003eAmong the study population, eight cases (6.1%) were pathologically identified with positive lymph nodes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Two cases (25%) were classified as pathologic stage IB with isolated tumor cells (ITCs) in observed SLNs, and the remaining 6 cases (75%) were classified as stage IIIC1 due to micrometastasis or macrometastasis of SLNs. Most tumors (n\u0026thinsp;=\u0026thinsp;7, 87.5%) were low-grade endometrioid carcinoma, except for one case of serous carcinoma. Median tumor size was 41mm (range from 18-108mm), with most cases (n\u0026thinsp;=\u0026thinsp;6, 75%) exhibited deep myometrial invasion (myoinvasion\u0026thinsp;≧\u0026thinsp;50%). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows a representative case of ITCs identified within a SLN, confirmed by positive AE1/AE3 immunohistochemical staining.\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\u003eClinicopathologic Features of Patients with Lymph Node Metastases\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCase\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003cp\u003e(years)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePreoperative Clinical FIGO stage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePostoperative pathologic\u003c/p\u003e\u003cp\u003eFIGO stage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHistology\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGrade\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTumor size\u003c/p\u003e\u003cp\u003e(mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMI depth\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eLVSI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eArea of\u003c/p\u003e\u003cp\u003enode-positive\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eMetastasis status\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eⅠB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eⅠB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e≧\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eFocal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSLN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eITCs\u003c/p\u003e\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\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eⅠB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eⅠB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e≧\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eFocal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSLN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eITCs\u003c/p\u003e\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\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eⅠA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eⅢC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e≧\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSubstantial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSLN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003em\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eⅠB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eⅢC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e≧\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNon-SLN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003em\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eⅠB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eⅢC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e≧\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSubstantial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSLN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003em\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eⅠB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eⅢC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eFocal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNon-SLN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eⅠA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eⅢC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh-grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNegative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNon-SLN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eⅠA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eⅢC1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e≧\u0026thinsp;50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSubstantial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSLN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003em\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNon-SLN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eM\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eLVSI, lymphovascular space invasion; SLNs, sentinel lymph nodes; MI, myometrium invasion.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003ea. E: endometrioid; S: serous\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eb. ITCs: isolated tumor cells; m: micrometastasis; M: macrometastasis\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe main findings of this study revealed that postoperative lymph node ultrastaging with the bread-loaf slicing method, following intraoperative SLN mapping with ICG tracer dye, optimizes the detection of lymph node metastases in uterine-confined EC. Furthermore, the use of ICG as a tracer agent mitigates common factors contributing to mapping failure and significantly improves SLN detection rates, even in open surgery cases.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eImpact of Surgical Approach and ICG Fluorescence on SLN mapping\u003c/h2\u003e\u003cp\u003eThe efficacy of MIS, particularly when combined with ICG fluorescence imaging, has been demonstrated in SLN mapping for EC[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. MIS was utilized in 87.8% of our cohort, reinforcing its well-established benefits in reducing morbidity while maintaining oncologic outcomes. Our study achieved a high SLN success mapping rate, with an overall success rate of 95.9% and a bilateral success rate of 74.8%. Compared to the FIRES trial, which reported an overall mapping success rate of 86% and a bilateral success rate of 52%, our findings suggest improved SLN mapping efficiency with the incorporation of standardized techniques[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Compared with patent blue, the use of ICG significantly improved bilateral SLN mapping success (27.3% vs. 79.2%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reinforcing its superiority as the preferred tracer for SLN detection. These findings are consistent with the FILM trial, which reported that ICG with near-infrared fluorescence imaging identified more SLNs than isosulfan blue dye in uterine cancers[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Eriksson et al. also demonstrated that ICG improves SLN detection, even in obese patients, compared to blue dye[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, our study confirmed that MIS was associated with significantly higher SLN detection rates than open surgery (79.1% vs. 43.8%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), consistent with findings from the LAP2 and SENTI-ENDO trials[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, within the subgroup of patients who received ICG as the tracer agent, no statistically significant difference in success rates was observed between MIS and laparotomy (79.6% vs. 71.4%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.635). This finding suggests that while MIS generally enhances mapping success, the use of ICG as a tracer may mitigate differences in outcomes between surgical approaches, potentially due to the superior efficacy of ICG in facilitating lymphatic mapping regardless of surgical technique. Taken together, these findings strongly support the routine use of ICG to optimize SLN detection and ensure accurate staging\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eClinical and Pathological Factors Affecting SLN Mapping Success\u003c/h2\u003e\u003cp\u003eThe SAGE multicenter study by Cianci et al. identified advanced age and higher BMI as independent risk factors for bilateral SLN mapping failure in EC, attributing these associations to age-related lymphatic capillary rarefaction and the technical challenges posed by obesity[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In contrast, our retrospective cohort analysis found that neither age nor BMI was significantly associated with bilateral SLN mapping success\u0026mdash;both in the overall cohort (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.994 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.186, respectively) and in the ICG subgroup (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.630 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.498, respectively). This discrepancy may be explained by differences in sample size and age distribution, as our study population was smaller and had a younger median age (56 years) compared to the SAGE cohort (64 years). Furthermore, the predominant use of ICG as the tracer and adherence to standardized mapping protocols in our study may have minimized the impact of age and BMI on mapping outcomes. Other contributing factors may include high surgeon experience and a lower proportion of patients with extreme obesity, which could have enhanced mapping success in our cohort. Cianci et al. also identified LVSI as a significant negative predictor of successful SLN mapping, suggesting that the presence of LVSI may disrupt effective tracer migration through lymphatic channels, thereby reducing mapping success[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, in our cohort, LVSI was not significantly associated with SLN detection rates. This discrepancy may reflect differences in methodology, such as the predominant use of ICG as a tracer in our study, or variations in patient populations, including a higher proportion of early-stage and low-grade tumors, which could diminish the impact of LVSI on mapping outcomes. Additionally, our definition of LVSI positivity was limited to cases of substantial LVSI, excluding focal involvement, which may differ from the criteria used by Cianci et al. and thus contribute to the observed differences.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eAnatomical Distribution of SLNs\u003c/h2\u003e\u003cp\u003eRegarding SLN detection rates, we observed an overall detection rate of 95.9%, with detection rates of 94.5% in the right hemipelvis and 90.2% in the left hemipelvis. These results are consistent with the SENTI-ENDO trial, which reported SLN detection rates of 89% (overall), 77% (right), and 76% (left)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Recent studies have demonstrated consistently high SLN detection rates in EC. Fierro et al. reported an overall detection rate of 96.7% using ICG, with 97.3% in laparoscopic surgery and 95.5% in robotic surgery, supporting the reliability of SLN mapping across different minimally invasive surgical approaches[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These findings are consistent with meta-analyses by Lin et al. showing bilateral detection rates of 82\u0026ndash;86% for both laparoscopic and robotic approaches when ICG is used as the tracer[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Additionally, SLNs were most frequently identified in the external iliac, obturator, and internal iliac regions, whereas para-aortic SLN detection was rare, aligning with previous literature. Recent data from studies that performed SLN mapping with reference lymphadenectomy has shown the risk of isolated para-aortic metastasis to be as low as 0.8-1%[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These findings further support that the lower paracervical pathway is the predominant drainage route, with the majority of mapped SLNs located in the pelvic region, while para-aortic SLN detection remains limited (~\u0026thinsp;5\u0026ndash;10%)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The localization of SLNs in the pelvic basin further supports SLN mapping as a reliable alternative to full lymphadenectomy, particularly for low- and intermediate-risk EC patients.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eBread-loaf Slicing Ultrastaging Protocol and Its Advantages\u003c/h2\u003e\u003cp\u003eSeveral methods were employed for slicing lymph nodes during ultrastaging in EC, each with distinct protocols, advantages, and limitations. Commonly used macroscopic slicing techniques include longitudinal slicing section and bread-loaf slicing section[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Longitudinal sectioning, on the other hand, slices the lymph node along its longest axis, which is technically simpler, faster to perform, and preserves nodal architecture[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Bread-loaf slicing involves cutting the lymph node into multiple thin sections, typically 2\u0026ndash;3 mm thick, perpendicular to the long axis. This approach ensures comprehensive sampling of the entire node, increasing the likelihood of detecting low-volume metastases, including micrometastases and ITCs[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In our study, bread-loaf slicing was adopted as the ultrastaging protocol for SLN assessment, following the methodology of the FIRES trial[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Specifically, SLNs larger than 8 mm were sliced perpendicular to the long axis into 2\u0026ndash;4 mm sections, with subsequent histological and immunohistochemical evaluation. This protocol was chosen based on robust evidence from a systematic review by Burg et al., which demonstrated that bread-loaf slicing achieved a significantly higher SLN metastasis detection rate (53%) compared to longitudinal slicing (33%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The enhanced sensitivity of bread-loaf slicing is attributed to its cross-sectional approach, which increases the probability of detecting metastatic deposits that might be missed with longitudinal slicing. Furthermore, the review found that increasing the number of IHC slides did not significantly improve detection rates, highlighting that the slicing method itself is the most influential factor in ultrastaging sensitivity. Despite the absence of standardized international guidelines for SLN ultrastaging, our findings align with previous studies supporting the bread-loaf slicing technique as an optimal approach. Given its higher diagnostic accuracy, our study further supports the need for standardizing SLN ultrastaging with the bread-loaf technique to optimize metastatic detection and improve staging accuracy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eComparison of Lymph Node Metastasis Detection Rates and Clinical Outcomes\u003c/h2\u003e\u003cp\u003eAccording to Katsoulakis et al., an analysis using the SEER registry evaluated the incidence of lymph node metastasis in clinical stage I, low-grade, endometrioid EC and reported a 3.5% risk of pelvic lymph node metastasis based on systematic pelvic lymphadenectomy[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Similarly, a recent study reported by Praiss et al. using pelvic lymphadenectomy for nodal assessment found a comparable incidence of 4.0% LN metastasis in low-grade, stage I ECs, further confirming the low but notable risk of nodal involvement[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In our study, we employed a different methodology, utilizing intraoperative ICG for SLN mapping and postoperative ultrastaging with the bread-loaf slicing method, yielding a comparable lymph node metastasis detection rate of 4.0%. This suggests that our combined approach of ICG dye for mapping and bread-loaf slicing for ultrastaging may detect micrometastases missed by conventional lymphadenectomy, highlighting the diagnostic advantage of this method while reducing surgical morbidity in early-stage, low-risk EC patients. As of this manuscript submission, none of the patients diagnosed with pathological stage I disease\u0026mdash;whether through successful SLN mapping or conventional lymph node dissection following mapping failure\u0026mdash;have experienced regional or distant recurrences. This suggests a favorable prognosis for patients treated at an early stage. We will maintain ongoing surveillance to ensure early detection and timely intervention if needed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study has several important limitations that should be acknowledged. First, its retrospective design inherently introduces selection bias and limits the ability to control for confounding variables that may influence SLN detection rates and metastasis identification. Second, as a single-institution study, the findings may not be fully generalizable to other clinical settings with different patient populations and levels of surgical expertise. Lastly, the study lacked a direct comparison group using longitudinal slicing within the same patient population, which would have provided stronger evidence for the superiority of the bread-loaf technique. Despite these limitations, this study offers valuable insights into optimizing lymph node metastasis detection in uterine-confined EC. Addressing these methodological issues in future prospective, multi-institutional research could enhance the reliability and clinical applicability of the findings.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study demonstrates that SLN mapping with ICG fluorescence imaging combined with bread-loaf slicing ultrastaging optimizes lymph node assessment in early-stage EC. Our approach achieved superior detection rates (95.9% overall, 74.8% bilateral) compared to previous studies, with ICG significantly outperforming blue dye. The combined methodology enhanced micrometastasis detection while maintaining oncologic safety, as evidenced by zero recurrences in pathological stage I patients to date. These findings support this combined approach as an effective strategy that balances accurate staging with reduced surgical morbidity in EC management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003cp\u003e This study was approved by the Institutional Review Board of Chang Gung Memorial Hospital, Taiwan (approval number: 202500410B0) and patient informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003cp\u003e All data and materials used in this study were obtained from the electronic medical records of Chang Gung Memorial Hospital, Taiwan.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThe research described in this study was conducted without any external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYS performed all the experiments and drafted the manuscript. H and CH provided the conception and design of study. HC, YW, and YY provided the acquisition of data. SW contributed to the data analysis and interpretation. YC, CC, and CH revised the manuscript critically for important intellectual content. All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAvailability of data and materials:\u003c/h2\u003e\u003cp\u003eNot applicable\u003c/p\u003e\u003cp\u003eCompeting interests:\u003c/p\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHealth Promotion Administration Ministry Of Health And Welfare Taiwan. Cancer Registry Annual Report. 2022 Taiwan. 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWorld Cancer Research Fund. Endometrial Cancer Statistics: World Cancer Research Fund; [Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wcrf.org/preventing-cancer/cancer-statistics/endometrial-cancer-statistics/#endometrial-cancer-incidence-cases\u003c/span\u003e\u003cspan address=\"https://www.wcrf.org/preventing-cancer/cancer-statistics/endometrial-cancer-statistics/#endometrial-cancer-incidence-cases\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDobrzycka B, Terlikowska KM, Kowalczuk O, Niklinski J, Kinalski M, Terlikowski SJ. Prognosis of Stage I Endometrial Cancer According to the FIGO 2023 Classification Taking into Account Molecular Changes. Cancers (Basel). 2024;16(2).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrost JA, Webster KE, Bryant A, Morrison J. Lymphadenectomy for the management of endometrial cancer. Cochrane Database Syst Rev. 2017;10(10):Cd007585.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKitchener H, Swart AM, Qian Q, Amos C, Parmar MK. Efficacy of systematic pelvic lymphadenectomy in endometrial cancer (MRC ASTEC trial): a randomised study. Lancet. 2009;373(9658):125\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePraiss AM, Huang Y, St Clair CM, Tergas AI, Melamed A, Khoury-Collado F, et al. A modern assessment of the surgical pathologic spread and nodal dissemination of endometrial cancer. Gynecol Oncol. 2020;157(2):329\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim CH, Soslow RA, Park KJ, Barber EL, Khoury-Collado F, Barlin JN, et al. Pathologic ultrastaging improves micrometastasis detection in sentinel lymph nodes during endometrial cancer staging. Int J Gynecol Cancer. 2013;23(5):964\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRau TT, Deppeler MV, Christe L, Siegenthaler F, Imboden S, Papadia A, et al. Pathological processing of sentinel lymph nodes in endometrial carcinoma - routine aspects of grossing, ultra-staging, and surgico-pathological parameters in a series of 833 lymph nodes. Virchows Arch. 2022;481(3):421\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBurg LC, 't Hengeveld EM. Hout J, Bulten J, Bult P, Zusterzeel PLM. Ultrastaging methods of sentinel lymph nodes in endometrial cancer - a systematic review. Int J Gynecol Cancer. 2021;31(5):744\u0026thinsp;\u0026ndash;\u0026thinsp;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFrumovitz M, Plante M, Lee PS, Sandadi S, Lilja JF, Escobar PF, et al. Near-infrared fluorescence for detection of sentinel lymph nodes in women with cervical and uterine cancers (FILM): a randomised, phase 3, multicentre, non-inferiority trial. Lancet Oncol. 2018;19(10):1394\u0026ndash;403.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuda A, Crivellaro C, Elisei F, Di Martino G, Guerra L, De Ponti E, et al. Impact of Indocyanine Green for Sentinel Lymph Node Mapping in Early Stage Endometrial and Cervical Cancer: Comparison with Conventional Radiotracer (99m)Tc and/or Blue Dye. Ann Surg Oncol. 2016;23(7):2183\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePersson J, Salehi S, Bollino M, L\u0026ouml;nnerfors C, Falconer H, Geppert B. Pelvic Sentinel lymph node detection in High-Risk Endometrial Cancer (SHREC-trial)-the final step towards a paradigm shift in surgical staging. Eur J Cancer. 2019;116:77\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalker JL, Piedmonte MR, Spirtos NM, Eisenkop SM, Schlaerth JB, Mannel RS, et al. Recurrence and survival after random assignment to laparoscopy versus laparotomy for comprehensive surgical staging of uterine cancer: Gynecologic Oncology Group LAP2 Study. J Clin Oncol. 2012;30(7):695\u0026ndash;700.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRossi EC, Kowalski LD, Scalici J, Cantrell L, Schuler K, Hanna RK, et al. A comparison of sentinel lymph node biopsy to lymphadenectomy for endometrial cancer staging (FIRES trial): a multicentre, prospective, cohort study. Lancet Oncol. 2017;18(3):384\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEriksson AG, Montovano M, Beavis A, Soslow RA, Zhou Q, Abu-Rustum NR, et al. Impact of Obesity on Sentinel Lymph Node Mapping in Patients with Newly Diagnosed Uterine Cancer Undergoing Robotic Surgery. Ann Surg Oncol. 2016;23(8):2522\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBallester M, Dubernard G, Lecuru F, Heitz D, Mathevet P, Marret H, et al. Detection rate and diagnostic accuracy of sentinel-node biopsy in early stage endometrial cancer: a prospective multicentre study (SENTI-ENDO). Lancet Oncol. 2011;12(5):469\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCianci S, Rosati A, Vargiu V, Capozzi VA, Sozzi G, Gio\u0026egrave; A, et al. Sentinel Lymph Node in Aged Endometrial Cancer Patients The SAGE Study: A Multicenter Experience. Front Oncol. 2021;11:737096.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFierro A, Flores I, Pellicer I, Alonso-Espias M, Garcia-Pineda V, Zapardiel I et al. Route of Surgery for Sentinel Node Biopsy in Endometrial Cancer: Laparoscopy Versus Robotics. J Clin Med. 2025;14(12).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin H, Ding Z, Kota VG, Zhang X, Zhou J. Sentinel lymph node mapping in endometrial cancer: a systematic review and meta-analysis. Oncotarget. 2017;8(28):46601\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDheur A, Kakkos A, Danthine D, Delbecque K, Goffin F, Gonne E, et al. Lymph node assessment in cervical cancer: current approaches. Front Oncol. 2024;14:1435532.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDundr P, Cibula D, Němejcov\u0026aacute; K, Tich\u0026aacute; I, B\u0026aacute;rtů M, Jakša R. Pathologic Protocols for Sentinel Lymph Nodes Ultrastaging in Cervical Cancer. Arch Pathol Lab Med. 2019.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKatsoulakis E, Mattes MD, Rineer JM, Nabhani T, Mourad WF, Choi K, et al. Contemporary analysis of pelvic and para-aortic metastasis in endometrial cancer using the SEER registry. Int J Gynaecol Obstet. 2014;127(3):293\u0026ndash;6.\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":"world-journal-of-surgical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjso","sideBox":"Learn more about [World Journal of Surgical Oncology](http://wjso.biomedcentral.com)","snPcode":"12957","submissionUrl":"https://submission.nature.com/new-submission/12957/3","title":"World Journal of Surgical Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Endometrial Neoplasms, Indocyanine Green, Lymphatic Metastasis / pathology (Ultrastaging), Sentinel Lymph Node","lastPublishedDoi":"10.21203/rs.3.rs-7661891/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7661891/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAccurate lymph node assessment is crucial in early-stage endometrial cancer staging, but traditional lymphadenectomy carries significant morbidity risks. This study evaluates whether indocyanine green (ICG)-based sentinel lymph node (SLN) mapping combined with bread-loaf slicing ultrastaging optimizes lymph node metastasis detection in uterine-confined endometrial cancer.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe retrospectively analyzed patients with early-stage endometrial cancer who underwent surgery with SLN mapping at Kaohsiung Chang Gung Memorial Hospital from November 2021 to December 2024. SLN mapping was performed using either ICG fluorescence imaging or patent blue dye during minimally invasive and open surgical approaches. All retrieved lymph nodes underwent ultrastaging examination using the bread-loaf slicing method.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong 131 patients, the overall SLN mapping success rate was 93.9%, with bilateral detection rate achieved 95.9%. ICG demonstrated superior bilateral mapping success compared to patent blue dye (79.2% vs. 27.3%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). While minimally invasive surgery showed higher mapping rates than open surgery (79.1% vs. 43.8%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), ICG effectively mitigated this difference by maintaining high detection rates even in open surgical cases. Bread-loaf slicing ultrastaging identified lymph node metastases in 4.0% of patients. Zero recurrence occurred in pathological stage I patients during follow-up.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions:\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe combination of intraoperative ICG-based SLN mapping with bread-loaf slicing ultrastaging represents an optimal strategy for lymph node assessment in early-stage endometrial cancer, achieving superior detection rates while minimizing surgical morbidity and maintaining oncologic safety.\u003c/p\u003e","manuscriptTitle":"Optimized Sentinel Node Detection in Endometrial Cancer: Intraoperative Indocyanine Green Mapping with Postoperative Bread-Loaf Slicing Ultrastaging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 11:42:19","doi":"10.21203/rs.3.rs-7661891/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-13T06:18:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-02T20:38:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176679103718471459818080107282448565553","date":"2025-10-22T05:47:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223254565791168855505767246080363367296","date":"2025-10-22T02:26:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-19T17:31:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-05T17:54:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123551418115819782778701041465176127739","date":"2025-10-01T16:58:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329339712795918634610810294728434596661","date":"2025-09-25T11:16:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222927041538486439363081564962140754429","date":"2025-09-25T05:53:54+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-23T05:40:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-22T05:49:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-21T23:22:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"World Journal of Surgical Oncology","date":"2025-09-20T02:50:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-surgical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjso","sideBox":"Learn more about [World Journal of Surgical Oncology](http://wjso.biomedcentral.com)","snPcode":"12957","submissionUrl":"https://submission.nature.com/new-submission/12957/3","title":"World Journal of Surgical Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ce42d02d-290c-4070-9dcd-939685daff53","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:00:46+00:00","versionOfRecord":{"articleIdentity":"rs-7661891","link":"https://doi.org/10.1186/s12957-026-04375-7","journal":{"identity":"world-journal-of-surgical-oncology","isVorOnly":false,"title":"World Journal of Surgical Oncology"},"publishedOn":"2026-04-29 15:58:17","publishedOnDateReadable":"April 29th, 2026"},"versionCreatedAt":"2025-10-06 11:42:19","video":"","vorDoi":"10.1186/s12957-026-04375-7","vorDoiUrl":"https://doi.org/10.1186/s12957-026-04375-7","workflowStages":[]},"version":"v1","identity":"rs-7661891","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7661891","identity":"rs-7661891","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}