Impact of Surgeons’ Experience on Oncologic Outcomes in Women Undergoing Laparoscopic Radical Hysterectomy for Cervical Cancer: A Comparison of the First 50 and last 50 Cases

Impact of Surgeons’ Experience on Oncologic Outcomes in Women Undergoing Laparoscopic Radical Hysterectomy for Cervical Cancer: A Comparison of the First 50 and last 50 Cases | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research article Impact of Surgeons’ Experience on Oncologic Outcomes in Women Undergoing Laparoscopic Radical Hysterectomy for Cervical Cancer: A Comparison of the First 50 and last 50 Cases Pengfei Li, Shan Kang, Jianxin Guo, Shiqi Liang, Ying Yang, Shaoguang Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-26906/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives: To compare the oncological outcomes of the first 50 laparoscopic radical hysterectomy (LRH) surgeries with the last 50 LRH, performed by high volume surgeons, for cervical cancer patients. Design : A nationwide multicentre retrospective cohort study Setting : Clinical diagnosis and treatment of cervical cancer patients in mainland China (Four C) database. Population: women with early cervical cancer undergone LRH. Methods: We retrospectively analyzed the oncological outcomes of 1004 cervical cancer patients who underwent LRH performed by 19 surgeons. They were divided into two groups according to the sequence of operations, the first 50 and the last 50 patients with LRH. Kaplan-Meier survival analysis and log-rank test, Cox proportional risk regression model and propensity score matching were used. Main Outcome Measures: 5-year overall survival (OS) and disease-free survival (DFS) rates. Results: There were no significant differences in the 5-year OS and DFS between first 50 patients with LRH group (n=413) and last 50 patients with LRH group (n=591) (OS: p=0.388; DFS: p=0.226). The last 50 cases of LRH was not an independent risk factor for OS and DFS in early cervical cancer patients (p=0.830, p=0.300). After propensity score matching, similar outcomes were observed (n=364:364，OS:P = 0.764; DFS:P = 0.705). Conclusions: The oncological outcomes of the first 50 LRH surgeries were similar to those of the last 50 surgeries in patients with early-stage cervical cancer. Increase in the surgeons’ experience did not improve significantly with oncological outcomes of patients with early stage cervical cancer after LRH. Cancer Biology Oncology cervical cancer laparoscopy surgical experience learning curve oncological outcome Figures Figure 1 Figure 2 Figure 3 1. Introduction Radical hysterectomy in combination with pelvic lymphadenectomy is the standard treatment for non-fertility sparing stage IA1 cervical cancer with positive lymphovascular space invasion (LVSI) to stage IIA2 cervical cancer. NCCN guideline 1 used to indicate that laparotomy or minimally invasive surgery (MIS) are acceptable for patients with early stage cervical cancer. Due to these suggestions, MIS was widely used. However, in November 2018, Ramirez et al. 2 published a multicentre, phase III randomized controlled trial in the New England Journal of Medicine, which reported that laparoscopic radical hysterectomy(LRH) was associated with worse OS and DFS rates after 4.5 years in patients with early stage cervical cancer than was abdominal radical hysterectomy (ARH). In the same period, retrospective cohort data from Melamed et al. 3 were also published, and LRH was reported to result in a shorter OS than ARH. These two studies overturned the traditional understanding of laparoscopic surgery in gynecologic oncology. Cervical Cancer Guidelines (Version 3.2019) released by NCCN Clinical Practice Guidelines in Oncology in 2019 4 no longer recommends laparoscopic surgery as the preferred surgical approach for the treatment of early stage cervical cancer . Although these two studies suggested that the long-term prognosis was poor for patients that underwent LRH with early stage cervical cancer, the study did not provide reasons for the poor efficacy of laparoscopy. Among many possible causes, surgical experience was often thought to influence oncological outcomes. Therefore, we suggest a hypothesis: was the surgeons’ experience one of the reasons for the poor outcomes of LRH surgery? However, the previous studies on the learning curve of LRH for early stage cervical cancer focused on operative techniques such as operation time, bleeding volume, incidence of intraoperative and postoperative complications and perioperative outcomes, rarely focused on long-term oncological outcomes and ignored the influence of surgeons’ experience on the long-term oncological outcomes of patients with early stage cervical cancer. In addition, in most of the studies, single-center, single-surgeon and small samples were included. To test this hypothesis, we explored whether the experience of surgeons affects the efficacy of LRH based on the clinical diagnosis and treatment of cervical cancer in China database. This study is a multicenter retrospective study. We compare the oncological outcomes of the first 50 LRH surgeries and the last 50 LRH surgeries of surgeons for patients with early stage cervical cancer. 2. Materials And Methods 2.1 Data Source The data used in this study is from the Clinical Diagnosis and Treatment for Cervical Cancer in China (Four C) Database, which covers 46 313 patients with cervical cancer in 37 hospitals in mainland China from January 2004 to December 2016. The establishment of this database was approved by the Ethics Committee of the Southern Hospital of Southern Medical University. The ethical approval number is NEEC-2017-135, and the clinical research registration number is CHiCTR1800017778 (International Clinical Trials Registry Platform Search Port, http://apps.who.int/trialsearch/ ). All follow-up procedures were carried out by trained gynecological oncology staff at each centre to keep the patients' personal data confidential and to provide disease management guidance at the same time. The follow-up information was gathered through the return visit system or telephone follow-up, including survival status, time of death, recurrence time, recurrence site, treatment after recurrence. The oncological outcomes were estimated according to the recorded information, and the last day of the return visit or telephone follow-up was defined as the last follow-up. We follow-up all patients, but the follow-up rate of oncological outcomes was 72.7% in this database. Detailed data collection requirements and database establishment processes were described in our previously published study. 5 , 6 Laparoscopic cervical cancer surgery started to be conducted in all participating institutions after 2004,so the first LRH cases performed by surgeons after this year were included in this database. In this study, more than 600 surgeons were enrolled. In the database, 19 surgeons who completed more than 100 cases of LRH were enrolled. The operation cases were sorted according to the operation date. The first 50 patients with LRH were considered a group and the last 50 patients with LRH were considered another group. The patients were grouped according to the date of the operation, regardless of whether they met the inclusion or exclusion criteria. In this database, the FIGO stage was recorded and corrected by tumour size according to the FIGO 2009 staging system. Tumour size was evaluated using magnetic resonance imaging, computed tomography, ultrasound, physical examination or postoperative pathological records. 2.2 Inclusion and exclusion criteria The patients’ inclusion criteria were as follows: age 18 years or older; cervical cancer diagnosis of FIGO stage IA1 with positive LVSI to stage IIA2; squamous cell carcinoma, adenocarcinoma, or adenosquamous cell carcinoma; a history of laparoscopic surgery; type QM-B or QM-C 7 hysterectomy with pelvic lymphadenectomy with or without paraaortic lymphadenectomy; and a postoperative survival outcome. Surgeons who performed 100 or more LRH surgeries were also included, and we excluded patients if they had neoadjuvant chemotherapy or radiotherapy before surgery. Patients with pregnancy or other types of malignant tumours were excluded, too. In subgroup analyses, the standard postoperative adjuvant therapy was defined as cervical cancer patients with one or more high-risk factors (lymph node metastasis, parametrial involvement, and surgical margin invasion) or patients with two or more intermediate-risk factors (deep cervical stromal invasion, tumor size > 4 cm, and LVSI were received radiotherapy or chemo-radiotherapy. 8 2.3 Outcome measures The overall 5-year survival rate (OS) and disease-free survival rate (DFS) were calculated. OS was defined as the time from diagnosis to death for any reason. DFS was defined as the time from diagnosis to death or recurrence for any reason. Data regarding patients with no evidence of recurrence or death were censored at the date of last follow-up. 2.4 Statistical methods Metrological data were expressed as the mean (+ standard deviation) and categorized data by percentage (%). The Kaplan-Meier curve was used to describe the changes in survival outcomes, and the log-rank test was used to compare the differences in survival curves among groups. A Cox proportional risk regression model was used to adjust for case mix, determine independent risk factors, and calculate the relevant risk and 95% confidence interval. The statistical software used was SPSS 23.0 (SPSS Inc., Chicago, IL, USA). P < 0.050 was considered statistically significant. In the multivariate model, we also included the following known variables that may affect prognosis to reduce confounding effects: FIGO stage, age, histology, tumour size, depth of the stromal invasion, LVSI, lymph node metastasis, surgical margin invasion, parametrial tumour invasion, and postoperative adjuvant treatment. To control confounding factors, each patient in the first 50 patients with LRH group was matched to one patient in last 50 patients with LRH group using the known risk factors for recurrence or death using propensity score matching (PSM). 3. Results A total of 46 313 patients with cervical cancer were included from the large database on the clinical diagnosis and treatment of cervical cancer in China. 7670 patents received LRH, and there were 19 surgeons operating more than 100 LRH. The mean LRH of 19 surgeons is 201.2 ± 85.2 (range 102 to 375). According to the inclusion and exclusion criteria, 1004 patients with early stage cervical cancer were finally included. The data filtering process is shown in Fig. 1 . Median follow-up of censored cases was 35.3 months (interquartile range, 26.0–51). Clinico-pathologic characteristics of two groups before and post matching are shown in Table 1 . The first 50 patients with LRH group was younger than another group. In both groups, patients with IB1 stage disease accounted for most(66.1% vs. 65.0%). The first 50 patients with LRH group were more likely to have lower stage disease, whereas the last 50 patients with LRH group were more likely to have higher stage disease. Tumours in the first 50 patients with LRH group were less likely to have lymphovascular space invasion and deep stromal invasion (all, P < 0.05). Table 1 Clinical and pathological parameters of first 50 patients with LRH group and last 50 patients with LRH group before matched P after matched P Characteristic First 50 patients with LRH(n = 413) Last 50 patients with LRH(n = 591) First 50 patients with LRH(n = 364) Last 50 patientes with LRH(n = 364) Age(years) 46.3 ± 8.913 49.1 ± 9.637 < 0.001 47.1 ± 8.789 47.3 ± 9.137 0.701 FIGO stage < 0.001 0.191 IA 13(3.1%) 13(2.2%) 11(3.0%) 10(2.7%) IB1 273(66.1%) 384(65.0%) 257(70.6%) 268(73.6%) IB2 35(8.5%) 48(8.1%) 32(8.8%) 30(8.2%) IIA1 56(13.6%) 111(18.8%) 50(13.7%) 50(13.7%) IIA2 7(1.7%) 28(4.7%) 7(1.9%) 0(0.0%) IB/IIA 29(7.0%) 7(1.2%) 7(1.9%) 6(1.6%) Histologic type 0.307 0.732 Squamous cell carcinoma 368(89.1%) 509(86.1%) 320(87.9%) 313(86.0%) Adenocarcinoma 37(9.0%) 71(12.0%) 36(9.9%) 41(11.3%) Adenosquamous carcinoma 8(1.9%) 11(1.9%) 8(2.2%) 10(2.7%) Tumour size < 0.001 0.495 ≤ 4 cm 342(82.8%) 508(86.0%) 318(87.4%) 328(90.1%) ༞4 cm 42(10.2%) 76(12.9%) 39(10.7%) 30(8.2%) Unknows 29(7.0%) 7(1.2%) 7(1.9%) 6(1.6%) LVSI < 0.001 0.672 Negative 363(87.9%) 426(72.1%) 314(86.3%) 310(85.2%) Positive 50(12.1%) 165(27.9%) 50(13.7%) 54(14.8%) Stromal invasion depth 0.021 0.868 ≤ 1/2 202(48.9%) 237(40.1%) 175(48.1%) 181(49.7%) ༞1/2 176(42.6%) 298(50.4%) 158(43.4%) 155(42.6%) Unknows 35(8.5%) 56(9.5%) 31(8.5%) 28(7.7%) Parametrium 0.573 0.362 Negative 406(98.3%) 578(97.8%) 357(98.1%) 360(98.9%) Positive 7(1.7%) 13(2.2%) 7(1.9%) 4(1.1%) Surgical margin 0.135 0.704 Negative 404(97.8%) 585(99.0%) 361(99.2%) 360(98.9%) Positive 9(2.2%) 6(1.0%) 3(0.8%) 4(1.1%) pelvic lymph nodes 0.836 0.128 Negative 346(83.8%) 498(84.3%) 308(84.6%) 322(88.5%) Positive 67(16.2%) 93(15.7%) 56(15.4%) 42(11.5%) postoperative treatment 0.185 0.103 Standard treatment 187(45.3%) 305(51.6%) 170(46.7%) 187(51.4%) Insufficient treatment 52(12.6%) 75(12.7%) 42(11.5%) 27(7.4%) Over treatment 163(39.5%) 200(33.8%) 150(41.2%) 144(39.6%) Uncertain 11(2.7%) 11(1.9%) 2(0.5%) 6(1.6%) There was no difference in 5-year OS and DFS between the first 50 patients with LRH group (n = 413) and the last 50 patients with LRH group (n = 591) (OS: 94.6% vs 96.6%, P = 0.388; DFS: 87.6% vs 90.5%, P = 0.226) (see in Fig. 2 ). Cox multivariate analysis (see in Table 3 ) showed that surgical experience was not an independent risk factor for poor OS in patients with early stage cervical cancer (p = 0.830) or an independent risk factor for poor DFS (p = 0.300). Table 3 Association of Surgical Experience of LRH and Survival outcome of first 50 patients with LRH group and last 50 patients with LRH group (n = 1004) Parameters Multivariate analysis for 5-year OS Multivariate analysis for 5-year DFS HR 95.0% CI P HR 95%CI p Age(years) - - 0.703 - - 0.736 Surgeons' experience - - 0.830 - - 0.300 FIGO stage - - 0.447 - - 0.765 Histologic type - - 0.241 - 0.049 Squamous cell carcinoma 1(Ref) - - Adenocarcinoma - - - 1.471 0.775–2.793 0.238 Adenosquamous carcinoma - - - 3.209 1.164–8.845 0.024 Tumour size - - 0.419 - - 0.944 LVSI - - 0.042 - - 0.718 Negative 1(Ref) - - - - - Positive 2.252 1.030–4.927 0.042 - - - Stromal invasion depth - - 0.019 - - 0.011 ≤ 1/2 1(Ref) - - 1(Ref) - - > 1/2 3.954 1.465–10.669 0.007 2.208 1.296–3.761 0.004 unknows 4.253 1.136–15.924 0.032 2.227 1.011–4.909 0.047 Parametrium - - 0.159 - - 0.792 Surgical margin - - 0.413 - - 0.655 pelvic lymph nodes - - 0.159 2.685 1.661–4.340 0.000 postoperative treatment - - 0.903 - - 0.689 Multicollinearity test and cox proportional hazard regression models were used for analysis. Proportional hazard assumption was tested and showed no interaction with time. Bold indicates significant P-value. After propensity score matching, a total of 728 patients (364 in first 50 patients with LRH group and 364 in last 50 patients with LRH group) met the criteria for inclusion. The baseline data of patients after matching were consistent (Table 1 ). OS was 93.8% in group first 50 patients with LRH group and 96.9% in group last 50 patients with LRH group (see in Fig. 2 ). There was no significant difference in OS between the two groups (p = 0.764). Cox multivariate analysis (see in Table 4 ) showed that LRH surgical experience was not associated with OS in patients with early stage cervical cancer (p = 0.410). DFS was 87.2% in first 50 patients with LRH group and 91.8% in last 50 patients with LRH group. There was no significant difference in DFS between the two groups (p = 0.705). Cox multivariate analysis showed that the experience of LRH surgery was not correlated with DFS in patients with early stage cervical cancer (p = 0.522). Table 4 After matched, association of Surgical Experience of LRH and Survival outcome of first 50 patients with LRH group and last 50 patients with LRH group (n = 728) Parameters Multivariate analysis for 5-year OS Multivariate analysis for 5-year DFS HR 95.0% CI P HR 95%CI p Age(years) - - 0.264 - - 0.728 Surgeons' experience - - 0.767 - - 0.522 FIGO stage - - 0.546 - - 0.614 Histologic type - - 0.434 - - 0.034 Squamous cell carcinoma - - - 1(Ref) - - Adenocarcinoma - - - 1.584 0.772–3.247 0.209 Adenosquamous carcinoma - - - 3.543 1.266–9.919 0.016 Tumour size - - 0.018 - - 0.461 ≤ 4 cm 1(Ref) - - - ༞4 cm 1.160 0.332–4.047 0.816 - - - Unknows 6.141 1.744–21.621 0.005 - - - LVSI - - 0.073 - - 0.610 Stromal invasion depth - - 0.015 - - 0.008 ≤ 1/2 1(Ref) - - 1(Ref) - - > 1/2 4.334 1.422–13.213 0.010 2.421 1.310–4.474 0.005 unknows 6.325 1.576–25.380 0.009 3.133 1.268–7.740 0.013 Parametrium - - 0.508 - - 0.465 Surgical margin - - 0.563 - - 0.363 pelvic lymph nodes - - 0.334 3.030 1.717–5.347 0.000 postoperative treatment - - 0.731 - - 0.715 Multicollinearity test and cox proportional hazard regression models were used for analysis. Proportional hazard assumption was tested and showed no interaction with time. Bold indicates significant P-value. The same results were obtained from the subgroup analysis of standard postoperative adjuvant therapy(Fig. 3 ,Table 5 – 6 ). The baselines of FIGO staging, histological type, tumour size, LVSI, cervical stromal invation depth and surgical margin were not consistent between the two groups (Table 2 ). The OS was 95.6% in first 50 patients with LRH group (n = 187) and 95.8% in last 50 patients with LRH group (n = 305) before standard treatment (see in Fig. 3 ). There was no significant difference in OS between the two groups (p = 0.189). Cox multivariate analysis (see in Table 5 ) showed that LRH surgical experience was not associated with OS in patients with early stage cervical cancer (p = 0.189). DFS was 90.2% in first 50 patients with LRH group and 92.2% in last 50 patients with LRH group (see in Fig. 3 ). There was no significant difference in DFS between the two groups (p = 0.280). Cox multivariate analysis showed that LRH operation experience was not related to DFS in patients with early stage cervical cancer (p = 0.516). The similar result were obtained after matching (see in Table 2 and Table 6 ). Table 2 Clinical and pathological parameters of standart post-operative therapy subgroup of first and last 50 patients with LRH before matched P after matched P Characteristic First 50 patients with LRH(n = 187) Last 50 patients with LRH(n = 305) First 50 patients with LRH(n = 166) Last 50 patients with LRH(n = 166) Age(years) 46.9 ± 8.499 48.0 ± 9.901 0.221 46.9 ± 8.327 47.1 ± 9.753 0.799 FIGO stage 0.002 0.999 IA 11(5.9%) 8(2.6%) 6(3.6%) 7(4.2%) IB1 127(67.9%) 205(67.2%) 122(73.5%) 120(72.3%) IB2 20(10.7%) 29(9.5%) 18(10.8%) 18(10.8%) IIA1 18(9.6%) 40(13.1%) 16(9.6%) 16(9.6%) IIA2 4(2.1%) 22(7.2%) 3(1.8%) 4(2.4%) IB/IIA 7(3.7%) 1(0.3%) 1(0.6%) 1(0.6%) Histologic type 0.040 0.845 Squamous cell carcinoma 175(93.6%) 263(86.2%) 156(94.0%) 155(93.4%) Adenocarcinoma 10(5.3%) 36(11.8%) 9(5.4%) 9(5.4%) Adenosquamous carcinoma 2(1.1%) 6(2.0%) 1(0.6%) 2(1.2%) Tumour size 0.009 0.987 ≤ 4 cm 156(83.4%) 253(83.0%) 144(86.7%) 143(86.1%) ༞4 cm 24(12.8%) 51(16.7%) 21(12.7%) 22(13.3%) Unknows 7(3.7%) 1(0.3%) 1(0.6%) 1(0.6%) LVSI < 0.001 0.875 Negative 161(86.1%) 210(68.9%) 142(85.5%) 143(86.1%) Positive 26(13.9%) 95(31.1%) 24(14.5%) 23(13.9%) Stromal invasion depth 0.002 0.992 ≤ 1/2 110(58.8%) 144(47.2%) 99(59.6%) 100(60.2%) ༞1/2 56(29.9%) 139(45.6%) 50(30.1%) 49(29.5%) Unknows 21(11.2%) 22(7.2%) 17(10.2%) 17(10.2%) Parametrium 0.736 0.474 Negative 183(97.9%) 297(97.4%) 163(98.2%) 161(97.0%) Positive 4(2.1%) 8(2.6%) 3(1.8%) 5(3.0%) Surgical margin 0.030 1.000 Negative 181(96.8%) 303(99.3%) 164(98.8%) 164(98.8%) Positive 6(3.2%) 2(0.7%) 2(1.2%) 2(1.2%) pelvic lymph nodes 0.834 0.104 Negative 150(80.2%) 247(81.0%) 133(80.1%) 144(86.7%) Positive 37(19.8%) 58(19.0%) 33(19.9%) 22(13.3%) Legends Table 5 Association of Surgical Experience of LRH and Survival outcome of standart post-operative therapy subgroup of first and last 50 patients with LRH (n = 492) Parameters Multivariate analysis for 5-year OS Multivariate analysis for 5-year DFS HR 95.0% CI P HR 95%CI p Age(years) - - 0.945 - - 0.142 Surgeons' experience - - 0.162 - - 0.294 FIGO stage - - 0.263 - - 0.259 Histologic type - - 0.096 - - 0.008 Squamous cell carcinoma - - - 1(Ref) - - Adenocarcinoma - - - 2.575 0.956–6.934 0.061 Adenosquamous carcinoma - - - 8.067 1.818–35.788 0.006 Tumour size - - 0.142 - - 0.176 LVSI - - 0.801 - - 0.278 Stromal invasion depth - - 0.304 - - 0.079 Parametrium - - 0.500 - - 0.311 Surgical margin - - 0.601 - - 0.458 pelvic lymph nodes - - 0.015 - - 0.011 Negative 1(Ref) - - 1(Ref) - - Positive 4.651 1.344–16.089 0.015 2.765 1.262–6.061 0.011 Multicollinearity test and cox proportional hazard regression models were used for analysis. Proportional hazard assumption was tested and showed no interaction with time. Bold indicates significant P-value. Table 6 After matched, association of Surgical Experience of LRH and Survival outcome of standart post-operative therapy subgroup of first and last 50 patients with LRH (n = 332) Parameters Multivariate analysis for 5-year OS Multivariate analysis for 5-year DFS HR 95.0% CI P HR 95%CI p Age(years) - - 0.191 - - 0.876 Surgeons' experience - - 0.725 - - 0.604 FIGO stage - - 0.902 - - 0.580 Histologic type - - 0.055 - - 0.400 Tumour size - - 0.917 - - 0.739 LVSI - - 0.157 - - 0.057 Stromal invasion depth - - 0.743 - - 0.137 Parametrium - - 0.616 - - 0.516 Surgical margin - - 0.746 - - 0.634 pelvic lymph nodes - - 0.022 - - 0.084 Negative 1(Ref) - - - - - Positive 8.153 1.562–48.805 0.022 - - - postoperative treatment - - 0.191 - - 0.876 Multicollinearity test and cox proportional hazard regression models were used for analysis. Proportional hazard assumption was tested and showed no interaction with time. Bold indicates significant P-value. 4. Discussion Our study found that long-term oncological outcomes in patients with early stage cervical cancer did not improve significantly with increased surgical experience for high LRH volume surgeons. The inclusion and exclusion criteria are relatively loose in the general analysis, and the results could reflect the influence of surgeons’ LRH experience on survival outcomes of early stage cervical cancer patients. In matched cohort studies, the patients’ baseline data were more uniform than those in the RWD, and mixed bias could be effectively controlled. However, the external validity of the results was poor, which meant that the ability to generalize the results to other groups of patients with cervical cancer was limited. 21 , 22 In this study, general analysis and matched cohort studies were consistent and mutually validated, which improved the reliability of the results. 23 This further confirmed that the surgical experience of LRH surgeons was not related to the oncological outcomes of the patients. At present, there are no original articles to study the reasons for the worse laparoscopic oncologic outcome of women with cervical cancer from the perspective of the experience of surgeons, but after the publication of LACC study, some articles 9 – 12 discussed whether the experience of surgeons could be one of the reasons for the worse laparoscopic oncologic outcome of women with cervical cancer. However, previous studies analyzed fewer institutions, surgeons and fewer patients than this study. For example, Chong et al. 13 compared the earliest 50 patients with LRH of a surgeon in a single center with the next 50 patients with LRH in patients with locally advanced cervical cancer. With an increase in surgical experience, the operation time, hospitalization days, time to recover normal residual urine and blood transfusion decreased, the number of lymph nodes obtained increased, and the intraoperative and postoperative complications decreased significantly. For each group, the five-year OS rates were 96% and 92%, and the five-year DFS rates were 92% and 90%. The differences were not statistically significant, and the study did not provide a reason for the lack of significant differences in long-term oncological outcomes. However, our finding differ from another study of surgeon experience on prostate cancer. Vickers et al. 14 conducted a retrospective cohort study of 4702 patients with prostate cancer treated by laparoscopy. A total of 29 surgeons from 7 hospitals participated in the study. It was found that the 5-year recurrence rate was 16.3%, 11.0% and 7.1% (p = 0.038) for patients treated by surgeons with 10, 250 and 750 laparoscopic surgeries, respectively, and that the increase in surgical experience was associated with the oncological outcomes of patients with prostate cancer. Matsuo et al. 15 conducted a nationwide multicenter retrospective study on the RH surgical volume of early stage cervical cancer. A total of 5964 patients with stage IB1-IIB cervical cancer were enrolled from 2004 to 2008. It was found that RH surgical volume may be a prognostic factor for early stage cervical cancer. High surgical volume hospitals may be associated with a reduced risk for local recurrence and an improved survival rate. In this study, there were fewer than 32 RH operations in low volume hospitals, 32 to 104 RH operations in medium volume hospitals and more than 105 RH operations in high volume hospitals over a five-year period. However, the number of surgeries in hospitals could not fully equal to the experience of the surgeons. High-volume hospitals also have doctors with less experience in surgery, and low-volume hospitals also have surgeons with a high surgical volume. The lack of information on surgical approaches in this study means that researchers cannot adjust for the confounding factors of surgical approaches to oncological outcomes. Recent studies such as the study by Ramirez et al. 2 suggest that LRH is associated with poor oncological outcomes in patients with early stage cervical cancer. This study addresses the lack of research on surgeons’ experience and surgical approaches highlighted in Matsuo et al.. 15 Our study found that the oncological outcomes of patients with early stage cervical cancer was not affected by the LRH experience of surgeons. According to literatures on the learning curve of laparoscopic surgery for cervical cancer, surgeons need 20–50 surgeries to master laparoscopic surgery. 13,16−20 Therefore, number 50 was selected as the cutting point of surgical experience in this study to compare the oncologic outcomes of the first 50 surgeries and the last 50 surgeries of surgeons with more than 100 surgeries. Our database collected consecutive surgical cases (both open and laparoscopic) of more than 600 surgeons. We screened 7,670 patients who underwent laparoscopic QM-B and C2 hysterectomy. Among them, there are 19 surgeons having more than 100 LRH surgeries, so 1,900 patients with their first 50 and last 50 surgeries were included in this study. On the basis of these 1,900 patients, we strictly included and excluded the following criteria. Finally, 1004 patients were selected for oncologic outcome analysis, including 413 patients in the first 50 LRH group and 591 patients in the last 50 LRH group. Patients enrolled in this study received postoperative adjuvant therapy according to NCCN and FIGO guidelines. FIGO guideline in 2018 8 mentioned that postoperative adjuvant therapy was added to patients, with any two of three factors intermediate- risk prognostic factors or with any one of high-risk prognostic factors. The high-risk prognostic factors included positive surgical margins or lymph node metastases or parametrial spread, and the intermediate- risk prognostic factors included tumor size more than 4 cm, lymphovascular invasion, deep stromal invasion. This study is a retrospective study. The earliest cases started from 2004, and Sedlis standard was officially recommended by NCCN guideline in 2015 24 . Therefore, the standards adopted in this study are not completely consistent with Sedlis standard. The similar survival outcome were observed in both general and subgroup analysis, before or after matching. It might be explained by the following reason. The first cervical cancer surgery case of this study has been recorded since January 2004 when LRH had been introduced in mainland China not so long. Only few experienced surgeons with a high volume of ARH started LRH. Most of the nineteen surgeons in our study were those surgeons who were pioneers on LRH, their enriched surgical experience of ARH helped them to conquer the difficulty on transferring ARH to LRH, so they mastered LRH technique more easily. It might explain why the 5-year OS and DFS results in the first 50 patients with LRH group were not poorer than the last 50 patients with LRH group in this study. We acknowledge several limitations in this study. First, this study only analyzed the effect of LRH surgical experience on the efficacy of LRH, without considering the effect of ARH experience on the efficacy of LRH. Second, this study is a retrospective study, although multivariate analysis and propensity score matching could effectively control for known confounding factors, the impact of confounding factors on result could not be eliminated completely. Third, this study only analyzed the overall survival rate and disease-free survival rate but did not analyze the recurrence patterns, which requires further study. Fourth, there may be differences in the writing standards of cases and reports in different hospitals, leading to a lack of some clinical data. Fifth, in China, gynecologists perform laparoscopic radical hysterectomy with Wertheim-Meigs operation. Patients with stage IB1 to IIA2 cervical cancer underwent QM-C radical hysterectomy and pelvic lymph node dissection. Patients with stage IA1 with positive LVSI to IA2 cervical cancer underwent QM-B radical hysterectomy. However, this study is a retrospective study. It is a pity that this database lacks specific surgical procedures. Whether we used a uterine manipulation, or made a vaginal stuff is not available. 5. Conclusion The oncological outcomes of the first 50 surgeries performed by high-volume LRH surgeons were similar to those of the last 50 surgeries in patients with early stage cervical cancer. The long-term oncological outcomes of LRH in patients with stage IA1 with positive LVSI to stage IIA2 cervical cancer did not improve with an increase in surgeons’ experience. Declarations Ethics approval and consent to participate This retrospective study was approved by the Ethics Committee of the Nanfang Hospital of Southern Medical University (approval number NFEC-2017-135 and clinical trial number CHiCTR1800017778; International Clinical Trials Registry Platform Search Port, http://apps.who.int/trialsearch/); registration date: August 14, 2018. Written consent to participate in the study was provided by all patients. Consent for publication Not Applicable. Availability of data and material The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests to disclose. Funding The National Science and Technology Support Program of China (2014BAI05B03) The Natural Science Foundation of Guangdong Province(2015A030311024) The Science and Technology Plan of Guangzhou (158100075) The above funding sources provided financial assistance that had an important role in data collection. Authors' contributions Pengfei Li did the literature search, data collection, data analysis and interpretation, drafted and revised the manuscript Shan Kang conceived and designed the study, interpreted the data, developed and revised the manuscript. Jianxin Guo conceived and designed the study, interpreted the data, developed and revised the manuscript. Shiqi Liang did the literature search, data analysis and interpretation and figures, drafted and revised the manuscript. Ying Yang conceived, designed the study, interpreted the data. conceived, designed the study, interpreted the data. Shaoguang Wang conceived, designed the study, interpreted the data. Xiaonong Bin did the data analysis and interpretation. Jinghe Lang conceived, designed and supervised the study. Ping Liu conceived, designed and supervised the study. Chunlin Chen conceived, designed and supervised the study. All authors read and approved the final manuscript. Acknowledgements This study received funding of the National Science and Technology Support Program of China (2014BAI05B03), the National Natural Science Fund of Guangdong (2015A030311024), the Science and Technology Plan of Guangzhou (158100075). We thank Min Hao (The second hospital of ShanXi medical university), Bin Ling (China-Japan Friendship Hospital), Lixin Sun and Hongwei Zhao (Shanxi Cancer Hospital), Jihong Liu and Lizhi Liang (Sun Yat-sen University Cancer Center), Lihong Lin and Yu Guo (Anyang Tumor Hospital ), Li Wang (The Affiliated Tumor Hospital of Zhengzhou University), Weidong Zhao (Anhui Provincial Cancer Hospital), Yan Ni (The Yuncheng central hospital of Shanxi province), Wentong Liang and Donglin Li (Guizhou Provincial People`s Hospital), Xuemei Zhan and Mingwei Li (Jiangmen Central Hospital), Weifeng Zhang (Ningbo Women & Children's Hospital), Peiyan Du (The Affiliated Cancer Hospital and Institute of Guangzhou Medical University), Ziyu Fang (Liuzhou workers' hospital), Rui Yang (Shenzhen hospital of Peking University), Long Chen (Qingdao Municipal Hospital), Encheng Dai and Ruilei Liu (Linyi People's Hospital), Yuanli He and Mubiao Liu (Zhujiang Hospital, Southern Medical University), Jilong Yao and Zhihua Liu (Shenzhen Maternity & Child Health Hospital), Xueqin Wang (The Fifth Affiliated Hospital of Southern Medical University), Anwei Lu (Maternal and Child Health Hospital of Guiyang Province), Shuangling Jin (Peace Hospital affiliated to Changzhi Medical College), Ben Ma (Guangzhou First People's Hospital), Zhonghai Wang (Shenzhen Nanshan People's Hospital), Lin Zhu (The Second Hospital of Shandong University), Hongxin Pan (The Third Affiliated Hospital of Shenzhen University), Qianyong Zhu (No.153. Center Hospital of Liberation Army /Hospital No.988 of the Chinese People's Liberation Army Joint Support Force), Dingyuan Zeng and Zhong Lin (Maternal and Child Health Care Hospital of Liuzhou) , Xiaohong Wang (Laiwu People's Hospital/Jinan City People's Hospital) and Bin Zhu (The Affiliated Yiwu Women and Children Hospital of Hangzhou Medical College) for their contribution in data collection. Authors' information (optional) Corresponding author 1 Chunlin Chen, PhD Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, No. 1838 Guangzhou Avenue, Guangzhou 510515, China. E-mail address: [email protected] . Tel: +86 02062787562. ORCID：0000-0002-1708-3047 2 Ping Liu MD/PhD Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, No. 1838, Guangzhou Avenue, Guangzhou 510515, China. E-mail address: [email protected] . Tel: +86 02087705641. Contributed equally to this work 1 Pengfei Li ， MD/PhD Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China, Email: [email protected] 2 Shan Kang ， MD/PhD Department of Gynecology , Fourth Hospital，Hebei Medical University,Shijiazhuang 050019, China, Email: [email protected] 3 Jianxin Guo ， MD/PhD Department of Obstetrics and Gynecology，Daping hospital, Army Medical University, Chongqing, 400042,China, Email: [email protected] 4 Shiqi Liang ， MD Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China, Email: [email protected] Other authors Ying Yang ， MD/PhD Department of Obstetrics and Gynecology，Xinqiao Hospital，Army Medical University, Chongqing 400037, China, Email: [email protected] Shaoguang Wang ， MD/PhD Department of Gynecology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai 264000, China, Email: [email protected] Xiaonong Bin, BA Department of Epidemiology, College of Public Health, Guangzhou Medical University, Email: [email protected] Jinghe Lang , MD/PhD 1 Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China 2 Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China. Email: [email protected] References NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®). Cervical Cancer. Version 2.2019 - October 12, 2018. 286 https://www.nccn.org/professionals/physician_gls/pdf/cervical.pdf) . Ramirez PT, Frumovitz M, Pareja R, et al. Minimally Invasive versus Abdominal Radical Hysterectomy for Cervical Cancer. N Engl J Med. 2018;379(20):1895–904. doi: 10.1056/NEJMoa1806395 . Melamed A, Margul DJ, Chen L, et al. Survival after Minimally Invasive Radical Hysterectomy for Early-Stage Cervical Cancer. N Engl J Med. 2018;379(20):1905–14. doi: 10.1056/NEJMoa1804923 . Koh WJ, Abu-Rustum NR, Bean S, et al. Cervical Cancer, Version 3.2019, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2019;17(1):64–84. doi: 10.6004/jnccn.2019.0001 . Zhang W, Chen C, Liu P, et al. Staging early cervical cancer in China: data from a multicenter collaborative. Int J Gynecol Cancer. 2019. doi: 10.1136/ijgc-2019-000263 . Zhang W, Chen C, Liu P, Li W, et al. Impact of pelvic MRI in routine clinical practice on staging of IB1-IIA2 cervical cancer. Cancer Manag Res. 2019;11:3603–9. doi: 10.2147/CMAR.S197496 . Querleu D, Morrow CP. Classification of radical hysterectomy. Lancet Oncol. 2008;9(3):297–303. doi: 10.1016/S1470-2045(08)70074-3. Bhatla N, Aoki D, Sharma DN, Sankaranarayanan R. Cancer of the cervix uteri. International Journal of Gynecology Obstetrics. 2018;143:22–36. doi: 10.1002/ijgo.12611 . Kanao H, Aoki Y, Takeshima N. Unexpected result of minimally invasive surgery for cervical cancer. Journal of Gynecologic Oncology. 2018;29(4) 'doi': 10.3802/jgo.2018.29.e73 . Park J, Nam J. How should gynecologic oncologists react to the unexpected results of LACC trial? Journal of Gynecologic Oncology. 2018;29(4) 'doi': 10.3802/jgo.2018.29.e74 . Kimmig R, Ind T. Minimally invasive surgery for cervical cancer: consequences for treatment after LACC Study. Journal of Gynecologic Oncology. 2018;29(4) 'doi': 10.3802/jgo.2018.29.e75 . Vergote I, Magrina JF, Zanagnolo V, Magtibay PM, et al. The LACC Trial and Minimally Invasive Surgery in Cervical Cancer. Journal of Minimally Invasive Gynecology. 2019. 10.1016/j.jmig.2019.09.767 . 'doi':. Chong GO, Park NY, Hong DG, Cho YL, Park IS, Lee YS. Learning Curve of Laparoscopic Radical Hysterectomy With Pelvic and/or Para-Aortic Lymphadenectomy in the Early and Locally Advanced Cervical Cancer. Int J Gynecol Cancer. 2009;19(8):1459–64. doi: 10.1111/IGC.0b013e3181b76640 . Vickers AJ, Savage CJ, Hruza M, et al. The surgical learning curve for laparoscopic radical prostatectomy: a retrospective cohort study. Lancet Oncol. 2009;10(5):475–80. doi: 10.1016/S1470-2045(09)70079-8 . Matsuo K, Shimada M, Yamaguchi S, Matoda M, et al. Association of Radical Hysterectomy Surgical Volume and Survival for Early-Stage Cervical Cancer. Obstet Gynecol. 2019;133(6):1086–98. doi: 10.1097/AOG.0000000000003280 . Hwang JH, Yoo HJ, Joo J, Kim S, et al. Learning curve analysis of laparoscopic radical hysterectomy and lymph node dissection in early cervical cancer. European Journal of Obstetrics Gynecology Reproductive Biology. 2012;163(2):219–23. 10.1016/j.ejogrb.2012.05.005 . 'doi':. Chong GO, Lee YH, Hong DG, Cho YL, Park IS, Lee YS. Robot Versus Laparoscopic Nerve-Sparing Radical Hysterectomy for Cervical Cancer. Int J Gynecol Cancer. 2013;23(6):1146–50. 10.1097/IGC.0b013e31829a5db0 . 'doi':. Yim GW, Kim SW, Nam EJ, Kim S, Kim YT. Learning curve analysis of robot-assisted radical hysterectomy for cervical cancer: initial experience at a single institution. Journal of Gynecologic Oncology. 2013;24(4):303. 10.3802/jgo.2013.24.4.303 . 'doi':. Sandadi S, Gadzinski JA, Lee S, Chi DS, et al. Fellowship learning curve associated with completing a robotic assisted total laparoscopic hysterectomy. Gynecol Oncol. 2014;132(1):102–6. 10.1016/j.ygyno.2013.11.017 . 'doi':. Oyama K, Kanno K, Kojima R, Shirane A, et al. Short-term outcomes of robotic-assisted versus conventional laparoscopic radical hysterectomy for early-stage cervical cancer: A single-center study. Journal of Obstetrics Gynaecology Research. 2019;45(2):405–11. 10.1111/jog.13858 . 'doi':. Fleurence RL, Blake K, Shuren J. The Future of Registries in the Era of Real-world Evidence for Medical Devices. JAMA cardiology. 2019;4(3):197–8. doi: 10.1001/jamacardio.2018.4933 . Yuan H, Ali MS, Brouwer ES, et al. Real-World Evidence: What It Is and What It Can Tell Us According to the International Society for Pharmacoepidemiology (ISPE) Comparative Effectiveness Research (CER) Special Interest Group (SIG). Clin Pharmacol Ther. 2018;104(2):239–41. doi: 10.1002/cpt.1086 . Sherman RE, Anderson SA, Dal Pan GJ, Gray GW, et al. Real-World Evidence - What Is It and What Can It Tell Us? N Engl J Med. 2016;375(23):2293–7. doi: 10.1056/NEJMsb1609216 . Cervical Cancer, Version 2.2015. Journal of the National Comprehensive Cancer Network,13(4), 395–404 . doi:10.6004/jnccn.2015.0055. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About In Review Editorial Policies 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-26906","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":637829,"identity":"8d97b11d-5c55-4a45-bc78-1ec6b5ce0bd1","order_by":0,"name":"Pengfei Li","email":"","orcid":"","institution":"Southern Medical University Nanfang Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"Li","suffix":""},{"id":637830,"identity":"a9ab9929-5637-40fe-a6e3-8d70bf7aaf01","order_by":1,"name":"Shan Kang","email":"","orcid":"","institution":"Hebei Medical University Fourth Affiliated Hospital and Hebei Provincial Tumor Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shan","middleName":"","lastName":"Kang","suffix":""},{"id":637831,"identity":"7b6927a1-c9fe-4b3e-a226-e9cec6e11569","order_by":2,"name":"Jianxin Guo","email":"","orcid":"","institution":"Third Military Medical University Daping Hospital and Research Institute of Surgery","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jianxin","middleName":"","lastName":"Guo","suffix":""},{"id":637832,"identity":"e69f7cd8-b2a6-4ca3-889c-ba83874f1a94","order_by":3,"name":"Shiqi Liang","email":"","orcid":"","institution":"Southern Medical University Nanfang Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shiqi","middleName":"","lastName":"Liang","suffix":""},{"id":637833,"identity":"36802b02-30f0-4e73-aef5-78e6bc58a4fd","order_by":4,"name":"Ying Yang","email":"","orcid":"","institution":"Xinqiao Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Yang","suffix":""},{"id":637834,"identity":"6ca7ca2d-570a-4f77-9990-dd51e62ac4f5","order_by":5,"name":"Shaoguang Wang","email":"","orcid":"","institution":"Qindao University Medical College Affiliated Yantai Yuhuangding Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shaoguang","middleName":"","lastName":"Wang","suffix":""},{"id":637835,"identity":"ab3f7918-bd19-4b94-b57e-d61edda791c9","order_by":6,"name":"Xiaonong Bin","email":"","orcid":"","institution":"Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaonong","middleName":"","lastName":"Bin","suffix":""},{"id":637836,"identity":"e1db8231-c5f3-4235-a59a-107111919e78","order_by":7,"name":"Jinghe Lang","email":"","orcid":"","institution":"Peking Union Medical College Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jinghe","middleName":"","lastName":"Lang","suffix":""},{"id":637837,"identity":"4f8663b9-bde1-4649-8a1b-fb7a03c1d863","order_by":8,"name":"Ping Liu","email":"","orcid":"","institution":"Southern Medical University Nanfang Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Liu","suffix":""},{"id":637838,"identity":"91165f71-0b42-4b13-ab1b-c9fe8998ec25","order_by":9,"name":"Chunlin Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAoklEQVRIiWNgGAWjYFAC5oYDHyAsA2K1MDYenEGqlubDPCRp4W8/2HDY5s+2xAb25m0SDDV3CGuROJPYcDi37XZiA8+xMgmGY88IazGQYARqaQBqkcgxA7GJ1GLxB6hF/g0pWhjYQLbwEKkF5JeDvW23jdt40ootEo4RoYW//fDhDz/+3JbtZz+88caHGiK0wAEbiEggQcMoGAWjYBSMAjwAALyGPFiYtwX1AAAAAElFTkSuQmCC","orcid":"","institution":"Southern Medical University Nanfang Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Chunlin","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2020-05-03 14:02:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-26906/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-26906/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":1281300,"identity":"58596857-23bd-4176-a86f-dbc96e9c09c8","added_by":"auto","created_at":"2020-06-08 18:44:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1243860,"visible":true,"origin":"","legend":"Study population.","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-26906/v1/fig1.jpg"},{"id":1281301,"identity":"02f4019c-31a9-46ca-b8ec-a7730e5d7a04","added_by":"auto","created_at":"2020-06-08 18:44:06","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1006106,"visible":true,"origin":"","legend":"Survival analysis of the first 50 patients with LRH group and the last 50 patients with LRH group (A,B) and after propensity score matching(C,D).\nThe 5-year OS(A) and 5-year DFS(B) were not associated with surgeons’ experience while comparing the first 50 cases to last 50 cases(OS:94.6% vs 96.6%, p=0.388; DFS:87.6% vs 90.5%, p=0.226). After PSM, similar result were observed in the 5-year OS(C) and 5-year DFS(D) (93.8% vs 96.9%, P = 0.764; 87.2% vs 91.8%, P = 0.705).","description":"","filename":"FIG2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-26906/v1/FIG2.jpg"},{"id":1281302,"identity":"23d1d3e5-8161-4d8e-b82a-ad15e5440230","added_by":"auto","created_at":"2020-06-08 18:44:06","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2147380,"visible":true,"origin":"","legend":"Survival analysis of the first 50 patients with LRH group and the last 50 patients with LRH group in the subgroup of standard postoperative therapy(A,B) and after propensity score matching(C,D).\nFor patients received standard postoperative therapy after LRH, the 5-year OS(A) and 5-year DFS(B) were not associated with surgeons’ experience while comparing the first 50 cases to last 50 cases (OS: 95.6% vs 95.8%, P = 0.189; DFS: 90.2% vs 92.2%, P = 0.280). After PSM, similar result were observed in the 5-year OS(C) and 5-year DFS(D) (OS: 95.1% vs 98.4%, P = 0.952; DFS: 89.7% vs 93.9%, P = 0.674).","description":"","filename":"FIG3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-26906/v1/FIG3.jpg"},{"id":13537941,"identity":"4969b80d-6db2-4b3d-9951-1db687a54380","added_by":"auto","created_at":"2021-09-17 01:37:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1280552,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-26906/v1/4be429c9-2acf-4fbe-98d7-4fa233f8c0de.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eImpact of Surgeons’ Experience on Oncologic Outcomes in Women Undergoing Laparoscopic Radical Hysterectomy for Cervical Cancer: A Comparison of the First 50 and last 50 Cases\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":" \u003cp\u003eRadical hysterectomy in combination with pelvic lymphadenectomy is the standard treatment for non-fertility sparing stage IA1 cervical cancer with positive lymphovascular space invasion (LVSI) to stage IIA2 cervical cancer. NCCN guideline\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e used to indicate that laparotomy or minimally invasive surgery (MIS) are acceptable for patients with early stage cervical cancer. Due to these suggestions, MIS was widely used. However, in November 2018, Ramirez et al. \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e published a multicentre, phase III randomized controlled trial in the New England Journal of Medicine, which reported that laparoscopic radical hysterectomy(LRH) was associated with worse OS and DFS rates after 4.5\u0026nbsp;years in patients with early stage cervical cancer than was abdominal radical hysterectomy (ARH). In the same period, retrospective cohort data from Melamed et al.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e were also published, and LRH was reported to result in a shorter OS than ARH. These two studies overturned the traditional understanding of laparoscopic surgery in gynecologic oncology. \u003cem\u003eCervical Cancer Guidelines (Version 3.2019)\u003c/em\u003e released by NCCN Clinical Practice Guidelines in Oncology in 2019 \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e no longer recommends laparoscopic surgery as the preferred surgical approach for the treatment of early stage cervical cancer .\u003c/p\u003e \u003cp\u003eAlthough these two studies suggested that the long-term prognosis was poor for patients that underwent LRH with early stage cervical cancer, the study did not provide reasons for the poor efficacy of laparoscopy. Among many possible causes, surgical experience was often thought to influence oncological outcomes. Therefore, we suggest a hypothesis: was the surgeons\u0026rsquo; experience one of the reasons for the poor outcomes of LRH surgery? However, the previous studies on the learning curve of LRH for early stage cervical cancer focused on operative techniques such as operation time, bleeding volume, incidence of intraoperative and postoperative complications and perioperative outcomes, rarely focused on long-term oncological outcomes and ignored the influence of surgeons\u0026rsquo; experience on the long-term oncological outcomes of patients with early stage cervical cancer. In addition, in most of the studies, single-center, single-surgeon and small samples were included.\u003c/p\u003e \u003cp\u003eTo test this hypothesis, we explored whether the experience of surgeons affects the efficacy of LRH based on the clinical diagnosis and treatment of cervical cancer in China database. This study is a multicenter retrospective study. We compare the oncological outcomes of the first 50 LRH surgeries and the last 50 LRH surgeries of surgeons for patients with early stage cervical cancer.\u003c/p\u003e "},{"header":"2. Materials And Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Source\u003c/h2\u003e \u003cp\u003eThe data used in this study is from the Clinical Diagnosis and Treatment for Cervical Cancer in China (Four C) Database, which covers 46 313 patients with cervical cancer in 37 hospitals in mainland China from January 2004 to December 2016. The establishment of this database was approved by the Ethics Committee of the Southern Hospital of Southern Medical University. The ethical approval number is NEEC-2017-135, and the clinical research registration number is CHiCTR1800017778 (International Clinical Trials Registry Platform Search Port, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://apps.who.int/trialsearch/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll follow-up procedures were carried out by trained gynecological oncology staff at each centre to keep the patients' personal data confidential and to provide disease management guidance at the same time. The follow-up information was gathered through the return visit system or telephone follow-up, including survival status, time of death, recurrence time, recurrence site, treatment after recurrence. The oncological outcomes were estimated according to the recorded information, and the last day of the return visit or telephone follow-up was defined as the last follow-up. We follow-up all patients, but the follow-up rate of oncological outcomes was 72.7% in this database. Detailed data collection requirements and database establishment processes were described in our previously published study. \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eLaparoscopic cervical cancer surgery started to be conducted in all participating institutions after 2004,so the first LRH cases performed by surgeons after this year were included in this database. In this study, more than 600 surgeons were enrolled. In the database, 19 surgeons who completed more than 100 cases of LRH were enrolled. The operation cases were sorted according to the operation date. The first 50 patients with LRH were considered a group and the last 50 patients with LRH were considered another group. The patients were grouped according to the date of the operation, regardless of whether they met the inclusion or exclusion criteria.\u003c/p\u003e \u003cp\u003eIn this database, the FIGO stage was recorded and corrected by tumour size according to the FIGO 2009 staging system. Tumour size was evaluated using magnetic resonance imaging, computed tomography, ultrasound, physical examination or postoperative pathological records.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Inclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eThe patients\u0026rsquo; inclusion criteria were as follows: age 18\u0026nbsp;years or older; cervical cancer diagnosis of FIGO stage IA1 with positive LVSI to stage IIA2; squamous cell carcinoma, adenocarcinoma, or adenosquamous cell carcinoma; a history of laparoscopic surgery; type QM-B or QM-C \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e hysterectomy with pelvic lymphadenectomy with or without paraaortic lymphadenectomy; and a postoperative survival outcome. Surgeons who performed 100 or more LRH surgeries were also included, and we excluded patients if they had neoadjuvant chemotherapy or radiotherapy before surgery. Patients with pregnancy or other types of malignant tumours were excluded, too.\u003c/p\u003e \u003cp\u003eIn subgroup analyses, the standard postoperative adjuvant therapy was defined as cervical cancer patients with one or more high-risk factors (lymph node metastasis, parametrial involvement, and surgical margin invasion) or patients with two or more intermediate-risk factors (deep cervical stromal invasion, tumor size\u0026thinsp;\u0026gt;\u0026thinsp;4\u0026nbsp;cm, and LVSI were received radiotherapy or chemo-radiotherapy. \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Outcome measures\u003c/h2\u003e \u003cp\u003eThe overall 5-year survival rate (OS) and disease-free survival rate (DFS) were calculated. OS was defined as the time from diagnosis to death for any reason. DFS was defined as the time from diagnosis to death or recurrence for any reason. Data regarding patients with no evidence of recurrence or death were censored at the date of last follow-up.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical methods\u003c/h2\u003e \u003cp\u003eMetrological data were expressed as the mean (+\u0026thinsp;standard deviation) and categorized data by percentage (%). The Kaplan-Meier curve was used to describe the changes in survival outcomes, and the log-rank test was used to compare the differences in survival curves among groups. A Cox proportional risk regression model was used to adjust for case mix, determine independent risk factors, and calculate the relevant risk and 95% confidence interval. The statistical software used was SPSS 23.0 (SPSS Inc., Chicago, IL, USA). P\u0026thinsp;\u0026lt;\u0026thinsp;0.050 was considered statistically significant. In the multivariate model, we also included the following known variables that may affect prognosis to reduce confounding effects: FIGO stage, age, histology, tumour size, depth of the stromal invasion, LVSI, lymph node metastasis, surgical margin invasion, parametrial tumour invasion, and postoperative adjuvant treatment.\u003c/p\u003e \u003cp\u003eTo control confounding factors, each patient in the first 50 patients with LRH group was matched to one patient in last 50 patients with LRH group using the known risk factors for recurrence or death using propensity score matching (PSM).\u003c/p\u003e \u003c/div\u003e "},{"header":"3. Results","content":" \u003cp\u003eA total of 46 313 patients with cervical cancer were included from the large database on the clinical diagnosis and treatment of cervical cancer in China. 7670 patents received LRH, and there were 19 surgeons operating more than 100 LRH. The mean LRH of 19 surgeons is 201.2\u0026thinsp;\u0026plusmn;\u0026thinsp;85.2 (range 102 to 375). According to the inclusion and exclusion criteria, 1004 patients with early stage cervical cancer were finally included. The data filtering process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMedian follow-up of censored cases was 35.3 months (interquartile range, 26.0\u0026ndash;51). Clinico-pathologic characteristics of two groups before and post matching are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The first 50 patients with LRH group was younger than another group. In both groups, patients with IB1 stage disease accounted for most(66.1% vs. 65.0%). The first 50 patients with LRH group were more likely to have lower stage disease, whereas the last 50 patients with LRH group were more likely to have higher stage disease. Tumours in the first 50 patients with LRH group were less likely to have lymphovascular space invasion and deep stromal invasion (all, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical and pathological parameters of first 50 patients with LRH group and last 50 patients with LRH group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ebefore matched\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eafter matched\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFirst 50 patients with LRH(n\u0026thinsp;=\u0026thinsp;413)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLast 50 patients with LRH(n\u0026thinsp;=\u0026thinsp;591)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eFirst 50 patients with LRH(n\u0026thinsp;=\u0026thinsp;364)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eLast 50 patientes with LRH(n\u0026thinsp;=\u0026thinsp;364)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge(years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e46.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.913\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e49.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.637\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFIGO stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11(3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10(2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e273(66.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e384(65.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e257(70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e268(73.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35(8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32(8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30(8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIIA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56(13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111(18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50(13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50(13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIIA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIB/IIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6(1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistologic type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e368(89.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e509(86.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e320(87.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e313(86.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71(12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36(9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41(11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenosquamous carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8(2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10(2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumour size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4\u0026nbsp;cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e342(82.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e508(86.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e318(87.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e328(90.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e༞4\u0026nbsp;cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76(12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39(10.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30(8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6(1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLVSI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e363(87.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e426(72.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e314(86.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e310(85.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50(12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165(27.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50(13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54(14.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStromal invasion depth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202(48.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e237(40.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e175(48.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e181(49.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e༞1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176(42.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e298(50.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e158(43.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e155(42.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35(8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31(8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28(7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParametrium\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e406(98.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e578(97.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e357(98.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e360(98.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(1.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgical margin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e404(97.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e585(99.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e361(99.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e360(98.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(0.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epelvic lymph nodes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e346(83.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e498(84.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e308(84.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e322(88.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67(16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93(15.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56(15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42(11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epostoperative treatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187(45.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e305(51.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e170(46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e187(51.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsufficient treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52(12.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75(12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42(11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOver treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163(39.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200(33.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e150(41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e144(39.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUncertain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6(1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere was no difference in 5-year OS and DFS between the first 50 patients with LRH group (n\u0026thinsp;=\u0026thinsp;413) and the last 50 patients with LRH group (n\u0026thinsp;=\u0026thinsp;591) (OS: 94.6% vs 96.6%, P\u0026thinsp;=\u0026thinsp;0.388; DFS: 87.6% vs 90.5%, P\u0026thinsp;=\u0026thinsp;0.226) (see in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Cox multivariate analysis (see in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e) showed that surgical experience was not an independent risk factor for poor OS in patients with early stage cervical cancer (p\u0026thinsp;=\u0026thinsp;0.830) or an independent risk factor for poor DFS (p\u0026thinsp;=\u0026thinsp;0.300).\u003c/p\u003e \u003cp\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eAssociation of Surgical Experience of LRH and Survival\u003c/b\u003e outcome of first 50 patients with LRH group and last 50 patients with LRH group (n\u0026thinsp;=\u0026thinsp;1004)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMultivariate analysis for 5-year OS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis for 5-year DFS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95.0% CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e95%CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge(years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgeons' experience\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFIGO stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistologic type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.775\u0026ndash;2.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenosquamous carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.164\u0026ndash;8.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumour size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLVSI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.030\u0026ndash;4.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStromal invasion depth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.465\u0026ndash;10.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.296\u0026ndash;3.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eunknows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.136\u0026ndash;15.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.011\u0026ndash;4.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParametrium\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgical margin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epelvic lymph nodes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.661\u0026ndash;4.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epostoperative treatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eMulticollinearity test and cox proportional hazard regression models were used for analysis.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eProportional hazard assumption was tested and showed no interaction with time.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBold indicates significant P-value.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter propensity score matching, a total of 728 patients (364 in first 50 patients with LRH group and 364 in last 50 patients with LRH group) met the criteria for inclusion. The baseline data of patients after matching were consistent (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). OS was 93.8% in group first 50 patients with LRH group and 96.9% in group last 50 patients with LRH group (see in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There was no significant difference in OS between the two groups (p\u0026thinsp;=\u0026thinsp;0.764). Cox multivariate analysis (see in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e) showed that LRH surgical experience was not associated with OS in patients with early stage cervical cancer (p\u0026thinsp;=\u0026thinsp;0.410). DFS was 87.2% in first 50 patients with LRH group and 91.8% in last 50 patients with LRH group. There was no significant difference in DFS between the two groups (p\u0026thinsp;=\u0026thinsp;0.705). Cox multivariate analysis showed that the experience of LRH surgery was not correlated with DFS in patients with early stage cervical cancer (p\u0026thinsp;=\u0026thinsp;0.522).\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAfter matched, association of Surgical Experience of LRH and Survival outcome of first 50 patients with LRH group and last 50 patients with LRH group (n\u0026thinsp;=\u0026thinsp;728)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMultivariate analysis for 5-year OS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis for 5-year DFS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95.0% CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e95%CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge(years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgeons' experience\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFIGO stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistologic type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.772\u0026ndash;3.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenosquamous carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.266\u0026ndash;9.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumour size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4\u0026nbsp;cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(Ref)\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e༞4\u0026nbsp;cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.332\u0026ndash;4.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.744\u0026ndash;21.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLVSI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.610\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStromal invasion depth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.422\u0026ndash;13.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.310\u0026ndash;4.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eunknows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.576\u0026ndash;25.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.268\u0026ndash;7.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParametrium\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgical margin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epelvic lymph nodes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.717\u0026ndash;5.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epostoperative treatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eMulticollinearity test and cox proportional hazard regression models were used for analysis.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eProportional hazard assumption was tested and showed no interaction with time.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBold indicates significant P-value.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe same results were obtained from the subgroup analysis of standard postoperative adjuvant therapy(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e,Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The baselines of FIGO staging, histological type, tumour size, LVSI, cervical stromal invation depth and surgical margin were not consistent between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The OS was 95.6% in first 50 patients with LRH group (n\u0026thinsp;=\u0026thinsp;187) and 95.8% in last 50 patients with LRH group (n\u0026thinsp;=\u0026thinsp;305) before standard treatment (see in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). There was no significant difference in OS between the two groups (p\u0026thinsp;=\u0026thinsp;0.189). Cox multivariate analysis (see in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) showed that LRH surgical experience was not associated with OS in patients with early stage cervical cancer (p\u0026thinsp;=\u0026thinsp;0.189). DFS was 90.2% in first 50 patients with LRH group and 92.2% in last 50 patients with LRH group (see in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). There was no significant difference in DFS between the two groups (p\u0026thinsp;=\u0026thinsp;0.280). Cox multivariate analysis showed that LRH operation experience was not related to DFS in patients with early stage cervical cancer (p\u0026thinsp;=\u0026thinsp;0.516). The similar result were obtained after matching (see in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical and pathological parameters of standart post-operative therapy subgroup of first and last 50 patients with LRH\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ebefore matched\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eafter matched\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFirst 50 patients with LRH(n\u0026thinsp;=\u0026thinsp;187)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eLast 50 patients with LRH(n\u0026thinsp;=\u0026thinsp;305)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eFirst 50 patients with LRH(n\u0026thinsp;=\u0026thinsp;166)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eLast 50 patients with LRH(n\u0026thinsp;=\u0026thinsp;166)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge(years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e46.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.499\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e48.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.901\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.221\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e46.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.327\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e47.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.753\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.799\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFIGO stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6(3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7(4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127(67.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205(67.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e122(73.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e120(72.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(10.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18(10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18(10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIIA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16(9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16(9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIIA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIB/IIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistologic type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e175(93.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e263(86.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e156(94.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e155(93.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(11.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9(5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenosquamous carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(2.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumour size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4\u0026nbsp;cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156(83.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e253(83.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144(86.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e143(86.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e༞4\u0026nbsp;cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51(16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21(12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1(0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLVSI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161(86.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210(68.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142(85.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e143(86.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95(31.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24(14.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23(13.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStromal invasion depth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.992\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110(58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144(47.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99(59.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100(60.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e༞1/2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56(29.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139(45.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50(30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49(29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(11.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17(10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17(10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParametrium\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183(97.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e297(97.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e163(98.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e161(97.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(2.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5(3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgical margin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e181(96.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e303(99.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e164(98.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e164(98.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epelvic lymph nodes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150(80.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e247(81.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e133(80.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e144(86.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37(19.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58(19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33(19.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22(13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eLegends\u003c/b\u003e\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=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eAssociation of Surgical Experience of LRH and Survival\u003c/b\u003e outcome of standart post-operative therapy subgroup of first and last 50 patients with LRH (n\u0026thinsp;=\u0026thinsp;492)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMultivariate analysis for 5-year OS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis for 5-year DFS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95.0% CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e95%CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge(years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgeons' experience\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFIGO stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistologic type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.956\u0026ndash;6.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenosquamous carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.818\u0026ndash;35.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumour size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLVSI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStromal invasion depth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParametrium\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgical margin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epelvic lymph nodes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.344\u0026ndash;16.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.262\u0026ndash;6.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eMulticollinearity test and cox proportional hazard regression models were used for analysis.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eProportional hazard assumption was tested and showed no interaction with time.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBold indicates significant P-value.\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=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eAfter matched, association of Surgical Experience of LRH and Survival outcome of\u003c/b\u003e standart post-operative therapy subgroup of first and last 50 patients with LRH \u003cb\u003e(n\u0026thinsp;=\u0026thinsp;332)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMultivariate analysis for 5-year OS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis for 5-year DFS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95.0% CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e95%CI\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge(years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgeons' experience\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFIGO stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistologic type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTumour size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLVSI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStromal invasion depth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParametrium\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgical margin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epelvic lymph nodes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.562\u0026ndash;48.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epostoperative treatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eMulticollinearity test and cox proportional hazard regression models were used for analysis.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eProportional hazard assumption was tested and showed no interaction with time.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBold indicates significant P-value.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"4. Discussion","content":" \u003cp\u003eOur study found that long-term oncological outcomes in patients with early stage cervical cancer did not improve significantly with increased surgical experience for high LRH volume surgeons.\u003c/p\u003e \u003cp\u003eThe inclusion and exclusion criteria are relatively loose in the general analysis, and the results could reflect the influence of surgeons\u0026rsquo; LRH experience on survival outcomes of early stage cervical cancer patients. In matched cohort studies, the patients\u0026rsquo; baseline data were more uniform than those in the RWD, and mixed bias could be effectively controlled. However, the external validity of the results was poor, which meant that the ability to generalize the results to other groups of patients with cervical cancer was limited.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e In this study, general analysis and matched cohort studies were consistent and mutually validated, which improved the reliability of the results.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e This further confirmed that the surgical experience of LRH surgeons was not related to the oncological outcomes of the patients.\u003c/p\u003e \u003cp\u003eAt present, there are no original articles to study the reasons for the worse laparoscopic oncologic outcome of women with cervical cancer from the perspective of the experience of surgeons, but after the publication of LACC study, some articles \u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e discussed whether the experience of surgeons could be one of the reasons for the worse laparoscopic oncologic outcome of women with cervical cancer. However, previous studies analyzed fewer institutions, surgeons and fewer patients than this study. For example, Chong et al. \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e compared the earliest 50 patients with LRH of a surgeon in a single center with the next 50 patients with LRH in patients with locally advanced cervical cancer. With an increase in surgical experience, the operation time, hospitalization days, time to recover normal residual urine and blood transfusion decreased, the number of lymph nodes obtained increased, and the intraoperative and postoperative complications decreased significantly. For each group, the five-year OS rates were 96% and 92%, and the five-year DFS rates were 92% and 90%. The differences were not statistically significant, and the study did not provide a reason for the lack of significant differences in long-term oncological outcomes. However, our finding differ from another study of surgeon experience on prostate cancer. Vickers et al.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e conducted a retrospective cohort study of 4702 patients with prostate cancer treated by laparoscopy. A total of 29 surgeons from 7 hospitals participated in the study. It was found that the 5-year recurrence rate was 16.3%, 11.0% and 7.1% (p\u0026thinsp;=\u0026thinsp;0.038) for patients treated by surgeons with 10, 250 and 750 laparoscopic surgeries, respectively, and that the increase in surgical experience was associated with the oncological outcomes of patients with prostate cancer.\u003c/p\u003e \u003cp\u003eMatsuo et al. \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e conducted a nationwide multicenter retrospective study on the RH surgical volume of early stage cervical cancer. A total of 5964 patients with stage IB1-IIB cervical cancer were enrolled from 2004 to 2008. It was found that RH surgical volume may be a prognostic factor for early stage cervical cancer. High surgical volume hospitals may be associated with a reduced risk for local recurrence and an improved survival rate. In this study, there were fewer than 32 RH operations in low volume hospitals, 32 to 104 RH operations in medium volume hospitals and more than 105 RH operations in high volume hospitals over a five-year period. However, the number of surgeries in hospitals could not fully equal to the experience of the surgeons. High-volume hospitals also have doctors with less experience in surgery, and low-volume hospitals also have surgeons with a high surgical volume. The lack of information on surgical approaches in this study means that researchers cannot adjust for the confounding factors of surgical approaches to oncological outcomes. Recent studies such as the study by Ramirez et al.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e suggest that LRH is associated with poor oncological outcomes in patients with early stage cervical cancer. This study addresses the lack of research on surgeons\u0026rsquo; experience and surgical approaches highlighted in Matsuo et al.. \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur study found that the oncological outcomes of patients with early stage cervical cancer was not affected by the LRH experience of surgeons. According to literatures on the learning curve of laparoscopic surgery for cervical cancer, surgeons need 20\u0026ndash;50 surgeries to master laparoscopic surgery. \u003csup\u003e13,16\u0026minus;20\u003c/sup\u003e Therefore, number 50 was selected as the cutting point of surgical experience in this study to compare the oncologic outcomes of the first 50 surgeries and the last 50 surgeries of surgeons with more than 100 surgeries. Our database collected consecutive surgical cases (both open and laparoscopic) of more than 600 surgeons. We screened 7,670 patients who underwent laparoscopic QM-B and C2 hysterectomy. Among them, there are 19 surgeons having more than 100 LRH surgeries, so 1,900 patients with their first 50 and last 50 surgeries were included in this study. On the basis of these 1,900 patients, we strictly included and excluded the following criteria. Finally, 1004 patients were selected for oncologic outcome analysis, including 413 patients in the first 50 LRH group and 591 patients in the last 50 LRH group.\u003c/p\u003e \u003cp\u003ePatients enrolled in this study received postoperative adjuvant therapy according to NCCN and FIGO guidelines. FIGO guideline in 2018 \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e mentioned that postoperative adjuvant therapy was added to patients, with any two of three factors intermediate- risk prognostic factors or with any one of high-risk prognostic factors. The high-risk prognostic factors included positive surgical margins or lymph node metastases or parametrial spread, and the intermediate- risk prognostic factors included tumor size more than 4\u0026nbsp;cm, lymphovascular invasion, deep stromal invasion. This study is a retrospective study. The earliest cases started from 2004, and Sedlis standard was officially recommended by NCCN guideline in 2015 \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Therefore, the standards adopted in this study are not completely consistent with Sedlis standard.\u003c/p\u003e \u003cp\u003eThe similar survival outcome were observed in both general and subgroup analysis, before or after matching. It might be explained by the following reason. The first cervical cancer surgery case of this study has been recorded since January 2004 when LRH had been introduced in mainland China not so long. Only few experienced surgeons with a high volume of ARH started LRH. Most of the nineteen surgeons in our study were those surgeons who were pioneers on LRH, their enriched surgical experience of ARH helped them to conquer the difficulty on transferring ARH to LRH, so they mastered LRH technique more easily. It might explain why the 5-year OS and DFS results in the first 50 patients with LRH group were not poorer than the last 50 patients with LRH group in this study.\u003c/p\u003e \u003cp\u003eWe acknowledge several limitations in this study. First, this study only analyzed the effect of LRH surgical experience on the efficacy of LRH, without considering the effect of ARH experience on the efficacy of LRH. Second, this study is a retrospective study, although multivariate analysis and propensity score matching could effectively control for known confounding factors, the impact of confounding factors on result could not be eliminated completely. Third, this study only analyzed the overall survival rate and disease-free survival rate but did not analyze the recurrence patterns, which requires further study. Fourth, there may be differences in the writing standards of cases and reports in different hospitals, leading to a lack of some clinical data. Fifth, in China, gynecologists perform laparoscopic radical hysterectomy with Wertheim-Meigs operation. Patients with stage IB1 to IIA2 cervical cancer underwent QM-C radical hysterectomy and pelvic lymph node dissection. Patients with stage IA1 with positive LVSI to IA2 cervical cancer underwent QM-B radical hysterectomy. However, this study is a retrospective study. It is a pity that this database lacks specific surgical procedures. Whether we used a uterine manipulation, or made a vaginal stuff is not available.\u003c/p\u003e "},{"header":"5. Conclusion","content":" \u003cp\u003eThe oncological outcomes of the first 50 surgeries performed by high-volume LRH surgeons were similar to those of the last 50 surgeries in patients with early stage cervical cancer. The long-term oncological outcomes of LRH in patients with stage IA1 with positive LVSI to stage IIA2 cervical cancer did not improve with an increase in surgeons\u0026rsquo; experience.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was approved by the Ethics Committee of the Nanfang Hospital of Southern Medical University (approval number NFEC-2017-135 and clinical trial number CHiCTR1800017778; International Clinical Trials Registry Platform Search Port, http://apps.who.int/trialsearch/); registration date: August 14, 2018. Written consent to participate in the study was provided by all patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Science and Technology Support Program of China (2014BAI05B03)\u003c/p\u003e\n\u003cp\u003eThe Natural Science Foundation of Guangdong Province(2015A030311024)\u003c/p\u003e\n\u003cp\u003eThe Science and Technology Plan of Guangzhou (158100075)\u003c/p\u003e\n\u003cp\u003eThe above funding sources provided financial assistance that had an important role in data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003ePengfei Li did the literature search, data collection, data analysis and interpretation, drafted and revised the manuscript\u003c/li\u003e\n\u003cli\u003eShan Kang conceived and designed the study, interpreted the data, developed and revised the manuscript.\u003c/li\u003e\n\u003cli\u003eJianxin Guo conceived and designed the study, interpreted the data, developed and revised the manuscript.\u003c/li\u003e\n\u003cli\u003eShiqi Liang did the literature search, data analysis and interpretation and figures, drafted and revised the manuscript.\u003c/li\u003e\n\u003cli\u003eYing Yang conceived, designed the study, interpreted the data. conceived, designed the study, interpreted the data.\u003c/li\u003e\n\u003cli\u003eShaoguang Wang conceived, designed the study, interpreted the data.\u003c/li\u003e\n\u003cli\u003eXiaonong Bin did the data analysis and interpretation.\u003c/li\u003e\n\u003cli\u003eJinghe Lang conceived, designed and supervised the study.\u003c/li\u003e\n\u003cli\u003ePing Liu conceived, designed and supervised the study.\u003c/li\u003e\n\u003cli\u003eChunlin Chen conceived, designed and supervised the study.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received funding of the National Science and Technology Support Program of China (2014BAI05B03), the National Natural Science Fund of Guangdong (2015A030311024), the Science and Technology Plan of Guangzhou (158100075). We thank Min Hao (The second hospital of ShanXi medical university), Bin Ling (China-Japan Friendship Hospital), Lixin Sun and Hongwei Zhao (Shanxi Cancer Hospital), Jihong Liu and Lizhi Liang (Sun Yat-sen\u0026nbsp;University\u0026nbsp;Cancer Center), Lihong Lin and Yu Guo (Anyang Tumor Hospital ), Li Wang (The Affiliated Tumor Hospital of Zhengzhou University), Weidong Zhao (Anhui Provincial Cancer Hospital), Yan Ni (The Yuncheng central hospital of Shanxi province), Wentong Liang and Donglin Li (Guizhou Provincial People`s Hospital), Xuemei Zhan and Mingwei Li (Jiangmen Central Hospital), Weifeng Zhang (Ningbo Women \u0026amp; Children's Hospital), Peiyan Du (The Affiliated Cancer Hospital and Institute of Guangzhou Medical University), Ziyu Fang (Liuzhou workers' hospital), Rui Yang (Shenzhen hospital of Peking University), Long Chen (Qingdao Municipal Hospital), Encheng Dai and Ruilei Liu (Linyi People's Hospital), Yuanli He and Mubiao Liu (Zhujiang Hospital, Southern Medical University), Jilong Yao and Zhihua Liu (Shenzhen Maternity \u0026amp; Child Health Hospital), Xueqin Wang (The Fifth Affiliated Hospital of Southern Medical University), Anwei Lu (Maternal and Child Health Hospital of Guiyang Province), Shuangling Jin (Peace Hospital affiliated to Changzhi Medical College), Ben Ma (Guangzhou First People's Hospital), Zhonghai Wang (Shenzhen Nanshan People's Hospital), Lin Zhu (The Second Hospital of Shandong University), Hongxin Pan (The Third Affiliated Hospital of Shenzhen University), Qianyong Zhu (No.153. Center Hospital of Liberation Army /Hospital No.988 of the Chinese People's Liberation Army Joint Support Force), Dingyuan Zeng and Zhong Lin (Maternal and Child Health Care Hospital of Liuzhou) , Xiaohong Wang (Laiwu People's Hospital/Jinan City People's Hospital) and Bin Zhu (The Affiliated Yiwu Women and Children Hospital of Hangzhou Medical College) for their contribution in data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information (optional) \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Chunlin Chen, PhD \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, No. 1838 Guangzhou Avenue, Guangzhou 510515, China. E-mail address: [email protected]. Tel: +86 02062787562. ORCID：0000-0002-1708-3047\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2\u0026nbsp;\u0026nbsp; Ping Liu \u003c/strong\u003e\u003cstrong\u003eMD/PhD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, No. 1838, Guangzhou Avenue, Guangzhou 510515, China. E-mail address: [email protected]. Tel: +86 02087705641.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributed equally to this work\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003cstrong\u003ePengfei Li\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003eMD/PhD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China, Email: [email protected]\u003c/p\u003e\n\u003cp\u003e2\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003cstrong\u003eShan Kang\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003eMD/PhD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Gynecology , Fourth Hospital，Hebei Medical University,Shijiazhuang 050019, China, Email: [email protected]\u003c/p\u003e\n\u003cp\u003e3\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003cstrong\u003eJianxin Guo\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003eMD/PhD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Obstetrics and Gynecology，Daping hospital, Army Medical University, Chongqing, 400042,China, Email: [email protected]\u003c/p\u003e\n\u003cp\u003e4\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003cstrong\u003eShiqi Liang\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003eMD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China, Email: [email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOther authors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYing Yang\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003eMD/PhD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Obstetrics and Gynecology，Xinqiao Hospital，Army Medical University, Chongqing 400037, China, Email: \u003ca href=\"mailto:[email protected]\"\[email protected]\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShaoguang Wang\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003eMD/PhD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Gynecology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai 264000, China, Email: [email protected]\u003c/p\u003e\n\u003cp\u003eXiaonong Bin, BA\u003c/p\u003e\n\u003cp\u003eDepartment of Epidemiology, College of Public Health, Guangzhou Medical University, Email: \u003ca href=\"mailto:[email protected]\"\[email protected]\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJinghe Lang\u003c/strong\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cstrong\u003eMD/PhD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1 Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China\u003c/p\u003e\n\u003cp\u003e2 Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China.\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e \u003cspan\u003eNCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines\u0026reg;). Cervical Cancer. Version 2.2019 - October 12, 2018. 286 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nccn.org/professionals/physician_gls/pdf/cervical.pdf)\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eRamirez PT, Frumovitz M, Pareja R, et al. Minimally Invasive versus Abdominal Radical Hysterectomy for Cervical Cancer. N Engl J Med. 2018;379(20):1895\u0026ndash;904. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa1806395\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eMelamed A, Margul DJ, Chen L, et al. Survival after Minimally Invasive Radical Hysterectomy for Early-Stage Cervical Cancer. N Engl J Med. 2018;379(20):1905\u0026ndash;14. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa1804923\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eKoh WJ, Abu-Rustum NR, Bean S, et al. Cervical Cancer, Version 3.2019, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2019;17(1):64\u0026ndash;84. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.6004/jnccn.2019.0001\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eZhang W, Chen C, Liu P, et al. Staging early cervical cancer in China: data from a multicenter collaborative. Int J Gynecol Cancer. 2019. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/ijgc-2019-000263\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eZhang W, Chen C, Liu P, Li W, et al. Impact of pelvic MRI in routine clinical practice on staging of IB1-IIA2 cervical cancer. Cancer Manag Res. 2019;11:3603\u0026ndash;9. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/CMAR.S197496\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eQuerleu D, Morrow CP. Classification of radical hysterectomy. Lancet Oncol. 2008;9(3):297\u0026ndash;303. doi: 10.1016/S1470-2045(08)70074-3.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eBhatla N, Aoki D, Sharma DN, Sankaranarayanan R. Cancer of the cervix uteri. International Journal of Gynecology Obstetrics. 2018;143:22\u0026ndash;36. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ijgo.12611\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eKanao H, Aoki Y, Takeshima N. Unexpected result of minimally invasive surgery for cervical cancer. Journal of Gynecologic Oncology. 2018;29(4) 'doi': \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3802/jgo.2018.29.e73\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003ePark J, Nam J. How should gynecologic oncologists react to the unexpected results of LACC trial? Journal of Gynecologic Oncology. 2018;29(4) 'doi': \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3802/jgo.2018.29.e74\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eKimmig R, Ind T. Minimally invasive surgery for cervical cancer: consequences for treatment after LACC Study. Journal of Gynecologic Oncology. 2018;29(4) 'doi': \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3802/jgo.2018.29.e75\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eVergote I, Magrina JF, Zanagnolo V, Magtibay PM, et al. The LACC Trial and Minimally Invasive Surgery in Cervical Cancer. Journal of Minimally Invasive Gynecology. 2019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jmig.2019.09.767\u003c/span\u003e\u003c/span\u003e. 'doi':.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eChong GO, Park NY, Hong DG, Cho YL, Park IS, Lee YS. Learning Curve of Laparoscopic Radical Hysterectomy With Pelvic and/or Para-Aortic Lymphadenectomy in the Early and Locally Advanced Cervical Cancer. Int J Gynecol Cancer. 2009;19(8):1459\u0026ndash;64. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/IGC.0b013e3181b76640\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eVickers AJ, Savage CJ, Hruza M, et al. The surgical learning curve for laparoscopic radical prostatectomy: a retrospective cohort study. Lancet Oncol. 2009;10(5):475\u0026ndash;80. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S1470-2045(09)70079-8\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eMatsuo K, Shimada M, Yamaguchi S, Matoda M, et al. Association of Radical Hysterectomy Surgical Volume and Survival for Early-Stage Cervical Cancer. Obstet Gynecol. 2019;133(6):1086\u0026ndash;98. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/AOG.0000000000003280\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eHwang JH, Yoo HJ, Joo J, Kim S, et al. Learning curve analysis of laparoscopic radical hysterectomy and lymph node dissection in early cervical cancer. European Journal of Obstetrics Gynecology Reproductive Biology. 2012;163(2):219\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ejogrb.2012.05.005\u003c/span\u003e\u003c/span\u003e. 'doi':.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eChong GO, Lee YH, Hong DG, Cho YL, Park IS, Lee YS. Robot Versus Laparoscopic Nerve-Sparing Radical Hysterectomy for Cervical Cancer. Int J Gynecol Cancer. 2013;23(6):1146\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/IGC.0b013e31829a5db0\u003c/span\u003e\u003c/span\u003e. 'doi':.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eYim GW, Kim SW, Nam EJ, Kim S, Kim YT. Learning curve analysis of robot-assisted radical hysterectomy for cervical cancer: initial experience at a single institution. Journal of Gynecologic Oncology. 2013;24(4):303. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3802/jgo.2013.24.4.303\u003c/span\u003e\u003c/span\u003e. 'doi':.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSandadi S, Gadzinski JA, Lee S, Chi DS, et al. Fellowship learning curve associated with completing a robotic assisted total laparoscopic hysterectomy. Gynecol Oncol. 2014;132(1):102\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ygyno.2013.11.017\u003c/span\u003e\u003c/span\u003e. 'doi':.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eOyama K, Kanno K, Kojima R, Shirane A, et al. Short-term outcomes of robotic-assisted versus conventional laparoscopic radical hysterectomy for early-stage cervical cancer: A single-center study. Journal of Obstetrics Gynaecology Research. 2019;45(2):405\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jog.13858\u003c/span\u003e\u003c/span\u003e. 'doi':.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eFleurence RL, Blake K, Shuren J. The Future of Registries in the Era of Real-world Evidence for Medical Devices. JAMA cardiology. 2019;4(3):197\u0026ndash;8. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamacardio.2018.4933\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eYuan H, Ali MS, Brouwer ES, et al. Real-World Evidence: What It Is and What It Can Tell Us According to the International Society for Pharmacoepidemiology (ISPE) Comparative Effectiveness Research (CER) Special Interest Group (SIG). Clin Pharmacol Ther. 2018;104(2):239\u0026ndash;41. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cpt.1086\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003eSherman RE, Anderson SA, Dal Pan GJ, Gray GW, et al. Real-World Evidence - What Is It and What Can It Tell Us? N Engl J Med. 2016;375(23):2293\u0026ndash;7. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMsb1609216\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e \u003c/li\u003e \u003cli\u003e \u003cspan\u003e\u003cem\u003eCervical Cancer, Version 2.2015. Journal of the National Comprehensive Cancer Network,13(4), 395\u0026ndash;404\u003c/em\u003e. doi:10.6004/jnccn.2015.0055.\u003c/span\u003e \u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"cervical cancer, laparoscopy, surgical experience, learning curve, oncological outcome","lastPublishedDoi":"10.21203/rs.3.rs-26906/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-26906/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e To compare the oncological outcomes of the first 50 laparoscopic radical hysterectomy (LRH) surgeries with the last 50 LRH, performed by high volume surgeons, for cervical cancer patients.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003e: A nationwide multicentre retrospective cohort study \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSetting\u003c/strong\u003e: Clinical diagnosis and treatment of cervical cancer patients in mainland China (Four C) database.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePopulation:\u003c/strong\u003e women with early cervical cancer undergone LRH. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We retrospectively analyzed the oncological outcomes of 1004 cervical cancer patients who underwent LRH performed by 19 surgeons. They were divided into two groups according to the sequence of operations, the first 50 and the last 50 patients with LRH. Kaplan-Meier survival analysis and log-rank test, Cox proportional risk regression model and propensity score matching were used. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMain Outcome Measures:\u003c/strong\u003e 5-year overall survival (OS) and disease-free survival (DFS) rates. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThere were no significant differences in the 5-year OS and DFS between first 50 patients with LRH group (n=413) and last 50 patients with LRH group (n=591) (OS: p=0.388; DFS: p=0.226). The last 50 cases of LRH was not an independent risk factor for OS and DFS in early cervical cancer patients (p=0.830, p=0.300). After propensity score matching, similar outcomes were observed (n=364:364，OS:P = 0.764; DFS:P = 0.705). \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe oncological outcomes of the first 50 LRH surgeries were similar to those of the last 50 surgeries in patients with early-stage cervical cancer. Increase in the surgeons’ experience did not improve significantly with oncological outcomes of patients with early stage cervical cancer after LRH.\u003c/p\u003e","manuscriptTitle":"Impact of Surgeons’ Experience on Oncologic Outcomes in Women Undergoing Laparoscopic Radical Hysterectomy for Cervical Cancer: A Comparison of the First 50 and last 50 Cases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-06-08 18:44:05","doi":"10.21203/rs.3.rs-26906/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b9a49db0-19d9-4fbd-aad1-8910ee1b89ce","owner":[],"postedDate":"June 8th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":115139,"name":"Cancer Biology"},{"id":115140,"name":"Oncology"}],"tags":[],"updatedAt":"2020-06-27T14:48:43+00:00","versionOfRecord":[],"versionCreatedAt":"2020-06-08 18:44:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-26906","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-26906","identity":"rs-26906","version":["v1"]},"buildId":"qQ7_6M8ijIrYJ9CiyUnPg","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}