Clinical application of FIGO 2023 staging system of endometrial cancer in a Chinese cohort

Clinical application of FIGO 2023 staging system of endometrial cancer in a Chinese cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Clinical application of FIGO 2023 staging system of endometrial cancer in a Chinese cohort Changmin Yu, Xinhui Yuan, Qianlan Yao, Yuyin Xu, Xiaoyan Zhou, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4451579/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jul, 2024 Read the published version in BMC Cancer → Version 1 posted 17 You are reading this latest preprint version Abstract Objective: The International Federation of Gynecology and Obstetrics (FIGO) 2023 staging system represents a refined classification with a comprehensive and improved prognostic significance for endometrial cancer (EC). Our objective is to further explore the clinical utility of the 2023 FIGO staging system in China. Methods: In current study, we conducted a retrospective analysis involving 547 patients who received standard surgeries and underwent genetic testing using multigene next-generation sequencing panels between December 2018 and December 2023 at Fudan University Shanghai Cancer Center. The genomic and clinical data of all patients were analyzed, and patients were restaged according to the 2023 staging systems. Results: After rearrangement of FIGO 2023 staging system in 547 cases, 345 cases of stage I, 107 cases of stage II, 69 cases of stage III, and 26 cases of stage IV were observed. The median follow-up duration was 19.0 months. Stage rearrangements occurred in 92/547 (16.8%) patients. 63 cases and 53 cases were re-classified into IAmPOLEmut and IICmp53abn from early stages (stage I and II), respectively, due to the molecular subtype of POLEmut and p53abn. Among stage I diseases, the 3-year PFS rate was 92.7% and 95.3% according to the 2009 and 2023 FIGO staging systems, respectively. The 3-year PFS of stage II patients under the 2023 FIGO staging system was lower than that of 2009 (3-year PFS: 85.0% versus 90.9%), especially in substage IIC and IICmp53abn. 3 cases (12%) of stage IIIA were rearranged into stage IA3, with 3-year PFS rates of 90.9% versus 100%, respectively. In genomic sequencing analysis, the most prevalent gene alterations occur in PTEN and PIK3CA. Conclusion: Predominant stage reclassifications were observed in early-stage diseases, emphasizing the enhanced precision provided by the new 2023 FIGO staging system in EC prognosis, which may facilitate more accurate therapeutic strategies. Distinct gene alterations also help to understand the characteristics of different subtypes. Endometrial cancer Cancer staging Molecular classification Prognostication Survival Figures Figure 1 Figure 2 Figure 3 Introduction Endometrial cancer ranks as the sixth most prevalent cancer among women, with a notable increase in incidence, particularly in economically developed regions [1-2]. It has been fifteen years since the International Federation of Gynecology and Obstetrics released the FIGO 2009 guidelines for endometrial cancer. In 2013, The Cancer Genome Atlas (TCGA) introduced a molecular classification system for endometrial cancer, based on tumor molecular characteristics, leading to substantial prognostic and biological distinctions. Simplifications of the TCGA classification by Talhouk et al. and Stelloo et al. via the ProMisE and Trans-PORTEC classification methods respectively, have markedly enhanced clinical utility [3-4]. More recently, the new 2023 FIGO staging system has been released, incorporating molecular classification, lympho-vascular space invasion (LVSI), and invasive pathological type as significant prognostic indicators [5]. Although the new staging system has been released for some time, it has not been widely applied broadly, especially in China. Several retrospective studies had reported improved discrimination of prognosis in the EC cohort with the 2023 FIGO staging system [6-8]. As some scholars still wondered if it was too early to incorporate evolving premature and complicated variables that lack robust supporting evidence, such as LVSI and aggressive histology [9,10]. Thus, we conducted this retrospective study to further evaluate the clinical applicability of the FIGO2023 staging system and explore its prognostic significance of endometrial cancer. Materials and methods We collected data from patients who received standard surgeries and genetic testing at Fudan University Shanghai Cancer Center (FUSCC) between December 2018 and December 2023, approved by the Institutional Review Board (IRB), which included surgical details, pathological reports, immunohistochemistry (IHC) results, postoperative therapies, and follow-up details. The multi-gene next-generation sequencing (NGS) Panel was utilized, including APC, AKT1, ATM, BRAF, BRCA1, BRCA2, CDH1, CHEK2, EGFR, EPCAM, ERBB2, HRAS, KIT, KRAS, MET, MLH1, MSH2, MSH6, MUTYH, NRAS, PDGFRA, PIK3CA, PMS2, POLD1, POLE, PTCH1, PTEN, SDHB, SDHC, SDHD, SMAD4, STK11, TP53, and 24 microsatellite loci, etc. The NGS panel detected insertions, deletions, base substitutions, and copy number alterations of the assessed genes, as well as microsatellite instability (MSI) status. Patients were classified into POLE mutated (POLEmut), mismatch repair deficient (MMRd), no specific molecular profile (NSMP), or p53 abnormal (p53abn) subtypes, according to the results of NGS and MSI status as previous studies suggested [11,12]. Clinical staging was re-evaluated according to the 2023 FIGO staging system. Sub-stratification of stages IIIC1 and IIIC2 was not evaluated due to flawed information on lymph node micro- and macro-metastasis assessments. Statistical analyses were performed using R Studio (version 4.2.1; R Studio, http://www.R-project.org) and IBM SPSS Statistics (Version 26.0). A significance level of p < 0.05 was considered statistically significant. Patients’ clinicopathologic characteristics were presented, and staging shifts were depicted using both table and Sankey diagram generated with RAWGraphs (https://app.rawgraphs.io). Progression-free survival (PFS) and overall survival (OS) were assessed via Kaplan-Meier analysis, and corresponding 95% confidence intervals (CIs) were calculated using the Brookmeyer-Crowley method. Results 1. Clinicopathologic characteristics A total of 547 patients were enrolled in the study. According to the results of NGS, 69 POLEmut patients, 118 MMRd patients, 280 NSMP patients, and 80 p53abn patients were observed, respectively. PFS and OS rates of different molecular subtypes were shown in Fig 1, in which 3-year PFS and OS of POLEmut patients were 98.5% [(95%-confidence interval [CI] 95.6-100] and 100%, respectively. 3-year survival rates of p53abn were the lowest [3-year PFS: 73.8% (95%-CI 61.6-88.5), 3-year OS: 94.0% (95%-CI 87.4-100),]. For MMRd and NSMP subtypes, the 3-year PFS rate was 85.7% (95%-CI 76.9-95.5) versus 90.7% (95%-CI 85.6-96.2), with the 3-year OS rate of 97.2% (95%-CI 93.2-100) versus 97.6% (95%-CI 94.8-100), respectively. The median follow-up time was 19.0 months (interquartile range [IQR]: 9.0-32.0). During the process of follow-up, 38 patients had recurrences, and 9 deaths were observed. More detailed characteristics are listed in Table 1. 2. Stage shifts of patients Among 547 patients, a total of 77 patients underwent upshifts (14.1%), and 15 underwent downshifts (2.7%). The stage shifts of all patients are listed in Table 2, with Fig 2 providing the Sankey diagram of stage shifts. In stage I diseases, 87 cases (20.9%) underwent rearrangements, among which 40 cases of stage IA (11.7%) and 10 cases of stage IB (13.5%) were converted to substage IICmp53abn due to the p53abn molecular subtype. Similarly, considering the inclusion of aggressive histological types, 16 cases of stage IA (4.7%) and 7 cases of stage IB (9.5%) were reclassified as stage IIC. 9 cases of stage IB patients (12.2%) were converted to substage IAmPOLEmut due to POLEmut molecular subtype. 3-year PFS and OS rate for stage I disease were identified through the 2009 and 2023 FIGO staging system, [3-year PFS: 92.7% (95%-CI 88.9-96.7 versus 95.3% (95%-CI 92.1-98.6)), respectively; 3-year OS: 99.6% (95%-CI 98.9-100) versus 100%]. Moreover, substage IAmPOLEmut exhibited 3-year PFS and OS survival rates of 98.4% (95%-CI 95.3-100), and 100%. Among stage II diseases, 3 cases (9.1%) were changed into substage IAmPOLEmut. The 3-year PFS of stage II patients under the 2023 FIGO staging system was lower than that of 2009, [3-year PFS: 85.0% (95%-CI 75.2-96.0) versus 90.9% (95%-CI 79.5-100)]. The 3-year PFS for substage IIC and IICmp53abn patients was lower than other early-stage diseases, [3-year PFS: 72.6% (95%-CI 45.9-100) and 88.3% (95%-CI 76.6-100), respectively]. 3 cases (12%) of stage IIIA were shifted into stage IA3, and 3-year PFS rates of stage IIIA1 and IA3 in the 2023 FIGO staging were 87.5% (95%-CI 67.3-100) versus 100%, respectively. For stage IV diseases, stage refinement was observed. Among the 22 cases of stage IV patients according to FIGO 2009, 6 patients remained stage IVB due to isolated extra-pelvic peritoneal metastases, while 16 patients were classified as stage IVC. The prognoses of stage I-IV of FIGO 2023 staging were detailed in Table 3. 3. IHC versus NGS concordance We also detected immunohistochemistry (IHC) of p53 protein and MMR protein, which includes p53, MLH1, PMS2, MSH2, and MSH6 protein expression. 48 cases were excluded due to the lack of IHC information. Detailed results of NGS and IHC detection for MSI/MMR and TP53/p53 status are shown in Fig 3. While taking MMR IHC as a substitution for MSI assay, 38 cases exhibited different results between MMR IHC and MSI assay, with a concordance rate of 92.4%. As for TP53/p53 status, 79 discordant NGS versus IHC cases were observed, representing a discordance rate of 15.8%. The overall concordance rate was 88.3% when taking the p53 IHC and MMR IHC together into consideration. These discordant situations mentioned above within IHC and NGS result in altered molecular subtypes, especially in the NSMP and p53abn subtypes, as shown in Table 4. 4. Targetable gene alterations Concurrently, we performed a comprehensive statistical analysis of the gene panel under investigation (refer to Table 5). Notably, PIK3CA and PTEN, pivotal components of the PI3K/AKT pathway, exhibited a high mutation rate of 59% and 81% in all cases, respectively. Especially in the POLEmut subgroup, the alteration rates of PIK3CA and PTEN were 88% and 96%, respectively. Genes associated with the homologous recombination repair (HRR) pathway, such as BRCA1/2 and ATM, demonstrated mutation rates of 14%, 26%, and 34% in the MMRd subtype. Additionally, estrogen receptor (ER) and progesterone receptor (PR) exhibited high positivity rates in the NSMP subgroup (by IHC), with positivity percentages of 90% and 88%, respectively. Discussion In this study, significant improvement was observed by restaging with the 2023 FIGO staging system to provide more precise prognostic guidance. Majority of staging alterations occurred in clinically earlier-stage patients (Stages I/II) due to the introduction of molecular subtypes, LVSI status, and aggressive histological types [5]. Multiple studies have confirmed good prognoses of the POLEmut subgroup, regardless of postoperative adjuvant therapy [13-15]. In this study, we also verified the excellent prognosis of the IAmPOLEmut cases, with a 3-year PFS rate of 98.4%. Therefore, de-escalating therapy was suggested by ESGO/ESTRO/ESP guidelines for early-stage POLEmut patients [16]. As IICmp53abn patients exhibiting poorer prognoses (3-year PFS rate of 88.3%), regardless of histological type, stage, or grade [17], more advanced therapies were suggested for p53abn EC patients[16]. Moreover, the aggressive histological types were involved in the FIGO 2023 staging system [5]. Previous studies demonstrated the worse prognoses of EC patients with aggressive histology [18,19]. Our results also found that substage IIC patients have significantly worse 3-year PFS rates compared to other stage II diseases with non-aggressive histology type, with 3-year PFS of 72.6% versus 85.6% (IIA) and 100% (IIB), respectively. Stage shift in stage III diseases was mainly because of the inclusion of the new IA3 category. The introduction of synchronous early-stage uterine and ovarian cancers (SEOs) provides additional refinement in prognosis compared to the 2009 FIGO system. Of note, there were discernible prognostic disparities between IA3 and IIIA1 patients, with a 3-year PFS rate of 100% and 87.5%, respectively. Similarly, Gravbrot et al. also noted significant prognostic distinctions between Stage IIIA and IA3 in their cohort of The National Cancer Database (NCDB)[8]. In the etiology of SEOs, most researchers suggest a complex interplay involving clonality, epigenetics, and the tumor microenvironment, indicating a shared origin associated with a favorable prognosis [20,21]. Previous studies, including the ProMisE and TransPORTEC classifiers, have suggested that p53, MLH1, MSH2, MSH6, and PMS2 protein immunostaining status as a substitute for TP53 NGS and microsatellite instability status [3,22], with an approximate accuracy range of 81.3%-95.6% and 93.3%-98.8% [23-26], respectively. We also employed the IHC as an alternative to molecular testing for economic practicality and clinical accessibility. According to our results, IHC was an acceptable alternative to next-generation NGS in molecular subtype of EC, especially for mismatch repair protein immunostaining with a concordance rate of 92.4%. However, the concordance rate (84.2%) between IHC and NGS indicated a more cautious decision of interchange of TP53/p53. Discordant results of two techniques testing TP53 status could be explained by tumor heterogeneity, alterations in non-coding regulatory regions, flawed detection of stop-gained mutation, and large-scale deletions or insertions [24,27]. Some conflicting cases within MMR IHC and MSI assay could be potentially explained by MLH1 promoter methylation, solely MLH1 or MSH6 mutation resulting in protein dysfunction without structural changes, and other undefined situations [26,28]. In some confusing cases, NGS might be recommended to provide more precise information and avoid misdiagnoses in these patients. The elevated somatic mutation frequency observed in our study of PIK3CA and PTEN suggests a potential therapeutic strategy targeting the PI3K/AKT/mTOR pathway. Various inhibitors targeting PI3K, mTOR, AKT, and dual PI3K/mTOR pathways have shown promising anti-tumor activities, with ongoing clinical trials validating their efficacy and safety [29,30]. In MMRd patients of our study, the highly mutated genes associated with homologous recombination repair pathways suggest potential responsiveness to immune checkpoint inhibition therapy, as previous studies reported [31,32]. High expression of estrogen and progesterone receptors of NSMP in our study implied the high hormone sensitivity of this subtype. Thereby emerging research has revealed the potential efficacy of anti-estrogen therapy for NSMP patients, such as Letrozole and Anastrozole , particularly among those at elevated risk of disease recurrence and metastasis [33-35]. Several limitations should be considered when interpreting the results of our study. Firstly, our cohort is confined to a single center which may compromise the power of our findings. Secondly, our results may suffer from a retrospective nature, which may lead to bias inevitably. In conclusion, we further explored the clinical applicability and prognostic significance of the 2023 FIGO staging system and investigated the gene characteristics to find out the potential targeted therapies for different molecular subtypes. Larger-scale and multi-regional investigations were needed to validate the accuracy of our insights. Declarations Ethics approval and consent to participate This study was approved by the clinical ethics committee of Fudan University Shanghai Cancer Center, Fudan University. All patients had signed informed consent. This study was in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration. Consent for publication Not appliable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interest The authors declare that they have no competing interests. Funding This study was supported by Shanghai Science and Technology Innovation Action Plan (No. 20Z11900703), and the Climb Grant of National Cancer Center (grant No. NCC201909B02). Authors' contributions C.Y., X.Y., Q.Y., and Y.X. contributed to data collection, data analysis, and bioinformatics analysis, X.Z. and X.H. contributed to experimental technical support and data management, H.Y. and H.W. contributed to project administration, X.Z. and Y.R. contributed to conceptualization, manuscript revision. Y.R. contributed to funding acquisition. All authors read and approved the final manuscript. Acknowledgements Not appliable. References Siegel, R.L., Miller, K.D., Fuchs, H.E., and Jemal, A. (2021). Cancer Statistics, 2021. CA Cancer J Clin 71 , 7-33. 10.3322/caac.21654. Crosbie, E.J., Kitson, S.J., McAlpine, J.N., Mukhopadhyay, A., Powell, M.E., and Singh, N. (2022). Endometrial cancer. Lancet 399 , 1412-1428. 10.1016/s0140-6736(22)00323-3. Talhouk, A., McConechy, M.K., Leung, S., Yang, W., Lum, A., Senz, J., Boyd, N., Pike, J., Anglesio, M., Kwon, J.S., et al. (2017). Confirmation of ProMisE: A simple, genomics-based clinical classifier for endometrial cancer. 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Phase II study of fulvestrant in recurrent/metastatic endometrial carcinoma: a Gynecologic Oncology Group study. Gynecol Oncol 120 , 185-188. 10.1016/j.ygyno.2010.10.015. Tables Table 1 . Patient characteristics Variable Number (%) Total 547 Age(year) 54.6 (24.0-87.0) BMI (kg/m 2 ) 24.9 (16.4-64.4) Obesity (BMI>30) 56 (10.2%) Diabetes mellitus 64 (11.7%) Arterial hypertension 164 (29.9%) Histology Endometrioid 491 (89.7%) Serous 15 (2.7%) Clear cell 3 (0.5%) Carcinosarcoma 7 (1.2%) Undifferentiated/ Dedifferentiated 10 (18.2%) Mixed histology 10 (18.2%) Atypical hyperplasia 1 (0.1%) Others 2 (0.3%) Unclear 8 (1.4%) Molecular classification a POLEmut 69 (12.6%) MMRd 118 (21.6%) NSMP 280 (51.2%) P53abn 80 (14.6%) LVSI b No LVSI 396 (72.3%) Local LVSI 128 (23.4%) Substantial LVSI 11 (2.0%) Missing 9 (1.6%) ER/PR status (based on IHC) c ER positive 514 (93.9%) PR positive 481 (87.9%) a: POLE mutated (POLEmut), mismatch repair deficient (MMRd), no specific molecular profile (NSMP), or p53 abnormal (p53abn) b: Lymphovascular space invasion (LVSI) c: Estrogen receptor (ER), progesterone receptor (PR), Immunohistochemistry (IHC) Table 2. FIGO 2009-2023 stage shifts in all patients. 2009FIGO IA IB II IIIA IIIB IIIC1 IIIC2 IVA IVB 2023FIGO n=342 n=74 n=33 n=25 n=5 n=26 n=16 n=4 n=22 IAm POLEmut n=63 51 9(↓) 3(↓) - - - - - - IA1 n=52 52 - - - - - - - - IA2 n=178 178 - - - - - - - - IA3 n=3 - - - 3 - - - - - IB n=48 - 48 - - - - - - - IC n=1 1(↑) - - - - - - - - IIA n=18 - - 18 - - - - - - IIB n=5 4(↑) - 1 - - - - - - IIC n=31 16(↑) 7(↑) 8 - - - - - - IICm p53abn n=53 40(↑) 10(↑) 3 - - - - - - IIIA1 n=17 - - - 17 - - - - - IIIB1 n=3 - - - - 3 - - - - IIIB2 n=7 - - - 5 2 - - - - IIIC1 n=26 - - - - - 26 - - - IIIC2 n=16 - - - - - - 16 - - IVA n=4 - - - - - - - 4 - IVB n=6 - - - - - - - - 6 IVC n=16 - - - - - - - - 16 Table 3. 3-year PFS and OS based on FIGO 2009 and FIGO 2023 staging system. FIGO 2009 3-year PFS rate in % (95%CI) 3-year OS rate in % (95%CI) FIGO 2023 3-year PFS rate in % (95%CI) 3-year OS rate in % (95%CI) I 92.7(88.9-96.7) 99.6(98.9- /) a I 95.3(92.1-98.6) / IA 92.6(88.3-97.2) / IA1 / / IA2 91.9(86.1-98.2) 95.2(86.5- /) IA3 / / IAmPOLEmut 98.4(95.3-/) / IB 92.8(84.8- /) 98.0(94.2- /) IB 97.7(93.4-/) / IC / / II 90.9(79.5- /) 94.7(85.2- /) II 85.0(75.2-96.0) 96.9(92.8- /) IIA 85.6(68.8- /) 92.3(78.9- /) IIB / / IIC 72.6(45.9- /) 94.7(85.2- /) IICmp53abn 88.3(76.6- /) / III 83.3(72.0-96.4) 94.3(88.0- /) III 82.8(71.1-96.3) 94.1(87.6- /) IIIA 90.9(75.4- /) / IIIA1 87.5(67.3- /) / IIIB 80.0(51.6- /) / IIIB1 66.7(30.0- /) / IIIB2 / / IIIC1 91.7(77.3- /) / IIIC1 91.7(77.3- /) / IIIC2 57.7(31.6- /) 71.6(47.2- /) IIIC2 57.7(31.6- /) 71.6(47.2- /) IV 36.4(19.1-69.3) 80.6(62.7- /) IV 36.4(19.1-69.3) 80.6(62.7- /) IVA 50.0(12.5- /) / IVA 50.0(12.5- /) / IVB 33.7(16.1-70.5) 78.9(60.0- /) IVB 20.0(3.4- /) 80.0(51.6- /) IVC 37.7(16.4-86.9) 80.0(58.7- /) a: / refers to a survival rate of 100%. Table 4. Alterations in molecular subtypes based on different methods. NGS a P53 IHC a MMR IHC a P53 & MMR IHC a NSMP 253 283 230 254 MMRd 111 111 137 139 P53abn 74 44 71 45 POLEmut 61 61 61 61 a: 48 samples were excluded due to incomplete immunohistochemical data. b: next-generation sequencing (NGS) Table 5. Gene mutation frequencies in interested pathways. Targetable alterations POLEmut N (%) MMRd N (%) NSMP N (%) p53abn n (%) Chi-square p value a Total(n) 69 118 280 80 PI3K-AKT pathway PIK3CA 61 (88) 78 (66) 142 (51) 32 (40) <0.001 PTEN 66 (96) 108 (92) 224 (80) 45 (56) <0.001 KRAS 9 (13) 32 (27) 69 (25) 10 (13) <0.05 NRAS 4 (6) 4 (3) 9 (3) 0 (0) ns AKT1 5 (7) 6 (5) 29 (10) 5 (6) ns Hormone receptor positivity (based on IHC) b ER 62 (90) 115 (97) 252 (90) 60 (75) <0.001 PR 55 (80) 102 (86) 246 (88) 52 (65) <0.001 Homologous recombination repair BRCA1 27 (39) 17 (14) 9 (3) 7 (9) <0.001 BRCA2 49 (71) 31 (26) 28 (10) 8 (10) <0.001 ATM 55 (80) 40 (34) 33 (12) 8 (10) <0.001 TGF-β pathway SMAD4 9 (13) 6 (5) 2 (1) 1 (1) <0.001 Receptor tyrosine kinase KIT 30 (43) 11 (9) 3 (1) 1 (1) <0.001 PDGFRA 24 (35) 9 (8) 11 (4) 2 (3) 0.05 b: Immunohistochemistry (IHC) Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team 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-4451579","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309855985,"identity":"ef65a50f-db10-4e6c-b416-62f295174e87","order_by":0,"name":"Changmin Yu","email":"","orcid":"","institution":"Fudan University Shanghai Cancer Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Changmin","middleName":"","lastName":"Yu","suffix":""},{"id":309855986,"identity":"9aa67494-5c7f-4832-ad90-3726b651e5a7","order_by":1,"name":"Xinhui Yuan","email":"","orcid":"","institution":"Fudan University Shanghai Cancer 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02:00:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4451579/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4451579/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12885-024-12633-8","type":"published","date":"2024-07-18T16:13:27+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57953475,"identity":"5310e900-0b87-4405-af43-fecbcf3a4b48","added_by":"auto","created_at":"2024-06-07 23:02:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3896998,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves of PFS and OS in different molecular subtypes.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4451579/v1/f9ca0720a28e634a1ab566b8.png"},{"id":57953473,"identity":"c38f52d8-a7fc-47b7-bc90-03def5197241","added_by":"auto","created_at":"2024-06-07 23:02:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":250853,"visible":true,"origin":"","legend":"\u003cp\u003eSankey diagram of stage shifts in all patients.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4451579/v1/d4601535958a93c0c3d14e30.png"},{"id":57953474,"identity":"1fc8308d-97ba-493c-bb26-a77c93934e73","added_by":"auto","created_at":"2024-06-07 23:02:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":938515,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram and results of the IHC versus NGS study.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4451579/v1/e4e750641e0e0e0c59cfc75b.png"},{"id":61595274,"identity":"c3b90a71-bce3-4518-9dbd-f165e8113704","added_by":"auto","created_at":"2024-08-01 17:21:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9551942,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4451579/v1/b8a50d30-7e79-4e6f-b151-770e07c3e3dd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical application of FIGO 2023 staging system of endometrial cancer in a Chinese cohort","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometrial cancer ranks as the sixth most prevalent cancer among women, with a notable increase in incidence, particularly in economically developed regions [1-2]. It has been fifteen years since the International Federation of Gynecology and Obstetrics released the FIGO 2009 guidelines for endometrial cancer. In 2013, The Cancer Genome Atlas (TCGA) introduced a molecular classification system for endometrial cancer, based on tumor molecular characteristics, leading to substantial prognostic and biological distinctions. Simplifications of the TCGA classification by Talhouk et al. and Stelloo et al. via the ProMisE and Trans-PORTEC classification methods respectively, have markedly enhanced clinical utility [3-4]. More recently, the new 2023 FIGO staging system has been released, incorporating molecular classification, lympho-vascular space invasion (LVSI), and invasive pathological type as significant prognostic indicators [5].\u003c/p\u003e\n\u003cp\u003eAlthough the new staging system has been released for some time, it has not been widely applied broadly, especially in China. Several retrospective studies had reported improved discrimination of prognosis in the EC cohort with the 2023 FIGO staging system [6-8]. As some scholars still wondered if it was too early to incorporate evolving premature and complicated variables that lack robust supporting evidence, such as LVSI and aggressive histology [9,10]. Thus, we conducted this retrospective study to further evaluate the clinical applicability of the FIGO2023 staging system and explore its prognostic significance of endometrial cancer.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eWe collected data from patients who received standard surgeries and genetic testing at Fudan University Shanghai Cancer Center (FUSCC) between December 2018 and December 2023, approved by the Institutional Review Board (IRB), which included surgical details, pathological reports, immunohistochemistry (IHC) results, postoperative therapies, and follow-up details. The multi-gene next-generation sequencing (NGS) Panel was utilized, including APC, AKT1, ATM, BRAF, BRCA1, BRCA2, CDH1, CHEK2, EGFR, EPCAM, ERBB2, HRAS, KIT, KRAS, MET, MLH1, MSH2, MSH6, MUTYH, NRAS, PDGFRA, PIK3CA, PMS2, POLD1, POLE, PTCH1, PTEN, SDHB, SDHC, SDHD, SMAD4, STK11, TP53, and 24 microsatellite loci, etc. The NGS panel detected insertions, deletions, base substitutions, and copy number alterations of the assessed genes, as well as microsatellite instability (MSI) status. Patients were classified into POLE mutated (POLEmut), mismatch repair deficient (MMRd), no specific molecular profile (NSMP), or p53 abnormal (p53abn) subtypes, according to the results of NGS and MSI status as previous studies suggested [11,12]. Clinical staging was re-evaluated according to the 2023 FIGO staging system. Sub-stratification of stages IIIC1 and IIIC2 was not evaluated due to flawed information on lymph node micro- and macro-metastasis assessments.\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using R Studio (version 4.2.1; R Studio, http://www.R-project.org) and IBM SPSS Statistics (Version 26.0). A significance level of p \u0026lt; 0.05 was considered statistically significant. Patients\u0026rsquo; clinicopathologic characteristics were presented, and staging shifts were depicted using both table and Sankey diagram generated with RAWGraphs (https://app.rawgraphs.io). Progression-free survival (PFS) and overall survival (OS) were assessed via Kaplan-Meier analysis, and corresponding 95% confidence intervals (CIs) were calculated using the Brookmeyer-Crowley method.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1. Clinicopathologic characteristics\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 547 patients were enrolled in the study. According to the results of NGS, 69 POLEmut patients, 118 MMRd patients, 280 NSMP patients, and 80 p53abn patients were observed, respectively. PFS and OS rates of different molecular subtypes were shown in Fig 1, in which 3-year PFS and OS of POLEmut patients were 98.5% [(95%-confidence interval [CI] 95.6-100] and 100%, respectively. 3-year survival rates of p53abn were the lowest [3-year PFS: 73.8% (95%-CI 61.6-88.5), 3-year OS: 94.0% (95%-CI 87.4-100),].\u0026nbsp;For MMRd and NSMP subtypes, the 3-year PFS rate was 85.7% (95%-CI 76.9-95.5) versus 90.7% (95%-CI 85.6-96.2), with the 3-year OS rate of 97.2% (95%-CI 93.2-100) versus 97.6% (95%-CI 94.8-100), respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The median follow-up time was 19.0 months (interquartile range [IQR]: 9.0-32.0). During the process of follow-up, 38 patients had recurrences, and 9 deaths were observed. More detailed characteristics are listed in Table 1. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Stage shifts of patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 547 patients, a total of 77 patients underwent upshifts (14.1%), and 15 underwent downshifts (2.7%). The stage shifts of all patients are listed in Table 2, with Fig 2 providing the Sankey diagram of stage shifts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn stage I diseases, 87 cases (20.9%) underwent rearrangements, among which 40 cases of stage IA (11.7%) and 10 cases of stage IB (13.5%) were converted to substage IICmp53abn due to the p53abn molecular subtype. Similarly, considering the inclusion of aggressive histological types, 16 cases of stage IA (4.7%) and 7 cases of stage IB (9.5%) were reclassified as stage IIC. 9 cases of stage IB patients (12.2%) were converted to substage IAmPOLEmut due to POLEmut molecular subtype. 3-year PFS and OS rate for stage I disease were identified through the 2009 and 2023 FIGO staging system, [3-year PFS: 92.7% (95%-CI 88.9-96.7 versus 95.3% (95%-CI 92.1-98.6)), respectively; 3-year OS: 99.6% (95%-CI 98.9-100) versus 100%]. Moreover, substage IAmPOLEmut exhibited 3-year PFS and OS survival rates of 98.4% (95%-CI 95.3-100), and 100%.\u003c/p\u003e\n\u003cp\u003eAmong stage II diseases, 3 cases (9.1%) were changed into substage IAmPOLEmut. The 3-year PFS of stage II patients under the 2023 FIGO staging system was lower than that of 2009, [3-year PFS: 85.0% (95%-CI 75.2-96.0) versus 90.9% (95%-CI 79.5-100)]. The 3-year PFS for substage IIC and IICmp53abn patients was lower than other early-stage diseases, [3-year PFS: 72.6% (95%-CI 45.9-100) and 88.3% (95%-CI 76.6-100), respectively].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3 cases (12%) of stage IIIA were shifted into stage IA3, and 3-year PFS rates of stage IIIA1 and IA3 in the 2023 FIGO staging were 87.5% (95%-CI 67.3-100) versus 100%, respectively. For stage IV diseases, stage refinement was observed. Among the 22 cases of stage IV patients according to FIGO 2009, 6 patients remained stage IVB due to isolated extra-pelvic peritoneal metastases, while 16 patients were classified as stage IVC. The\u0026nbsp;prognoses of stage I-IV of FIGO 2023 staging were detailed in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. IHC versus NGS concordance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe also detected immunohistochemistry (IHC) of p53 protein and MMR protein, which includes p53, MLH1, PMS2, MSH2, and MSH6 protein expression. 48 cases were excluded due to the lack of IHC information. Detailed results of NGS and IHC detection for MSI/MMR and TP53/p53 status are shown in Fig 3. \u0026nbsp;While taking MMR IHC as a substitution for MSI assay, 38 cases exhibited different results between MMR IHC and MSI assay, with a concordance rate of 92.4%. As for TP53/p53 status, 79 discordant NGS versus IHC cases were observed, representing a discordance rate of 15.8%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe overall concordance rate was 88.3% when taking the p53 IHC and MMR IHC together into consideration. These discordant situations mentioned above within IHC and NGS result in altered molecular subtypes, especially in the NSMP and p53abn subtypes, as shown in Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Targetable gene alterations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcurrently, we performed a comprehensive statistical analysis of the gene panel under investigation (refer to Table 5). Notably, PIK3CA and PTEN, pivotal components of the PI3K/AKT pathway, exhibited a high mutation rate of 59% and 81% in all cases, respectively. Especially in the POLEmut subgroup, the alteration rates of PIK3CA and PTEN were 88% and 96%, respectively. Genes associated with the homologous recombination repair (HRR) pathway, such as BRCA1/2 and ATM, demonstrated mutation rates of 14%, 26%, and 34% in the MMRd subtype. Additionally, estrogen receptor (ER) and progesterone receptor (PR) exhibited high positivity rates in the NSMP subgroup (by IHC), with positivity percentages of 90% and 88%, respectively.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, significant improvement was observed by restaging with the 2023 FIGO staging system to provide more precise prognostic guidance.\u003c/p\u003e\n\u003cp\u003eMajority of staging alterations occurred in clinically earlier-stage patients (Stages I/II) due to the introduction of molecular subtypes, LVSI status, and aggressive histological types [5]. Multiple studies have confirmed good prognoses of the POLEmut subgroup, regardless of postoperative adjuvant therapy [13-15]. In this study, we also verified the excellent prognosis of the IAmPOLEmut cases, with a 3-year PFS rate of 98.4%. Therefore, de-escalating therapy was suggested by ESGO/ESTRO/ESP guidelines for early-stage POLEmut patients\u0026nbsp;[16]. As IICmp53abn patients\u0026nbsp;exhibiting poorer prognoses (3-year PFS rate of 88.3%), regardless\u0026nbsp;of histological type, stage, or grade [17], more advanced therapies were suggested for p53abn EC patients[16]. Moreover, the aggressive histological types were involved in the FIGO 2023 staging system\u0026nbsp;[5]. Previous studies demonstrated the worse prognoses of EC patients with aggressive histology [18,19]. Our results also found that substage IIC patients have significantly worse 3-year PFS rates compared to other stage II diseases with non-aggressive histology type, with 3-year PFS of 72.6% versus 85.6% (IIA)\u0026nbsp;and 100% (IIB), respectively.\u003c/p\u003e\n\u003cp\u003eStage shift in stage III diseases was mainly because of the inclusion of the new IA3 category.\u0026nbsp;The introduction of synchronous early-stage uterine and ovarian cancers (SEOs) provides additional refinement in prognosis compared to the 2009 FIGO system. Of note, there were discernible prognostic disparities between IA3 and IIIA1 patients, with a 3-year PFS rate of 100% and 87.5%, respectively. Similarly, Gravbrot et al. also noted significant prognostic distinctions between Stage IIIA and IA3 in their cohort of The National Cancer Database (NCDB)[8]. In the etiology of SEOs, most researchers suggest a complex interplay involving clonality, epigenetics, and the tumor microenvironment, indicating a shared origin associated with a favorable prognosis [20,21].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrevious studies, including the ProMisE and TransPORTEC classifiers, have suggested that p53, MLH1, MSH2, MSH6, and PMS2 protein immunostaining status as a substitute for TP53 NGS and microsatellite instability status [3,22], with an approximate accuracy range of 81.3%-95.6% and 93.3%-98.8% [23-26], respectively.\u0026nbsp;We also employed the IHC as an alternative to molecular testing for economic practicality and clinical accessibility. According to our results,\u0026nbsp;IHC was an acceptable alternative to next-generation NGS in molecular subtype of EC, especially for mismatch repair protein immunostaining with a concordance rate of 92.4%. However, the concordance rate (84.2%) between IHC and NGS indicated a more cautious decision of interchange of TP53/p53. Discordant results of two techniques testing TP53 status could be explained by tumor heterogeneity, alterations in non-coding regulatory regions, flawed detection of stop-gained mutation, and large-scale deletions or insertions [24,27]. Some conflicting cases within MMR IHC and MSI assay could be potentially explained by\u0026nbsp;MLH1 promoter methylation, solely MLH1 or MSH6 mutation resulting in protein dysfunction without structural changes, and other undefined situations [26,28]. In some confusing cases, NGS might be recommended to provide more precise information and avoid misdiagnoses in these patients.\u003c/p\u003e\n\u003cp\u003eThe elevated somatic mutation frequency observed in our study of PIK3CA and PTEN suggests a potential therapeutic strategy targeting the PI3K/AKT/mTOR pathway. Various inhibitors targeting PI3K, mTOR, AKT, and dual PI3K/mTOR pathways have shown promising anti-tumor activities, with ongoing clinical trials validating their efficacy and safety [29,30]. In MMRd patients of our study, the highly mutated genes associated with homologous recombination repair pathways suggest potential responsiveness to immune checkpoint inhibition therapy, as previous studies reported [31,32]. High expression of estrogen and progesterone receptors of NSMP in our study implied the high hormone sensitivity of this subtype. Thereby emerging research has revealed the potential efficacy of anti-estrogen therapy for NSMP patients, such as \u003cem\u003eLetrozole\u003c/em\u003e and \u003cem\u003eAnastrozole\u003c/em\u003e, particularly among those at elevated risk of disease recurrence and metastasis [33-35].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be considered when interpreting the results of our study. Firstly, our cohort is confined to a single center which may compromise the power of our findings. Secondly, our results may suffer from a retrospective nature, which may lead to bias inevitably.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, we further explored the clinical applicability and prognostic significance of the 2023 FIGO staging system and investigated the gene characteristics to find out the potential targeted therapies for different molecular subtypes. Larger-scale and multi-regional investigations were needed to validate the accuracy of our insights.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the clinical ethics committee of Fudan University Shanghai Cancer Center, Fudan University. All patients had signed informed consent. This study was in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot appliable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Shanghai Science and Technology Innovation Action Plan (No. 20Z11900703), and the Climb Grant of National Cancer Center (grant No. NCC201909B02).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.Y., X.Y., Q.Y., and Y.X. contributed to data collection, data analysis, and bioinformatics analysis, X.Z. and X.H. contributed to experimental technical support and data management, H.Y. and H.W. contributed to project administration, X.Z. and Y.R. contributed to conceptualization, manuscript revision. Y.R. contributed to funding acquisition. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot appliable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel, R.L., Miller, K.D., Fuchs, H.E., and Jemal, A. (2021). Cancer Statistics, 2021. CA Cancer J Clin \u003cem\u003e71\u003c/em\u003e, 7-33. 10.3322/caac.21654.\u003c/li\u003e\n\u003cli\u003eCrosbie, E.J., Kitson, S.J., McAlpine, J.N., Mukhopadhyay, A., Powell, M.E., and Singh, N. (2022). Endometrial cancer. Lancet \u003cem\u003e399\u003c/em\u003e, 1412-1428. 10.1016/s0140-6736(22)00323-3.\u003c/li\u003e\n\u003cli\u003eTalhouk, A., McConechy, M.K., Leung, S., Yang, W., Lum, A., Senz, J., Boyd, N., Pike, J., Anglesio, M., Kwon, J.S., et al. (2017). Confirmation of ProMisE: A simple, genomics-based clinical classifier for endometrial cancer. 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Gynecol Oncol \u003cem\u003e120\u003c/em\u003e, 185-188. 10.1016/j.ygyno.2010.10.015.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e.\u0026nbsp;Patient characteristics\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"463\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.41558441558441%\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"65.58441558441558%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Number (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e547\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge(year)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e54.6 (24.0-87.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e24.9 (16.4-64.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eObesity (BMI\u0026gt;30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e56 (10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes mellitus\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e64 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eArterial hypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e164 (29.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003eEndometrioid\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e491 (89.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003eSerous\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e15 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003eClear cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e3 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003eCarcinosarcoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e7 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003eUndifferentiated/ Dedifferentiated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e10 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003eMixed histology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e10 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003eAtypical hyperplasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e1 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003eOthers\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e2 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003eUnclear\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e8 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular classification \u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003ePOLEmut\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e69 (12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003eMMRd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e118 (21.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003eNSMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e280 (51.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003eP53abn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e80 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLVSI\u0026nbsp;\u003c/strong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003eNo LVSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e396 (72.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003eLocal LVSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e128 (23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003eSubstantial LVSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e11 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003eMissing\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e9 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eER/PR status (based on IHC) \u003csup\u003ec\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003eER positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e514 (93.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.02164502164502%\" colspan=\"2\"\u003e\n \u003cp\u003ePR positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.97835497835498%\"\u003e\n \u003cp\u003e481 (87.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ea: POLE mutated (POLEmut), mismatch repair deficient (MMRd), no specific molecular profile (NSMP), or p53 abnormal (p53abn)\u003c/p\u003e\n\u003cp\u003eb:\u0026nbsp;Lymphovascular space invasion (LVSI)\u003c/p\u003e\n\u003cp\u003ec: Estrogen receptor (ER), progesterone receptor (PR), Immunohistochemistry (IHC)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e FIGO 2009-2023 stage shifts in all patients.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"576\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"77.81629116117851%\" colspan=\"9\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2009FIGO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003eIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003eIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003eIIIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003eIIIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003eIIIC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003eIIIC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003eIVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003eIVB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e2023FIGO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003en=342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003en=74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003en=33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003en=25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003en=5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003en=26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003en=16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003en=4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003en=22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003eIAm\u003csub\u003ePOLEmut\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003en=63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003e9(\u0026darr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e3(\u0026darr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003eIA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003en=52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003eIA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003en=178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003eIA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003en=3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003eIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003en=48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003eIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003en=1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003e1(\u0026uarr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003eIIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003en=18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003eIIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003en=5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003e4(\u0026uarr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003eIIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003en=31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003e16(\u0026uarr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003e7(\u0026uarr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003eIICm\u003csub\u003ep53abn\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003en=53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003e40(\u0026uarr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003e10(\u0026uarr;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003eIIIA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003en=17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003eIIIB1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003en=3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003eIIIB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003en=7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003eIIIC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003en=26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003eIIIC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003en=16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003eIVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003en=4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003eIVB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003en=6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.651646447140381%\" valign=\"top\"\u003e\n \u003cp\u003eIVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.532062391681109%\" valign=\"top\"\u003e\n \u003cp\u003en=16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.918544194107453%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.051993067590988%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.145580589254767%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.79896013864818%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.31889081455806%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.972270363951473%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.492201039861351%\" valign=\"top\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e 3-year PFS and OS based on FIGO 2009 and FIGO 2023 staging system.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.289241622574956%\" valign=\"top\"\u003e\n \u003cp\u003eFIGO\u003c/p\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e3-year PFS rate in % (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e3-year OS rate in % (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003eFIGO\u003c/p\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e3-year PFS rate in % (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e3-year OS rate in % (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.289241622574956%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e92.7(88.9-96.7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.6(98.9- /) \u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95.3(92.1-98.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e/\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.289241622574956%\" valign=\"top\"\u003e\n \u003cp\u003eIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e92.6(88.3-97.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003eIA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.289241622574956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003eIA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e91.9(86.1-98.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e95.2(86.5- /)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.289241622574956%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003eIA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.289241622574956%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003eIAmPOLEmut\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e98.4(95.3-/)\u003c/p\u003e\n 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valign=\"top\"\u003e\n \u003cp\u003eIIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e85.6(68.8- /)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e92.3(78.9- /)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.289241622574956%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003eIIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.289241622574956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003eIIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e72.6(45.9- /)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e94.7(85.2- /)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.289241622574956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003eIICmp53abn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e88.3(76.6- /)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.289241622574956%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eIII\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n 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valign=\"top\"\u003e\n \u003cp\u003e66.7(30.0- /)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.289241622574956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003eIIIB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.289241622574956%\" valign=\"top\"\u003e\n \u003cp\u003eIIIC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e91.7(77.3- /)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003eIIIC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e91.7(77.3- /)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.289241622574956%\" valign=\"top\"\u003e\n \u003cp\u003eIIIC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e57.7(31.6- /)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n 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\u003cp\u003e\u003cstrong\u003eIV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e36.4(19.1-69.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e80.6(62.7- /)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.289241622574956%\" valign=\"top\"\u003e\n \u003cp\u003eIVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e50.0(12.5- /)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003eIVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e50.0(12.5- /)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.289241622574956%\" valign=\"top\"\u003e\n \u003cp\u003eIVB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e33.7(16.1-70.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e78.9(60.0- /)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003eIVB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e20.0(3.4- /)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e80.0(51.6- /)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.289241622574956%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003eIVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e37.7(16.4-86.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.34215167548501%\" valign=\"top\"\u003e\n \u003cp\u003e80.0(58.7- /)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ea: / refers to a survival rate of 100%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Alterations in molecular subtypes based on different methods.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.772823779193207%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.772823779193207%\" valign=\"top\"\u003e\n \u003cp\u003eNGS \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.772823779193207%\" valign=\"top\"\u003e\n \u003cp\u003eP53 IHC \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.957537154989385%\" valign=\"top\"\u003e\n \u003cp\u003eMMR IHC \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.723991507431%\" valign=\"top\"\u003e\n \u003cp\u003eP53 \u0026amp; MMR IHC \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.772823779193207%\" valign=\"top\"\u003e\n \u003cp\u003eNSMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.772823779193207%\" valign=\"top\"\u003e\n \u003cp\u003e253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.772823779193207%\" valign=\"top\"\u003e\n \u003cp\u003e283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.957537154989385%\" valign=\"top\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.723991507431%\" valign=\"top\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.772823779193207%\" valign=\"top\"\u003e\n \u003cp\u003eMMRd\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.772823779193207%\" valign=\"top\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.772823779193207%\" valign=\"top\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.957537154989385%\" valign=\"top\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.723991507431%\" valign=\"top\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.772823779193207%\" valign=\"top\"\u003e\n \u003cp\u003eP53abn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.772823779193207%\" valign=\"top\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.772823779193207%\" valign=\"top\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.957537154989385%\" valign=\"top\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.723991507431%\" valign=\"top\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.772823779193207%\" valign=\"top\"\u003e\n \u003cp\u003ePOLEmut\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.772823779193207%\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.772823779193207%\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.957537154989385%\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.723991507431%\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ea: 48 samples were excluded due to incomplete immunohistochemical data.\u003c/p\u003e\n\u003cp\u003eb: next-generation sequencing (NGS)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e Gene mutation frequencies in interested pathways.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.845070422535212%\"\u003e\n \u003cp\u003e\u003cem\u003eTargetable alterations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.950704225352112%\"\u003e\n \u003cp\u003e\u003cem\u003ePOLEmut\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eN (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.788732394366198%\"\u003e\n \u003cp\u003e\u003cem\u003eMMRd\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eN (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02112676056338%\"\u003e\n \u003cp\u003e\u003cem\u003eNSMP\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eN (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\"\u003e\n \u003cp\u003e\u003cem\u003ep53abn\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003en (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\"\u003e\n \u003cp\u003e\u003cem\u003eChi-square \u0026nbsp; p value \u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.845070422535212%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTotal(n)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.950704225352112%\" valign=\"top\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.788732394366198%\" valign=\"top\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02112676056338%\" valign=\"top\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"83.80281690140845%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePI3K-AKT pathway\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.845070422535212%\" valign=\"top\"\u003e\n \u003cp\u003ePIK3CA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.950704225352112%\" valign=\"top\"\u003e\n \u003cp\u003e61 (88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.788732394366198%\" valign=\"top\"\u003e\n \u003cp\u003e78 (66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02112676056338%\" valign=\"top\"\u003e\n \u003cp\u003e142 (51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e32 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.845070422535212%\" valign=\"top\"\u003e\n \u003cp\u003ePTEN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.950704225352112%\" valign=\"top\"\u003e\n \u003cp\u003e66 (96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.788732394366198%\" valign=\"top\"\u003e\n \u003cp\u003e108 (92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02112676056338%\" valign=\"top\"\u003e\n \u003cp\u003e224 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e45 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.845070422535212%\" valign=\"top\"\u003e\n \u003cp\u003eKRAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.950704225352112%\" valign=\"top\"\u003e\n \u003cp\u003e9 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.788732394366198%\" valign=\"top\"\u003e\n \u003cp\u003e32 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02112676056338%\" valign=\"top\"\u003e\n \u003cp\u003e69 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e10 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.845070422535212%\" valign=\"top\"\u003e\n \u003cp\u003eNRAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.950704225352112%\" valign=\"top\"\u003e\n \u003cp\u003e4 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.788732394366198%\" valign=\"top\"\u003e\n \u003cp\u003e4 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02112676056338%\" valign=\"top\"\u003e\n \u003cp\u003e9 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.845070422535212%\" valign=\"top\"\u003e\n \u003cp\u003eAKT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.950704225352112%\" valign=\"top\"\u003e\n \u003cp\u003e5 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.788732394366198%\" valign=\"top\"\u003e\n \u003cp\u003e6 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02112676056338%\" valign=\"top\"\u003e\n \u003cp\u003e29 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e5 (6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003ens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"83.80281690140845%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eHormone receptor positivity (based on IHC)\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.845070422535212%\" valign=\"top\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.950704225352112%\" valign=\"top\"\u003e\n \u003cp\u003e62 (90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.788732394366198%\" valign=\"top\"\u003e\n \u003cp\u003e115 (97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02112676056338%\" valign=\"top\"\u003e\n \u003cp\u003e252 (90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e60 (75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.845070422535212%\" valign=\"top\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.950704225352112%\" valign=\"top\"\u003e\n \u003cp\u003e55 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.788732394366198%\" valign=\"top\"\u003e\n \u003cp\u003e102 (86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02112676056338%\" valign=\"top\"\u003e\n \u003cp\u003e246 (88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e52 (65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"83.80281690140845%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eHomologous recombination repair\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.845070422535212%\" valign=\"top\"\u003e\n \u003cp\u003eBRCA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.950704225352112%\" valign=\"top\"\u003e\n \u003cp\u003e27 (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.788732394366198%\" valign=\"top\"\u003e\n \u003cp\u003e17 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02112676056338%\" valign=\"top\"\u003e\n \u003cp\u003e9 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e7 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.845070422535212%\" valign=\"top\"\u003e\n \u003cp\u003eBRCA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.950704225352112%\" valign=\"top\"\u003e\n \u003cp\u003e49 (71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.788732394366198%\" valign=\"top\"\u003e\n \u003cp\u003e31 (26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02112676056338%\" valign=\"top\"\u003e\n \u003cp\u003e28 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e8 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.845070422535212%\" valign=\"top\"\u003e\n \u003cp\u003eATM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.950704225352112%\" valign=\"top\"\u003e\n \u003cp\u003e55 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.788732394366198%\" valign=\"top\"\u003e\n \u003cp\u003e40 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02112676056338%\" valign=\"top\"\u003e\n \u003cp\u003e33 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e8 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"83.80281690140845%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eTGF-\u0026beta;\u003c/em\u003e\u003cem\u003e\u0026nbsp;pathway\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.845070422535212%\" valign=\"top\"\u003e\n \u003cp\u003eSMAD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.950704225352112%\" valign=\"top\"\u003e\n \u003cp\u003e9 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.788732394366198%\" valign=\"top\"\u003e\n \u003cp\u003e6 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02112676056338%\" valign=\"top\"\u003e\n \u003cp\u003e2 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"83.80281690140845%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eReceptor tyrosine kinase\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.845070422535212%\" valign=\"top\"\u003e\n \u003cp\u003eKIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.950704225352112%\" valign=\"top\"\u003e\n \u003cp\u003e30 (43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.788732394366198%\" valign=\"top\"\u003e\n \u003cp\u003e11 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02112676056338%\" valign=\"top\"\u003e\n \u003cp\u003e3 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e1 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.845070422535212%\" valign=\"top\"\u003e\n \u003cp\u003ePDGFRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.950704225352112%\" valign=\"top\"\u003e\n \u003cp\u003e24 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.788732394366198%\" valign=\"top\"\u003e\n \u003cp\u003e9 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.02112676056338%\" valign=\"top\"\u003e\n \u003cp\u003e11 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e2 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.197183098591548%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003ea: non-significant (ns) refers to p\u0026gt;0.05\u003c/p\u003e\n\u003cp\u003eb: Immunohistochemistry (IHC)\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Endometrial cancer, Cancer staging, Molecular classification, Prognostication, Survival","lastPublishedDoi":"10.21203/rs.3.rs-4451579/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4451579/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eObjective: \u003c/strong\u003e\u003c/em\u003eThe International Federation of Gynecology and Obstetrics (FIGO) 2023 staging system represents a refined classification with a comprehensive and improved prognostic significance for endometrial cancer (EC). Our objective is to further explore the clinical utility of the 2023 FIGO staging system in China.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethods: \u003c/strong\u003e\u003c/em\u003e\u0026nbsp;In current study,\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003ewe conducted a retrospective analysis involving 547 patients who received standard surgeries and underwent genetic testing using multigene next-generation sequencing panels between December 2018 and December 2023 at Fudan University Shanghai Cancer Center. The genomic and clinical data of all patients were analyzed, and patients were restaged according to the 2023 staging systems.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/em\u003e After rearrangement of FIGO 2023 staging system in 547 cases, 345 cases of stage I, 107 cases of stage II, 69 cases of stage III, and 26 cases of stage IV were observed. The median follow-up duration was 19.0 months. Stage rearrangements occurred in 92/547 (16.8%) patients. 63 cases and 53 cases were re-classified into IAmPOLEmut and IICmp53abn from early stages (stage I and II), respectively, due to the molecular subtype of POLEmut and p53abn. Among stage I diseases, the 3-year PFS rate was 92.7% and 95.3% according to the 2009 and 2023 FIGO staging systems, respectively. The 3-year PFS of stage II patients under the 2023 FIGO staging system was lower than that of 2009 (3-year PFS: 85.0% versus 90.9%), especially in substage IIC and IICmp53abn. 3 cases (12%) of stage IIIA were rearranged into stage IA3, with 3-year PFS rates of 90.9% versus 100%, respectively. In genomic sequencing analysis, the most prevalent gene alterations occur in PTEN and PIK3CA.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003e\u003c/em\u003ePredominant stage reclassifications were observed in early-stage diseases, emphasizing the enhanced precision provided by the new 2023 FIGO staging system in EC prognosis, which may facilitate more accurate therapeutic strategies. Distinct gene alterations also help to understand the characteristics of different subtypes.\u003c/p\u003e","manuscriptTitle":"Clinical application of FIGO 2023 staging system of endometrial cancer in a Chinese cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 23:02:40","doi":"10.21203/rs.3.rs-4451579/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-18T06:32:20+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"310293948104254840536959574247702587596","date":"2024-06-17T14:20:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"284166875998698668733100966957774873353","date":"2024-06-17T05:05:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-17T02:16:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196620457193894718948405217593758924676","date":"2024-06-17T02:08:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-16T18:25:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158116096043692280743756340782372670361","date":"2024-06-15T19:50:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-15T17:02:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327220961923164508332723956307304504760","date":"2024-06-15T15:00:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63124769170242662957201708571179748483","date":"2024-06-15T12:53:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197411276441727432027908987174564139376","date":"2024-06-15T09:57:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"83078040242074107737431724458183920122","date":"2024-05-29T08:06:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-29T00:53:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-28T14:09:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-23T15:58:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-23T15:58:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2024-05-21T01:59:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e74a0d03-954b-4d91-940e-1074e30e1080","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-01T16:20:54+00:00","versionOfRecord":{"articleIdentity":"rs-4451579","link":"https://doi.org/10.1186/s12885-024-12633-8","journal":{"identity":"bmc-cancer","isVorOnly":false,"title":"BMC Cancer"},"publishedOn":"2024-07-18 16:13:27","publishedOnDateReadable":"July 18th, 2024"},"versionCreatedAt":"2024-06-07 23:02:40","video":"","vorDoi":"10.1186/s12885-024-12633-8","vorDoiUrl":"https://doi.org/10.1186/s12885-024-12633-8","workflowStages":[]},"version":"v1","identity":"rs-4451579","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4451579","identity":"rs-4451579","version":["v1"]},"buildId":"CiT4i_kKBbxQbnFL0ufpk","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}