The prognostic importance of features of myometrial invasion in endometrial endometrioid carcinoma

The prognostic importance of features of myometrial invasion in endometrial endometrioid carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The prognostic importance of features of myometrial invasion in endometrial endometrioid carcinoma Maya Pasternak, Roy Kessous, Benzion Samueli, Jacob Dreiher, Mihai Meirovitz, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6228646/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jul, 2025 Read the published version in Archives of Gynecology and Obstetrics → Version 1 posted 11 You are reading this latest preprint version Abstract Purpose The depth of myometrial invasion (MI) is known to have a prognostic value in endometrial carcinoma (EC), and the FIGO 50% cutoff is widely accepted; however, recent studies have suggested other measurements such as the absolute depth of invasion and tumor-free distance (TFD) from the serosal surface to also be predictive. The aim of this study was to assess the association between the FIGO cutoff and other measures with overall survival and disease-free survival of patients. Methods This is a retrospective analysis of a cohort of 248 women diagnosed with stage I endometrioid endometrial carcinoma, treated at Soroka University Medical Center between 2006 and 2020. Clinical and pathological data were collected and analyzed. ROC analysis was used to define the best cutoffs in all three categories (MI, absolute depth and TDF). Survival analyses were then conducted using Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression. Results Absolute myometrial invasion (MI) to the depth of 1 cm significantly predicts overall survival (log-rank, p = 0.009). Additionally, a 33% MI cutoff demonstrated potential for better outcome prediction as compared to the commonly used 50% MI threshold, though it did not reach statistical significance. Tumor-free distance (TFD) from the serosal surface was not significantly associated with recurrence. Conclusions MI depth of more than 1 cm is a valid indicator of patient outcome. Additionally, a cutoff of 33% MI probably has a better prognostic value than the current cutoff 50%. Endometrial carcinoma (EC) myometrial invasion (MI) tumor-free distance (TFD) International Federation of Gynecology and Obstetrics (FIGO) Figures Figure 1 Figure 2 Introduction Cancer of the uterine corpus is the most common gynecological cancer in the developed world [ 1 ] with a rise in incidence and mortality in recent years [ 2 ]. Endometrial carcinoma (EC) is the most common type of uterine cancer and accounts for 75–80% of all uterine malignancies [ 3 ]. EC is historically classified into two groups, type I and type II, based on epidemiology, histopathology, prognosis, and treatment [ 3 ]. Type I EC accounts for most cases (85%) with tumors of endometrioid histology. Type I EC have relatively good five-year survival and low recurrence rate. They primarily arise in obese perimenopausal women with a background of endometrial hyperplasia due to increased exposure to estrogen [ 4 ]. The prognosis, as well as management decisions, in patients diagnosed with EC are based on the International Federation of Gynecology and Obstetrics (FIGO) staging system using, among other features, evaluation of tumor architectural components and nuclear grading [ 5 , 6 ]. The 2023 FIGO revision [ 6 ] represents a significant shift in endometrial cancer staging by integrating both histological type and grade as key components of the staging criteria. The new system distinguishes between less-aggressive histological types (comprising low-grade endometrioid carcinomas) and aggressive types, with some low-grade endometrioid tumors being down-staged based on their favorable prognostic features. However, the implementation of the 2023 FIGO system is still in transition, and this ongoing change period highlights the importance of careful evaluation of prognostic factors including as myometrial invasion. One very important prognostic factor in the FIGO staging system is the extent of tumor myometrial invasion (MI). The extent of tumor MI is typically calculated as the percentage of tumor invasion of the myometrium layer out of the total myometrial wall thickness [ 7 ]. MI holds great importance as a prognostic index and therefore is an integral part of the pathological evaluation completed for every sample suspected of endometrial cancer according to the protocol of the College of American Pathologists (CAP) [ 8 ]. Patients with deep MI may require a more advanced surgical staging procedure, as well as possible consideration of adjuvant treatment, due to the higher risk of pelvic lymph node metastasis and poorer prognosis [ 9 ]. In addition, deep MI correlates with a higher incidence of other poor prognostic factors such as uterine cervix extension, positive peritoneal fluid cytology, adnexal involvement, and lymph-vascular space invasion [ 7 ]. Historically, the FIGO staging system classified deep myometrial invasion using a one-third (33%) threshold before transitioning to a one-half (50%) cutoff in the 2009 update [ 10 ]. Studies at the time suggested that 50% invasion correlated more strongly with lymphovascular space invasion (LVSI) and nodal metastases [ 11 , 13 ]. The adoption of a 50% threshold aimed to enhance the prognostic accuracy of the staging system and has proven to be a relatively reliable predictor of outcome [ 11 – 13 ]. The change was also made to improve reproducibility among pathologists, as differentiating between one-third and one-half invasion was often challenging. However, whether this change truly optimized prognostic accuracy remains a topic of debate, and the specific rationale for choosing 50% is not clear [ 14 , 15 ]. Pathologically, invasion of the myometrial wall is sometimes challenging to assess, since the endometrial-myometrial border is not always sharp and clear. Consequently, pathologists occasionally use estimations to conclude whether the invasion of the myometrial wall is greater than or less than 50 percent [ 14 , 16 ]. These estimations include using the arcuate vascular plexus (AVP) in the myometrium as a landmark for over fifty percent invasion and assuming that the anterior and posterior uterine walls are of the same thickness [ 16 ]. Given those challenges, previous studies evaluated the tumor-free distance from the uterine serosa (TFD) as a possible, more accurate, and objective assessment of myometrial invasion [ 17 – 19 ]. In addition, recent studies have tried to evaluate whether assessing the overall depth of invasion might be the best variable to correlate with outcome. Part of these studies showed that TFD to the serosa, as well as absolute depth of invasion (DOI) [ 18 ], may be better prognostic indicators compared to the percentage of myometrial invasion [ 17 , 21 ]. The aim of the current study, focusing on a population of patients diagnosed with early-stage EC, is to assess and compare the prognostic value of various depth of invasion measurements. We first aim to understand whether the current FIGO classification cutoff of 50% is a reliable predictor of outcome. In addition, we compared this cutoff to the absolute DOI, as well as TFD, in order to assess whether they offer additional important information or even better prediction. Given the complexity of management decisions in early-stage EC, applying more accurate methods for identifying patients at higher risk for disease recurrence and mortality would facilitate adequate treatment, ultimately resulting in improved prognosis. Methods This is a retrospective cohort study based on a population of women diagnosed with early-stage endometrioid type endometrial carcinoma, treated at Soroka University Medical Center between 2006 and 2020. All patients underwent surgical staging as part of their treatment. Participants were included if they were 18 or older, had a confirmed diagnosis of Stage IA or IB endometrioid carcinoma. Furthermore, all patients had complete clinical follow-up data available for at least five years post-surgery, allowing for long-term survival analysis. Patients were excluded from the study if they had been diagnosed with a non-endometrioid type endometrial malignancy, or had advanced disease stage at diagnosis. Additionally, cases with incomplete surgical staging procedure or missing follow-up data were excluded from the analysis to ensure the robustness of the study findings. In addition to the primary clinical data, the severity of comorbid diseases was recorded and scored using the Charlson Comorbidity Index (CCI) [ 22 ], accounting for both the number and severity of the patient’s comorbid conditions. CCI scores were used to classify patients into five categories based on their overall comorbidity burden: no comorbidity (CCI score of 0), low comorbidity (CCI score of 1), moderate comorbidity (CCI score of 2), high comorbidity (CCI scores of 3–4), and severe comorbidity (CCI score ≥ 5). This enabled a comprehensive assessment of how comorbidities might influence patients survival and clinical outcomes in the context of endometrial carcinoma. Data was collected from hospital database which provided comprehensive clinical and pathological information. Pathological data were extracted from histopathology reports and included tumor grade, depth of myometrial invasion (both as a percentage and in millimeters), and the TFD. For the purpose of this study, relied on the original pathology reports at the time of hysterectomy for data extraction, and reviewed the slides on an as-needed bases (eg missing or unclear data points). Clinical outcomes, including overall and disease-free survival were analyzed. Data Analysis Descriptive statistics were used to summarize the baseline characteristics of the study population. Categorical variables were expressed as frequencies and percentages, while continuous variables, such as patient’s age and depth of invasion, were summarized using means, standard deviations, medians and interquartile ranges. To evaluate the prognostic value of the different invasion parameters, ROC (Receiver Operating Characteristic) curve analysis was performed for each variable, with the area under the curve (AUC) used to determine the most accurate predictor of survival and various cut-offs checked for sensitivity and specificity. Kaplan-Meier survival curves were constructed to estimate overall survival and disease-free survival. Log-rank test was used to compare survival between the different invasion parameter groups. Univariate Cox proportional hazards regression was applied to assess the relationship between each variable and survival outcomes. Variables with a p-value of less than 0.05 in the univariate analysis were entered into a multivariate Cox regression model to adjust for potential confounders, including age and tumor grade. Assuming a 20% difference in survival between patients with more invasive cancer vs. less invasive cancer and a significance level of 0.05 yields a sample size of at least 180 patients to ensure a power of 90% (computed using sample size immediate command in Stata version 12). Results A total of 248 women with stage I endometrioid endometrial carcinoma were included in the study. The mean age of the cohort was 64 years (SD ± 8.5). Tumor characteristics and patient outcomes varied considerably within the cohort, as summarized in Table 1 . Regarding tumor grade, the majority of cases were FIGO grade 1 (55.2%, n = 137), followed by grade 2 (29.8%, n = 74), and grade 3 (14.9%, n = 37). During the follow-up period, 31.9% of patients (n = 79) had died, with a mean survival time of 68.3 months (SD ± 52.9). Disease relapse occurred in 28.6% (n = 71) of patients. Table 1 Baseline characteristics of the study population (N = 248) Age (Years) Mean (SD) 64.2 (± 8.5) Median [Min, Max] 66.9 [41.5, 84.8] Death (n = 247) 79 (32.0%) Survival Time (Months) Mean (SD) 68.3 (± 52.9) Median [Min, Max] 51.3 [0.5, 185.1] Relapse 71 (28.63%) Smoking 32 (12.9%) FIGO Grade 1 137 (55.2%) 2 74 (29.8%) 3 37 (14.9%) Charlson Comorbidity Index No Comorbidity 30 (12.1%) Low Comorbidity 33 (13.3%) Moderate Comorbidity 45 (18.1%) High Comorbidity 48 (19.4%) Severe Comorbidity 92 (37.1%) Comorbid Conditions Congestive Heart Failure 8 (3.2%) Peripheral Vascular Disease 6 (2.4%) Diabetes Melitus 58 (23.4%) Chronic Kidney Disease 10 (4.0%) Myocardial infarction 10 (4.0%) Chronic Obstructive Pulmonary Disease 18 (7.3%) Liver Disease 20 (8.1%) Comorbidities were prevalent in our study population, as assessed by the Charlson Comorbidity Index. A significant proportion of patients (37.1%, n = 92) had severe comorbidity, while only 12.1% (n = 30) had no comorbidity. The remaining patients were distributed across low (13.3%, n = 33), moderate (18.1%, n = 45), and high (19.4%, n = 48) comorbidity categories. The most common comorbidity was diabetes mellitus, present in 58 participants (23.4%). Chronic obstructive pulmonary disease (COPD) was observed in 18 participants (7.3%), while liver disease was noted in 20 participants (8.1%). Table 2 presents the characteristics of myometrial invasion in our cohort. The mean depth of myometrial invasion was 0.68 cm (SD ± 0.62), with a median of 0.5 cm. When expressed as a percentage, the mean myometrial invasion was 37% (SD ± 30%), with a median of 31%. The mean distance from the serosal surface was 1.21 cm (SD ± 0.78), with a median of 1.1 cm (range: 0–6.5 cm). These data demonstrate considerable variability in the extent of myometrial invasion among patients with early-stage endometrial cancer. Table 2 Myometrial invasion characteristics (N = 248) Depth of Myometrial Invasion (Cm) Mean (SD) 0.68 (± 0.62) Median [Min, Max] 0.5 [0, 3.9] Depth of Myometrial Invasion (%) Mean (SD) 0.37 (± 0.30) Median [Min, Max] 0.31 [0, 1] Distance from Serosal Surface (Cm) Mean (SD) 1.21 (± 0.78) Median [Min, Max] 1.1 [0, 6.5] Lymphovascular Invasion 41 (16.5%) To evaluate and compare the predictive ability of the myometrial invasion depth, percentage, and distance from the serosal surface, we performed ROC curve analyses for all three estimators (Fig. 1 ). The area under the curve (AUC) for the depth of myometrial invasion (in cm) was 0.575 (95% CI: 0.497–0.653; p = 0.05), indicating a modest discriminatory ability (Fig. 1 A). The AUC for the percentage of myometrial invasion was 0.546 (p = 0.244), with optimal cutoff of 33%, yielding the highest correlation with outcome (Figs. 1 B). As for the distance from the serosal surface, AUC was 0.492 (p = 0.839) (Fig. 1 C). For mortality, the log-rank test comparing survival distributions at the 50% myometrial invasion threshold was not statistically significant (p = 0.182). However, when using the 1 cm invasion cutoff, the log-rank test showed a significant difference in survival distributions (p = 0.009), suggesting that the 1 cm threshold may be a more relevant predictor of survival (Fig. 2 A and Fig. 2 B). Similar comparisons were made for disease relapse. Both the log-rank test for the 50% myometrial invasion threshold and for the 1 cm invasion failed to show significant differences in relapse-free survival (p = 0.583 and p = 0.349, respectively, Fig. 2 C and Fig. 2 D). We proceeded with Cox proportional hazards regression analysis to further assess the impact of myometrial invasion depth (1 cm) on overall survival, controlling for other covariates. In the final model, myometrial invasion at 1 cm was not independently associated with survival or relapse. Table 3 presents full details of the Cox regression analysis, including hazard ratios and confidence intervals. Table 3 Cox Proportional Hazards Regression Analysis for Overall Survival Hazard ratio P-value 95% Confidence Interval Lower Upper Age (Years) 1.028 .019* 1.005 1.051 Invasion Distance (1 Cm) .725 .309 .389 1.348 Note . * Indicates statistical significance at p < .05. Discussion Endometrial cancer (EC) is one of the most common gynecological malignancies, and its incidence continues to rise globally. While the prognosis for early-stage EC is generally favorable, identifying reliable prognostic markers is crucial for tailoring treatment strategies and improving patient outcomes. Myometrial invasion (MI) is a critical prognostic factor influencing recurrence risk and overall survival. In the current study, we evaluated the predictive value of the 50% cutoff for MI, a widely accepted threshold in the literature related to patient outcome, recurrence, and overall survival in early-stage EC. The FIGO staging system historically classified deep myometrial invasion using a one-third (33%) threshold before transitioning to a one-half (50%) cutoff in the 2009 update. Our results show that while 50% or more MI may relate to patient outcomes, it does not independently predict recurrence or overall survival. Interestingly, according to the results of our study, a cutoff of 33% MI provided stronger prognostication. Although the 50% cutoff has proven to be a reliable prognostic factor, our study suggests that the original 33% cutoff is a more accurate predictor of outcomes. This challenges the 2009 shift in the staging system and emphasizes the importance of further investigation. In addition, we looked for an alternative for the current measurement method by evaluating several other MI parameters, such as absolute invasion depth and distance from uterine serosa. In this analysis, using absolute DOI with a cutoff of one centimeter appeared to correlate better with overall survival. DOI was our study's most statistically significant parameter with the best correlation to outcome. Our findings agree with a study by Doghri et al. [ 20 ] which showed that DOI was superior to TFD as well as MI percentage as a prognostic factor. In their study the best DOI cutoff was found to be of 3 mm. Regarding distance from the serosa, our results are consistent with previously published results from a study by Oge et al. [ 18 ] that assessed 133 patients with early stage IB endometrioid endometrial cancer. The authors found that TFD did not significantly predict recurrence or survival outcomes and is not an independent prognostic factor in early EC patients. Contradicting results were presented by Chattopadhyay [ 17 ] demonstrating TFD is an independent predictor of survival and recurrence. Our findings regarding alternative MI measurements are particularly relevant considering the 2023 FIGO staging revision, which takes under consideration the complexity of tumor assessment by incorporating multiple prognostic factors beyond the traditional 50% MI cutoff. Our finding that a 33% MI cutoff might be more prognostically significant than the traditional 50% is particularly interesting in the context of the evolving FIGO criteria. The 2023 FIGO revision acknowledges that historical staging parameters, including the 50% MI cutoff, may need refinement as our understanding of endometrial cancer biology improves. This aligns with our observation that alternative measurements, such as absolute depth of invasion, might better predict outcome in early-stage disease. Furthermore, the new FIGO system's emphasis on integrating multiple prognostic factors supports our approach of examining various invasion parameters rather than relying solely on percentage-based measurements. Study strengths and limitations Our study benefited from relatively long follow-up as well as the review and merging of pathological and clinical data. This allowed us to capture important clinical outcomes and provide a detailed evaluation of prognostic factors in early-stage endometrial cancer (EC). It ensures that the correlations between myometrial invasion (MI) and patient outcomes are strong over time. Our sample size allowed for a thorough, detailed clinical and pathological analysis. The revision of pathological slides under question ensured the consistency and accuracy of our data. However, the present study has some limitations as well. This is a single-center study, and the experience of other medical centers may be different. The Israeli population might have different features than the international patient population. Therefore, we acknowledge that more extensive multicenter studies would be beneficial in order to confirm our findings and detect additional prognostic factors. EC (especially early stage) is often an indolent disease, requiring large cohorts and extended follow-up period to fully capture the disease course and create more accurate prognostic models for early-stage endometrial cancer. The study period is from 2006 to 2020, a period in which changes in therapy and prognosis might have had an important implication on prognosis. Conclusions Our findings suggest that alternative cutoffs, particularly absolute myometrial invasion (MI) of 1 cm and optionally 33% MI cutoff, may offer better prognostic value for overall survival in early-stage endometrial endometrioid carcinoma compared to the traditionally used 50% cutoff. Furthermore, investigating various types of MI measures may contribute to ongoing efforts to refine prognostic markers in early-stage EC, potentially improving treatment strategies for future patients. Declarations Ethics approval This study was performed in line with the principles of the Declaration of Helsinki and approved by the Institutional Review Board of Soroka University Medical Center (Approval No. 0192-23-SOR). Funding and Competing Interests The authors declare that no funds, grants, or other support were received during the preparation of this manuscript, The authors have no relevant financial or non-financial interests to disclose. Author Contribution M.P- data collection, manuscript writingR.K- project development, manuscript writing and editingB.S- data collection, manuscript editingJ.D- data analysis, Manuscript editingM.M- Project developmentS.D- data collection, project developmentR.S.L- project development, manuscript editing References Braun MM, Overbeek-Wager E, Grumbo RJ. Diagnosis and Management of Endometrial Cancer. Am Fam Physician 2016 Mar 15;93(6):468-74. Available from: https://www.aafp.org/pubs/afp/issues/2016/0315/p468.html Raglan O, Kalliala I, Markozannes G, Cividini S, Gunter MJ, Nautiyal J, et al. Risk factors for endometrial cancer: An umbrella review of the literature. Int J Cancer. 2019;145(7):1719-30. Available from: https://onlinelibrary.wiley.com/doi/10.1002/ijc.31961 Passarello K, Kurian S, Villanueva V. Endometrial Cancer: An Overview of Pathophysiology, Management, and Care. Semin Oncol Nurs. 2019;35(2):157-65. 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Cite Share Download PDF Status: Published Journal Publication published 16 Jul, 2025 Read the published version in Archives of Gynecology and Obstetrics → Version 1 posted Editorial decision: Revision requested 07 May, 2025 Reviews received at journal 21 Apr, 2025 Reviews received at journal 10 Apr, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviewers agreed at journal 30 Mar, 2025 Reviewers agreed at journal 30 Mar, 2025 Reviewers invited by journal 30 Mar, 2025 Editor assigned by journal 15 Mar, 2025 Submission checks completed at journal 15 Mar, 2025 First submitted to journal 14 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6228646","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443288836,"identity":"b52f9de5-ed16-4542-92c9-40f06c5736ba","order_by":0,"name":"Maya Pasternak","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYHAD5gNAQkKGFC1sCSAtPKRo4TEAkwTV6bafMfzAUHE4n1+65/OrGzUWPAzsh49uwKfF7EyOsQTDmcOWM+ec3WadcwzoMJ60tBt4tRzIMZBgbEszMLiRu804hw2oRYLHDL+W82+MfzD+A2nJeWac848YLTdyzCQYG2xAWpgf57YRpeVZmUXCMRsDyRlpZsy5fRI8bAT9cj55840PNRIG/BLJjz/nfKuT42c/fAyvFgYGDgOGBAiLTQJM4lcOAuwPYCzmD4RVj4JRMApGwUgEACeLRKc1qyQUAAAAAElFTkSuQmCC","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":true,"prefix":"","firstName":"Maya","middleName":"","lastName":"Pasternak","suffix":""},{"id":443288839,"identity":"a9f12c99-bbf6-4d69-91ed-735391bbc0c8","order_by":1,"name":"Roy Kessous","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Roy","middleName":"","lastName":"Kessous","suffix":""},{"id":443288842,"identity":"d6bb7cf4-d49e-4bfe-a50e-55b0333fa355","order_by":2,"name":"Benzion Samueli","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Benzion","middleName":"","lastName":"Samueli","suffix":""},{"id":443288843,"identity":"0cb71c61-e5fc-4806-aad9-7bd2dcfaeb35","order_by":3,"name":"Jacob Dreiher","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Jacob","middleName":"","lastName":"Dreiher","suffix":""},{"id":443288844,"identity":"9371b659-3b82-45fa-a6cf-baefccf18554","order_by":4,"name":"Mihai Meirovitz","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Mihai","middleName":"","lastName":"Meirovitz","suffix":""},{"id":443288845,"identity":"89748919-da09-4747-beb7-f234b72f1a85","order_by":5,"name":"Sharon Davidesko","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Sharon","middleName":"","lastName":"Davidesko","suffix":""},{"id":443288846,"identity":"2b7d2da4-f7ff-4850-af1c-d3c9520ae7a8","order_by":6,"name":"Ruthy Shaco Levy","email":"","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":false,"prefix":"","firstName":"Ruthy","middleName":"Shaco","lastName":"Levy","suffix":""}],"badges":[],"createdAt":"2025-03-14 18:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6228646/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6228646/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00404-025-08103-6","type":"published","date":"2025-07-16T15:56:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":81030113,"identity":"33115c6c-b68d-4a74-a02f-18e35d7b0e53","added_by":"auto","created_at":"2025-04-21 11:14:02","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":271991,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eROC Curves for Myometrial Invasion Depth, Percentage, and Distance from Serosa\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eNote\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e \u003cem\u003eThe red diagonal line represents the performance of a random classifier. The blue lines represent the discriminatory performance of the various prognostic factors.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6228646/v1/e3182407aefacc0fe345626f.jpeg"},{"id":81030112,"identity":"da593d64-695f-4c99-9ef5-5f55cea94886","added_by":"auto","created_at":"2025-04-21 11:14:02","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":354291,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eKaplan-Meier survival curves comparing 50% myometrial invasion and 1 cm myometrial invasion for both overall survival and relapse-free survival\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6228646/v1/ae2706889da8e891a546b28d.jpeg"},{"id":87219318,"identity":"e2f46ff5-e5e8-4531-9b64-35d093cfcd4d","added_by":"auto","created_at":"2025-07-21 16:03:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1204051,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6228646/v1/6b067ee0-5e06-427a-a269-6f15cf2dd737.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The prognostic importance of features of myometrial invasion in endometrial endometrioid carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCancer of the uterine corpus is the most common gynecological cancer in the developed world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] with a rise in incidence and mortality in recent years [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Endometrial carcinoma (EC) is the most common type of uterine cancer and accounts for 75\u0026ndash;80% of all uterine malignancies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. EC is historically classified into two groups, type I and type II, based on epidemiology, histopathology, prognosis, and treatment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Type I EC accounts for most cases (85%) with tumors of endometrioid histology. Type I EC have relatively good five-year survival and low recurrence rate. They primarily arise in obese perimenopausal women with a background of endometrial hyperplasia due to increased exposure to estrogen [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe prognosis, as well as management decisions, in patients diagnosed with EC are based on the International Federation of Gynecology and Obstetrics (FIGO) staging system using, among other features, evaluation of tumor architectural components and nuclear grading [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The 2023 FIGO revision [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] represents a significant shift in endometrial cancer staging by integrating both histological type and grade as key components of the staging criteria. The new system distinguishes between less-aggressive histological types (comprising low-grade endometrioid carcinomas) and aggressive types, with some low-grade endometrioid tumors being down-staged based on their favorable prognostic features. However, the implementation of the 2023 FIGO system is still in transition, and this ongoing change period highlights the importance of careful evaluation of prognostic factors including as myometrial invasion.\u003c/p\u003e \u003cp\u003eOne very important prognostic factor in the FIGO staging system is the extent of tumor myometrial invasion (MI). The extent of tumor MI is typically calculated as the percentage of tumor invasion of the myometrium layer out of the total myometrial wall thickness [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. MI holds great importance as a prognostic index and therefore is an integral part of the pathological evaluation completed for every sample suspected of endometrial cancer according to the protocol of the College of American Pathologists (CAP) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Patients with deep MI may require a more advanced surgical staging procedure, as well as possible consideration of adjuvant treatment, due to the higher risk of pelvic lymph node metastasis and poorer prognosis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In addition, deep MI correlates with a higher incidence of other poor prognostic factors such as uterine cervix extension, positive peritoneal fluid cytology, adnexal involvement, and lymph-vascular space invasion [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHistorically, the FIGO staging system classified deep myometrial invasion using a one-third (33%) threshold before transitioning to a one-half (50%) cutoff in the 2009 update [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Studies at the time suggested that 50% invasion correlated more strongly with lymphovascular space invasion (LVSI) and nodal metastases [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The adoption of a 50% threshold aimed to enhance the prognostic accuracy of the staging system and has proven to be a relatively reliable predictor of outcome [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The change was also made to improve reproducibility among pathologists, as differentiating between one-third and one-half invasion was often challenging. However, whether this change truly optimized prognostic accuracy remains a topic of debate, and the specific rationale for choosing 50% is not clear [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePathologically, invasion of the myometrial wall is sometimes challenging to assess, since the endometrial-myometrial border is not always sharp and clear. Consequently, pathologists occasionally use estimations to conclude whether the invasion of the myometrial wall is greater than or less than 50 percent [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These estimations include using the arcuate vascular plexus (AVP) in the myometrium as a landmark for over fifty percent invasion and assuming that the anterior and posterior uterine walls are of the same thickness [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven those challenges, previous studies evaluated the tumor-free distance from the uterine serosa (TFD) as a possible, more accurate, and objective assessment of myometrial invasion [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In addition, recent studies have tried to evaluate whether assessing the overall depth of invasion might be the best variable to correlate with outcome. Part of these studies showed that TFD to the serosa, as well as absolute depth of invasion (DOI) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], may be better prognostic indicators compared to the percentage of myometrial invasion [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aim of the current study, focusing on a population of patients diagnosed with early-stage EC, is to assess and compare the prognostic value of various depth of invasion measurements. We first aim to understand whether the current FIGO classification cutoff of 50% is a reliable predictor of outcome. In addition, we compared this cutoff to the absolute DOI, as well as TFD, in order to assess whether they offer additional important information or even better prediction. Given the complexity of management decisions in early-stage EC, applying more accurate methods for identifying patients at higher risk for disease recurrence and mortality would facilitate adequate treatment, ultimately resulting in improved prognosis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis is a retrospective cohort study based on a population of women diagnosed with early-stage endometrioid type endometrial carcinoma, treated at Soroka University Medical Center between 2006 and 2020. All patients underwent surgical staging as part of their treatment. Participants were included if they were 18 or older, had a confirmed diagnosis of Stage IA or IB endometrioid carcinoma. Furthermore, all patients had complete clinical follow-up data available for at least five years post-surgery, allowing for long-term survival analysis. Patients were excluded from the study if they had been diagnosed with a non-endometrioid type endometrial malignancy, or had advanced disease stage at diagnosis. Additionally, cases with incomplete surgical staging procedure or missing follow-up data were excluded from the analysis to ensure the robustness of the study findings.\u003c/p\u003e \u003cp\u003eIn addition to the primary clinical data, the severity of comorbid diseases was recorded and scored using the Charlson Comorbidity Index (CCI) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], accounting for both the number and severity of the patient\u0026rsquo;s comorbid conditions. CCI scores were used to classify patients into five categories based on their overall comorbidity burden: no comorbidity (CCI score of 0), low comorbidity (CCI score of 1), moderate comorbidity (CCI score of 2), high comorbidity (CCI scores of 3\u0026ndash;4), and severe comorbidity (CCI score\u0026thinsp;\u0026ge;\u0026thinsp;5). This enabled a comprehensive assessment of how comorbidities might influence patients survival and clinical outcomes in the context of endometrial carcinoma.\u003c/p\u003e \u003cp\u003eData was collected from hospital database which provided comprehensive clinical and pathological information. Pathological data were extracted from histopathology reports and included tumor grade, depth of myometrial invasion (both as a percentage and in millimeters), and the TFD. For the purpose of this study, relied on the original pathology reports at the time of hysterectomy for data extraction, and reviewed the slides on an as-needed bases (eg missing or unclear data points). Clinical outcomes, including overall and disease-free survival were analyzed.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to summarize the baseline characteristics of the study population. Categorical variables were expressed as frequencies and percentages, while continuous variables, such as patient\u0026rsquo;s age and depth of invasion, were summarized using means, standard deviations, medians and interquartile ranges.\u003c/p\u003e \u003cp\u003eTo evaluate the prognostic value of the different invasion parameters, ROC (Receiver Operating Characteristic) curve analysis was performed for each variable, with the area under the curve (AUC) used to determine the most accurate predictor of survival and various cut-offs checked for sensitivity and specificity. Kaplan-Meier survival curves were constructed to estimate overall survival and disease-free survival. Log-rank test was used to compare survival between the different invasion parameter groups. Univariate Cox proportional hazards regression was applied to assess the relationship between each variable and survival outcomes. Variables with a p-value of less than 0.05 in the univariate analysis were entered into a multivariate Cox regression model to adjust for potential confounders, including age and tumor grade. Assuming a 20% difference in survival between patients with more invasive cancer vs. less invasive cancer and a significance level of 0.05 yields a sample size of at least 180 patients to ensure a power of 90% (computed using sample size immediate command in Stata version 12).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 248 women with stage I endometrioid endometrial carcinoma were included in the study. The mean age of the cohort was 64 years (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5). Tumor characteristics and patient outcomes varied considerably within the cohort, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Regarding tumor grade, the majority of cases were FIGO grade 1 (55.2%, n\u0026thinsp;=\u0026thinsp;137), followed by grade 2 (29.8%, n\u0026thinsp;=\u0026thinsp;74), and grade 3 (14.9%, n\u0026thinsp;=\u0026thinsp;37). During the follow-up period, 31.9% of patients (n\u0026thinsp;=\u0026thinsp;79) had died, with a mean survival time of 68.3 months (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;52.9). Disease relapse occurred in 28.6% (n\u0026thinsp;=\u0026thinsp;71) of patients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eBaseline characteristics of the study population\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;248)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.2 (\u0026plusmn;\u0026thinsp;8.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.9 [41.5, 84.8]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeath (n\u0026thinsp;=\u0026thinsp;247)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (32.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurvival Time (Months)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.3 (\u0026plusmn;\u0026thinsp;52.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.3 [0.5, 185.1]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRelapse\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (28.63%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFIGO Grade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137 (55.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (29.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharlson Comorbidity Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Comorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Comorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate Comorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (18.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Comorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (19.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere Comorbidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92 (37.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbid Conditions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive Heart Failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral Vascular Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes Melitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (23.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Kidney Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Obstructive Pulmonary Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (7.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eComorbidities were prevalent in our study population, as assessed by the Charlson Comorbidity Index. A significant proportion of patients (37.1%, n\u0026thinsp;=\u0026thinsp;92) had severe comorbidity, while only 12.1% (n\u0026thinsp;=\u0026thinsp;30) had no comorbidity. The remaining patients were distributed across low (13.3%, n\u0026thinsp;=\u0026thinsp;33), moderate (18.1%, n\u0026thinsp;=\u0026thinsp;45), and high (19.4%, n\u0026thinsp;=\u0026thinsp;48) comorbidity categories. The most common comorbidity was diabetes mellitus, present in 58 participants (23.4%). Chronic obstructive pulmonary disease (COPD) was observed in 18 participants (7.3%), while liver disease was noted in 20 participants (8.1%).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the characteristics of myometrial invasion in our cohort. The mean depth of myometrial invasion was 0.68 cm (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62), with a median of 0.5 cm. When expressed as a percentage, the mean myometrial invasion was 37% (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;30%), with a median of 31%. The mean distance from the serosal surface was 1.21 cm (SD\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78), with a median of 1.1 cm (range: 0\u0026ndash;6.5 cm). These data demonstrate considerable variability in the extent of myometrial invasion among patients with early-stage endometrial cancer.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eMyometrial invasion characteristics\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;248)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepth of Myometrial Invasion (Cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68 (\u0026plusmn;\u0026thinsp;0.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 [0, 3.9]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDepth of Myometrial Invasion (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37 (\u0026plusmn;\u0026thinsp;0.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.31 [0, 1]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistance from Serosal Surface (Cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21 (\u0026plusmn;\u0026thinsp;0.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 [0, 6.5]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLymphovascular Invasion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (16.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo evaluate and compare the predictive ability of the myometrial invasion depth, percentage, and distance from the serosal surface, we performed ROC curve analyses for all three estimators (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The area under the curve (AUC) for the depth of myometrial invasion (in cm) was 0.575 (95% CI: 0.497\u0026ndash;0.653; p\u0026thinsp;=\u0026thinsp;0.05), indicating a modest discriminatory ability (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The AUC for the percentage of myometrial invasion was 0.546 (p\u0026thinsp;=\u0026thinsp;0.244), with optimal cutoff of 33%, yielding the highest correlation with outcome (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). As for the distance from the serosal surface, AUC was 0.492 (p\u0026thinsp;=\u0026thinsp;0.839) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor mortality, the log-rank test comparing survival distributions at the 50% myometrial invasion threshold was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.182). However, when using the 1 cm invasion cutoff, the log-rank test showed a significant difference in survival distributions (p\u0026thinsp;=\u0026thinsp;0.009), suggesting that the 1 cm threshold may be a more relevant predictor of survival (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Similar comparisons were made for disease relapse. Both the log-rank test for the 50% myometrial invasion threshold and for the 1 cm invasion failed to show significant differences in relapse-free survival (p\u0026thinsp;=\u0026thinsp;0.583 and p\u0026thinsp;=\u0026thinsp;0.349, respectively, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe proceeded with Cox proportional hazards regression analysis to further assess the impact of myometrial invasion depth (1 cm) on overall survival, controlling for other covariates. In the final model, myometrial invasion at 1 cm was not independently associated with survival or relapse. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents full details of the Cox regression analysis, including hazard ratios and confidence intervals.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eCox Proportional Hazards Regression Analysis for Overall Survival\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.019*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvasion Distance (1 Cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e. \u003cem\u003e* Indicates statistical significance at p\u0026thinsp;\u0026lt;\u0026thinsp;.05.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEndometrial cancer (EC) is one of the most common gynecological malignancies, and its incidence continues to rise globally. While the prognosis for early-stage EC is generally favorable, identifying reliable prognostic markers is crucial for tailoring treatment strategies and improving patient outcomes. Myometrial invasion (MI) is a critical prognostic factor influencing recurrence risk and overall survival.\u003c/p\u003e \u003cp\u003eIn the current study, we evaluated the predictive value of the 50% cutoff for MI, a widely accepted threshold in the literature related to patient outcome, recurrence, and overall survival in early-stage EC. The FIGO staging system historically classified deep myometrial invasion using a one-third (33%) threshold before transitioning to a one-half (50%) cutoff in the 2009 update. Our results show that while 50% or more MI may relate to patient outcomes, it does not independently predict recurrence or overall survival. Interestingly, according to the results of our study, a cutoff of 33% MI provided stronger prognostication. Although the 50% cutoff has proven to be a reliable prognostic factor, our study suggests that the original 33% cutoff is a more accurate predictor of outcomes. This challenges the 2009 shift in the staging system and emphasizes the importance of further investigation.\u003c/p\u003e \u003cp\u003eIn addition, we looked for an alternative for the current measurement method by evaluating several other MI parameters, such as absolute invasion depth and distance from uterine serosa. In this analysis, using absolute DOI with a cutoff of one centimeter appeared to correlate better with overall survival. DOI was our study's most statistically significant parameter with the best correlation to outcome.\u003c/p\u003e \u003cp\u003eOur findings agree with a study by Doghri et al. [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] which showed that DOI was superior to TFD as well as MI percentage as a prognostic factor. In their study the best DOI cutoff was found to be of 3 mm. Regarding distance from the serosa, our results are consistent with previously published results from a study by Oge et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] that assessed 133 patients with early stage IB endometrioid endometrial cancer. The authors found that TFD did not significantly predict recurrence or survival outcomes and is not an independent prognostic factor in early EC patients. Contradicting results were presented by Chattopadhyay [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] demonstrating TFD is an independent predictor of survival and recurrence.\u003c/p\u003e \u003cp\u003eOur findings regarding alternative MI measurements are particularly relevant considering the 2023 FIGO staging revision, which takes under consideration the complexity of tumor assessment by incorporating multiple prognostic factors beyond the traditional 50% MI cutoff. Our finding that a 33% MI cutoff might be more prognostically significant than the traditional 50% is particularly interesting in the context of the evolving FIGO criteria.\u003c/p\u003e \u003cp\u003eThe 2023 FIGO revision acknowledges that historical staging parameters, including the 50% MI cutoff, may need refinement as our understanding of endometrial cancer biology improves. This aligns with our observation that alternative measurements, such as absolute depth of invasion, might better predict outcome in early-stage disease. Furthermore, the new FIGO system's emphasis on integrating multiple prognostic factors supports our approach of examining various invasion parameters rather than relying solely on percentage-based measurements.\u003c/p\u003e\n\u003ch3\u003eStudy strengths and limitations\u003c/h3\u003e\n\u003cp\u003e Our study benefited from relatively long follow-up as well as the review and merging of pathological and clinical data. This allowed us to capture important clinical outcomes and provide a detailed evaluation of prognostic factors in early-stage endometrial cancer (EC). It ensures that the correlations between myometrial invasion (MI) and patient outcomes are strong over time. Our sample size allowed for a thorough, detailed clinical and pathological analysis. The revision of pathological slides under question ensured the consistency and accuracy of our data.\u003c/p\u003e \u003cp\u003eHowever, the present study has some limitations as well. This is a single-center study, and the experience of other medical centers may be different. The Israeli population might have different features than the international patient population. Therefore, we acknowledge that more extensive multicenter studies would be beneficial in order to confirm our findings and detect additional prognostic factors. EC (especially early stage) is often an indolent disease, requiring large cohorts and extended follow-up period to fully capture the disease course and create more accurate prognostic models for early-stage endometrial cancer. The study period is from 2006 to 2020, a period in which changes in therapy and prognosis might have had an important implication on prognosis.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur findings suggest that alternative cutoffs, particularly absolute myometrial invasion (MI) of 1 cm and optionally 33% MI cutoff, may offer better prognostic value for overall survival in early-stage endometrial endometrioid carcinoma compared to the traditionally used 50% cutoff. Furthermore, investigating various types of MI measures may contribute to ongoing efforts to refine prognostic markers in early-stage EC, potentially improving treatment strategies for future patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003e This study was performed in line with the principles of the Declaration of Helsinki and approved by the Institutional Review Board of Soroka University Medical Center (Approval No. 0192-23-SOR).\u003c/p\u003e \u003cp\u003eFunding and Competing Interests\u003c/p\u003e \u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript, The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.P- data collection, manuscript writingR.K- project development, manuscript writing and editingB.S- data collection, manuscript editingJ.D- data analysis, Manuscript editingM.M- Project developmentS.D- data collection, project developmentR.S.L- project development, manuscript editing\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBraun MM, Overbeek-Wager E, Grumbo RJ. 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Available from: https://journals.lww.com/intjgynpathology/abstract/2006/01000/assessment_of_uterine_wall_thickness_and_position.8.aspx\u003c/li\u003e\n\u003cli\u003eChattopadhyay S, Galaal KA, Patel A, Fisher A, Nayar A, Cross P, et al. Tumour-free distance from serosa is a better prognostic indicator than depth of invasion and percentage myometrial invasion in endometrioid endometrial cancer. BJOG. 2012 Sep;119(10):1162-70. Available from: https://obgyn.onlinelibrary.wiley.com/doi/10.1111/j.1471-0528.2012.03427.x\u003c/li\u003e\n\u003cli\u003eOge T, Comert DK, Cakmak Y, Arik D. Is Tumor-Free Distance an Independent Prognostic Factor for Early-Stage Endometrioid Endometrial Cancer? J Oncol. 2020 Apr;14;2020:2934291. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC7178498/\u003c/li\u003e\n\u003cli\u003ePergialiotis V, Zachariou E, Efthymios Vlachos D, Vlachos A, Goula K, Thomakos N, et al. 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Available from: https://www.sciencedirect.com/science/article/abs/pii/0021968187901718?via%3Dihub\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"archives-of-gynecology-and-obstetrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arch","sideBox":"Learn more about [Archives of Gynecology and Obstetrics](https://www.springer.com/journal/404)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/arch/default.aspx","title":"Archives of Gynecology and Obstetrics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Endometrial carcinoma (EC), myometrial invasion (MI), tumor-free distance (TFD), International Federation of Gynecology and Obstetrics (FIGO)","lastPublishedDoi":"10.21203/rs.3.rs-6228646/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6228646/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThe depth of myometrial invasion (MI) is known to have a prognostic value in endometrial carcinoma (EC), and the FIGO 50% cutoff is widely accepted; however, recent studies have suggested other measurements such as the absolute depth of invasion and tumor-free distance (TFD) from the serosal surface to also be predictive. The aim of this study was to assess the association between the FIGO cutoff and other measures with overall survival and disease-free survival of patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis is a retrospective analysis of a cohort of 248 women diagnosed with stage I endometrioid endometrial carcinoma, treated at Soroka University Medical Center between 2006 and 2020. Clinical and pathological data were collected and analyzed. ROC analysis was used to define the best cutoffs in all three categories (MI, absolute depth and TDF). Survival analyses were then conducted using Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAbsolute myometrial invasion (MI) to the depth of 1 cm significantly predicts overall survival (log-rank, p\u0026thinsp;=\u0026thinsp;0.009). Additionally, a 33% MI cutoff demonstrated potential for better outcome prediction as compared to the commonly used 50% MI threshold, though it did not reach statistical significance. Tumor-free distance (TFD) from the serosal surface was not significantly associated with recurrence.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMI depth of more than 1 cm is a valid indicator of patient outcome. Additionally, a cutoff of 33% MI probably has a better prognostic value than the current cutoff 50%.\u003c/p\u003e","manuscriptTitle":"The prognostic importance of features of myometrial invasion in endometrial endometrioid carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 11:13:57","doi":"10.21203/rs.3.rs-6228646/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-07T05:28:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-21T18:22:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-10T11:54:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22254057179059970143034805273107705691","date":"2025-03-31T22:40:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17581448872098436120256261632914852406","date":"2025-03-31T10:00:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14450170583446231197103680593073708034","date":"2025-03-30T17:43:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123747486078292214013322184832633688688","date":"2025-03-30T17:16:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-30T15:33:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-15T09:57:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-15T06:08:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Archives of Gynecology and Obstetrics","date":"2025-03-14T18:35:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"archives-of-gynecology-and-obstetrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arch","sideBox":"Learn more about [Archives of Gynecology and Obstetrics](https://www.springer.com/journal/404)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/arch/default.aspx","title":"Archives of Gynecology and Obstetrics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6554c7dd-07ce-492a-86a3-7adf60d1f950","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-21T15:59:28+00:00","versionOfRecord":{"articleIdentity":"rs-6228646","link":"https://doi.org/10.1007/s00404-025-08103-6","journal":{"identity":"archives-of-gynecology-and-obstetrics","isVorOnly":false,"title":"Archives of Gynecology and Obstetrics"},"publishedOn":"2025-07-16 15:56:57","publishedOnDateReadable":"July 16th, 2025"},"versionCreatedAt":"2025-04-21 11:13:57","video":"","vorDoi":"10.1007/s00404-025-08103-6","vorDoiUrl":"https://doi.org/10.1007/s00404-025-08103-6","workflowStages":[]},"version":"v1","identity":"rs-6228646","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6228646","identity":"rs-6228646","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}