Recently, the new 2023 International Federation of Gynecology and Obstetrics (FIGO) staging system for endometrial cancer (EC) critically integrating new pathological and molecular features was published. The present study evaluated the clinical impact of the new 2023 FIGO staging system by comparing it to the previous 2009 system. This is an international, pooled retrospective study of 519 EC patients who underwent primary treatment (and molecular characterisation) at three European Society of Gynaecological Oncology (ESGO) accredited centres in Austria/Italy. Patients were categorised according to the 2009 and the 2023 FIGO staging systems. Stage shifts were analysed and (sub)stage specific 5-year progression-free (PFS) and overall survival (OS) rates were calculated and compared. Different statistical tests were applied to evaluate the prognostic precision of the two FIGO staging systems and to compare them to each other. (Sub)stage shifts occurred in 143/519 (27.6%) patients: 123 upshifts (23.7%) and 20 (3.9%) downshifts. 2023 FIGO staging system identified a stage I cohort with a notably higher 5-year PFS rate compared to 2009 (93.0% versus 87.4%, respectively). For stage II disease, the 5-year PFS rate was similar in the 2023 and the 2009 FIGO staging systems (70.2% versus 71.2%, respectively). The two new molecularly defined 2023 FIGO substages IAm_POLEmut and IICm_p53abn displayed distinct, particularly favourable and adverse oncologic outcomes within early stage disease, respectively. A remarkably lower 5-year PFS rate for stage III patients was revealed in the 2023 FIGO staging system compared to 2009 (44.4% versus 54.1%, respectively). All applied statistical tests confirmed a more accurate prediction of PFS and OS by the 2023 FIGO staging system compared to 2009. The new 2023 FIGO stating system led to a substantial stage shift in about one quarter of patients leading to a higher prognostic precision. In early stage disease, the new substages added further prognostic granularity and identified treatment relevant subgroups.

Objective Ultrastaging is accurate in detecting nodal metastases, but increases costs and may not be necessary in certain low-risk subgroups. In this study we examined the risk of nodal involvement detected by sentinel lymph node (SLN) biopsy in a large population of apparent early-stage endometrial cancer and stratified by histopathologic characteristics. Furthermore, we aimed to identify a subgroup in which ultrastaging may be omitted. Methods We retrospectively included patients who underwent SLN (with bilateral mapping and no empty nodal packets on final pathology) ± systematic lymphadenectomy for apparent early-stage endometrial cancer at two referral cancer centers. Lymph node status was determined by SLN only, regardless of non-SLN findings. The incidence of macrometastasis, micrometastasis, and isolated tumor cells (ITC) was measured in the overall population and after stratification by histotype (endometrioid vs serous), myometrial invasion (none, <50%, ≥50%), and grade (G1, G2, G3). Results Bilateral SLN mapping was accomplished in 1570 patients: 1359 endometrioid and 211 non-endometrioid, of which 117 were serous. The incidence of macrometastasis, micrometastasis, and ITC was 3.8%, 3.4%, and 4.8%, respectively. In patients with endometrioid histology (n=1359) there were 2.9% macrometastases, 3.2% micrometastases, and 5.3% ITC. No macro/micrometastases and only one ITC were found in a subset of 274 patients with low-grade (G1-G2) endometrioid endometrial cancer without myometrial invasion (all <1%). The incidence of micro/macrometastasis was higher, 2.8%, in 708 patients with low-grade endometrioid endometrial cancer invading <50% of the myometrium. In patients with serous histology (n=117), the incidence of macrometastases, micrometastasis, and ITC was 11.1%, 6.0%, and 1.7%, respectively. For serous carcinoma without myometrial invasion (n=36), two patients had micrometastases for an incidence of 5.6%. Conclusions Ultrastaging may be safely omitted in patients with low-grade endometrioid endometrial cancer without myometrial invasion. No other subgroups with a risk of nodal metastasis of less than 1% have been identified.

Perrone E, et al. J Gynecol Oncol. 2021 Mar;32(3):e45. https://doi.org/10.3802/jgo.2021.32.e45

This review provides a comprehensive update on recent evidence regarding gynecologic tumors associated with Lynch Syndrome (LS). Endometrial cancer (EC) and ovarian cancer (OC) are the first and second most common gynecologic malignancies in developed countries, respectively, and LS is estimated to be the hereditary cause in 3% of both EC and OC. Despite the increasing evidence on LS-related tumors, few studies have analyzed the outcomes of LS-related EC and OC stratified by mutational variant. This review aims to provide a comprehensive overview of the literature and comparison between updated international guidelines, to help outline a shared pathway for the diagnosis, prevention, and management of LS. Through the widespread adoption of the immunohistochemistry-based Universal Screening, LS diagnosis and identification of mutational variants could be standardized and recognized by international guidelines as a feasible, reproducible, and cost-effective method. Furthermore, the development of a better understanding of LS and its mutational variants will support our ability to better tailor EC and OC management in terms of prophylactic surgery and systemic treatment in the light of the promising results shown by immunotherapy.

Background Nearly 65% of patients with endometrial cancer who undergo primary hysterectomy have concurrent obesity. Retrospective data show advantages in using robotic surgery in these patients compared with conventional laparoscopy, namely lower conversion rate, increased rate of same-day discharge, and reduced blood loss. Nevertheless, to date no prospective randomized controlled trials have compared laparoscopic surgery versus robotic-assisted surgery in morbidly obese patients. Primary Objective The robotic-assisted versus conventional laparoscopic surgery in the management of obese patients with early endometrial cancer in the sentinel lymph node era: a randomized controlled study (RObese) trial aims to find the most appropriate minimally invasive surgical approach in morbidly obese patients with endometrial carcinoma. Study Hypothesis Robotic surgery will reduce conversions to laparotomy in endometrial cancer patients with obesity compared with those who undergo surgery with conventional laparoscopy. Trial Design This phase III multi-institutional study will randomize consecutive obese women with apparent early-stage endometrial cancer to either laparoscopic or robot-assisted surgery. Major Inclusion/Exclusion riteria The RObese trial will include obese (BMI≥30 kg/m²) patients aged over 18 years with apparent 2009 Federation of Gynecology and Obstetrics (FIGO) stage IA-IB endometriod endometrial cancer. Primary Endpoint Conversion rate to laparotomy between laparoscopic surgery versus robot-assisted surgery. Sample Size RObese is a superiority trial. The clinical superiority margin for this study is defined as a difference in conversion rate of −6%. Assuming a significance level of 0.05 and a power of 80%, the study plans to randomize 566 patients. Estimated Dates for Completing Accrual and Presenting Results Patient recruitment will be completed by 2026, and follow-up will be completed by 2029 with presentation of data shortly thereafter. Two interim analyses are planned: one after the first 188 and the second after 376 randomized patients. Trial Registration NCT05974995

Objectives Transvaginal ultrasound is typically the initial diagnostic approach in patients with postmenopausal bleeding for detecting endometrial atypical hyperplasia/cancer. Although transvaginal ultrasound demonstrates notable sensitivity, its specificity remains limited. The objective of this study was to enhance the diagnostic accuracy of transvaginal ultrasound through the integration of artificial intelligence. By using transvaginal ultrasound images, we aimed to develop an artificial intelligence based automated segmentation model and an artificial intelligence based classifier model. Methods Patients with postmenopausal bleeding undergoing transvaginal ultrasound and endometrial sampling at Mayo Clinic between 2016 and 2021 were retrospectively included. Manual segmentation of images was performed by four physicians (readers). Patients were classified into cohort A (atypical hyperplasia/cancer) and cohort B (benign) based on the pathologic report of endometrial sampling. A fully automated segmentation model was developed, and the performance of the model in correctly identifying the endometrium was compared with physician made segmentation using similarity metrics. To develop the classifier model, radiomic features were calculated from the manually segmented regions-of-interest. These features were used to train a wide range of machine learning based classifiers. The top performing machine learning classifier was evaluated using a threefold approach, and diagnostic accuracy was assessed through the F1 score and area under the receiver operating characteristic curve (AUC-ROC). Results 302 patients were included. Automated segmentation–reader agreement was 0.79±0.21 using the Dice coefficient. For the classification task, 92 radiomic features related to pixel texture/shape/intensity were found to be significantly different between cohort A and B. The threefold evaluation of the top performing classifier model showed an AUC-ROC of 0.90 (range 0.88–0.92) on the validation set and 0.88 (range 0.86–0.91) on the hold-out test set. Sensitivity and specificity were 0.87 (range 0.77–0.94) and 0.86 (range 0.81–0.94), respectively. Conclusions We trained an artificial intelligence based algorithm to differentiate endometrial atypical hyperplasia/cancer from benign conditions on transvaginal ultrasound images in a population of patients with postmenopausal bleeding.