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For various reasons, journals may convert from subscription-based to open-access (OA) publishing models, commonly referred to as flipping. In 2022, the Acta Obstetricia et Gynecologica Scandinavica flipped to OA. We performed a bibliometric analysis of authorship patterns in this journal during and after the flipping period. A total of 898 research articles were included. In the period after flipping to OA, there were more publications by authors in various countries, including from China (7.2% vs. 3.3%, P = 0.001). Accordingly, the flip to OA in a leading obstetrics and gynaecology journal seemed to impact the authorship locale., (Copyright © 2023 The Society of Obstetricians and Gynaecologists of Canada/La Société des obstétriciens et gynécologues du Canada. Published by Elsevier Inc. All rights reserved.)

Objective: To identify predictive clinico-pathologic factors for concurrent endometrial carcinoma (EC) among patients with endometrial intraepithelial neoplasia (EIN) using machine learning., Methods: a retrospective analysis of 160 patients with a biopsy proven EIN. We analyzed the performance of multiple machine learning models (n = 48) with different parameters to predict the diagnosis of postoperative EC. The prediction variables included: parity, gestations, sampling method, endometrial thickness, age, body mass index, diabetes, hypertension, serum CA-125, preoperative histology and preoperative hormonal therapy. Python 'sklearn' library was used to train and test the models. The model performance was evaluated by sensitivity, specificity, PPV, NPV and AUC. Five iterations of internal cross-validation were performed, and the mean values were used to compare between the models., Results: Of the 160 women with a preoperative diagnosis of EIN, 37.5% (60) had a post-op diagnosis of EC. In univariable analysis, there were no significant predictors of EIN. For the five best machine learning models, all the models had a high specificity (71%-88%) and a low sensitivity (23%-51%). Logistic regression model had the highest specificity 88%, XG Boost had the highest sensitivity 51%, and the highest positive predictive value 62% and negative predictive value 73%. The highest area under the curve was achieved by the random forest model 0.646., Conclusions: Even using the most elaborate AI algorithms, it is not possible currently to predict concurrent EC in women with a preoperative diagnosis of EIN. As women with EIN have a high risk of concurrent EC, there may be a value of surgical staging including sentinel lymph node evaluation, to more precisely direct adjuvant treatment in the event EC is identified on final pathology., (© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

Objective: To compare the rates of post-operative radiotherapy between two methods of lymph nodes assessment during surgical staging for endometrial cancer (EC)., Methods: We conducted a comparative study of all consecutive women with endometrial cancer who underwent sentinel lymph node detection and biopsy using blue dye and isotope scan (SLNB) at Kaplan Medical Center and patients from the IGOG database, who underwent staging lymphadenectomy (PLND). The primary outcome was the rate of adjuvant and therapeutic radiation. The secondary outcome was a comparison of disease-free survival (DFS) and overall survival (OS)., Results: There were 138 patients in the SLNB group and 1022 women in the PLND group. The detection rate of SLN was 74% for unilateral detection and 54% for bilateral detection. In the PLND group 57% were high risk patients vs. 47% in SLNB group (p = 0.03). 43% of high-risk patients in the PLND group received adjuvant or therapeutic pelvic radiation vs. 28% of high-risk women in the SLNB arm (p = 0.017). No statistically significant difference in recurrence rates nor in death rates had been observed in the high-risk group patients. The 5-years survival in the high-risk PLND group was 80% and the recurrence rate was 19% vs. 75% 5-year survival and 14% recurrence in high-risk SLNB cohort, log-rank p = 0.82 for survival and long-rank p = 0.25 for recurrence., Conclusion: Endometrial cancer patients undergoing lymph node assessment by sentinel lymph node biopsy, receive less pelvic radiotherapy., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors. Published by Elsevier Inc.)

Objective: Sentinel node mapping is widely used in the treatment of gynecologic cancers. The current study aimed to identify predictors of uncommon sentinel lymph node (SLN) locations., Methods: The current study included women who were operated for endometrial or cervical cancer with attempted sentinel lymph node mapping during surgical staging. Data were collected from electronic charts. The pelvis and the external ilia and obturator basins were common node locations. Para-aortic, pre-sacral, common iliac, internal iliac, and parametrial nodes were considered uncommon locations. We conducted analyses stratified according to common, uncommon, and very uncommon ( para -aortic, pre-sacral, parametrial) node location sites., Results: A total of 304 women were enrolled in the current study; 15.8% had SLN in uncommon locations and 4.3% had very uncommon node locations. Body mass index (BMI) was a negative predictor for uncommon SLN locations (OR 0.88, p = 0.03). The use of either indocyanine green (ICG) or Tc 99 & blue dye was an independent predictor for uncommon SLN locations (OR 8.24, p = 0.006). More recent surgeries and the presence of positive nodes were independent predictors for very uncommon node locations (OR 2.13, p = 0.011, and OR 9.3, p = 0.002, respectively)., Conclusions: BMI, tracer type, surgical year, and positive nodes were independent predictors for uncommon SLN locations. These findings suggest that surgical effort, technique and experience may result in better identification of uncommon SLN locations., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors. Published by Elsevier Inc.)