More than 50% of all gynecologic tumors can be classified as rare (defined as an incidence of ≤6 per 100,000 women) and usually have a poor prognosis owing to delayed diagnosis and treatment. In contrast to almost all other common solid tumors, the treatment of rare gynecologic tumors (RGT) is often based on expert opinion, retrospective studies, or extrapolation from other tumor sites with similar histology, leading to difficulty in developing guidelines for clinical practice. Currently, gynecologic cancer research, due to distinct scientific and technological challenges, is lagging behind. Moreover, the overall efforts for addressing these challenges are fragmented across different European countries and indeed, worldwide. The GYNOCARE, COST Action CA18117 (European Network for Gynecological Rare Cancer Research) programme aims to address these challenges through the creation of a unique network between key stakeholders covering distinct domains from concept to cure: basic research on RGT, biobanking, bridging with industry, and setting up the legal and regulatory requirements for international innovative clinical trials. On this basis, members of this COST Action, (Working Group 1, “Basic and Translational Research on Rare Gynecological Cancer”) have decided to focus their future efforts on the development of new approaches to improve the diagnosis and treatment of RGT. Here, we provide a brief overview of the current state-of-the-art and describe the goals of this COST Action and its future challenges with the aim to stimulate discussion and promote synergy across scientists engaged in the fight against this rare cancer worldwide.

Objective: The P-POSSUM scale is widely used in predicting perioperative morbidity and mortality. Evidence on the performance of P-POSSUM in predicting outcomes after cytoreductive surgery (CRS) for ovarian cancer (OC) is limited. In this study, we assess how well P-POSSUM predicts morbidity in OC CRS and explore whether incorporating additional clinical variables can enhance its predictive accuracy. We retrospectively collected data on consecutive patients undergoing OC CRS within a tertiary gynaecologic oncology network. The collected information included demographic characteristics, P-POSSUM morbidity and mortality scores, Edmonton Frail Scale (EFS) scores, preoperative serum albumin levels, and observed 30-day postoperative morbidity and mortality, classified using the Clavien–Dindo (CD) scale. The predictive performance of P-POSSUM was evaluated using receiver operating characteristic (ROC) curves to calculate sensitivity and specificity. A stepwise regression analysis was then applied to identify additional variables that could improve model performance, incorporating preoperative covariates. The final model incorporated parameters chosen through bootstrap investigation of the model variability (stepAIC). Predicted versus observed morbidity was calibrated and performance compared between P-POSSUM and the final model. Results: Of 161 sequential OC patients, 95 (59%) underwent primary, 45 (28%) interval, and 21 (13%) delayed CRS. The mean age was 66.4 (95%CI: 60–75) and duration of surgery was 223 mins (95%CI: 142–279). Sixty-five (40.3%) patients had ≥1 postoperative complication. Two deaths were reported. Among the observed complications, 4 patients (6.1%) experienced CD4, 10 patients (15.3%) CD3, 38 patients (58.5%) CD2, and 11 patients (16.9%) CD1 events. The mean P-POSSUM-predicted morbidity and mortality were 59.5% (95%CI: 56.7–62.3%) and 5.86% (95%CI: 5.02–6.70%), respectively. The area under the curve (AUC) for P-POSSUM in predicting morbidity and mortality was 0.539 (p = 0.401) and 0.569 (p = 0.137), respectively. Given the small number of deaths, no robust conclusions regarding mortality are possible. EFS and BMI emerged as significant predictors of observed morbidity using a stepwise-model selection process. The AIC of this final model was 211.44. Our final model of PPOSSUM + EFS + BMI had AUC = 0.6551 (Delong’s Z = 1.8845, p-value = 0.05949). Conclusions: The P-POSSUM scale shows poor performance for predicting morbidity in OC CRS. New validated and accurate model(s) are necessary for predicting surgical morbidity. Our proposed model incorporates additional variables to improve P-POSSUM’s performance. This requires further development and validation.