We present findings of a cancer multidisciplinary-team (MDT) coordinated mainstreaming pathway of unselected 5-panel germline BRCA1/BRCA2/RAD51C/RAD51D/BRIP1 and parallel somatic BRCA1/BRCA2 testing in all women with epithelial-OC and highlight the discordance between germline and somatic testing strategies across two cancer centres. Patients were counselled and consented by a cancer MDT member. The uptake of parallel multi-gene germline and somatic testing was 97.7%. Counselling by clinical-nurse-specialist more frequently needed >1 consultation (53.6% (30/56)) compared to a medical (15.0% (21/137)) or surgical oncologist (15.3% (17/110)) (p < 0.001). The median age was 54 (IQR = 51–62) years in germline pathogenic-variant (PV) versus 61 (IQR = 51–71) in BRCA wild-type (p = 0.001). There was no significant difference in distribution of PVs by ethnicity, stage, surgery timing or resection status. A total of 15.5% germline and 7.8% somatic BRCA1/BRCA2 PVs were identified. A total of 2.3% patients had RAD51C/RAD51D/BRIP1 PVs. A total of 11% germline PVs were large-genomic-rearrangements and missed by somatic testing. A total of 20% germline PVs are missed by somatic first BRCA-testing approach and 55.6% germline PVs missed by family history ascertainment. The somatic testing failure rate is higher (23%) for patients undergoing diagnostic biopsies. Our findings favour a prospective parallel somatic and germline panel testing approach as a clinically efficient strategy to maximise variant identification. UK Genomics test-directory criteria should be expanded to include a panel of OC genes.

Ovarian cancer survival in the UK lags behind comparable countries. Results from the ongoing National Ovarian Cancer Audit feasibility pilot (OCAFP) show that approximately 1 in 4 women with advanced ovarian cancer (Stage 2, 3, 4 and unstaged cancer) do not receive any anticancer treatment and only 51% in England receive international standard of care treatment, i.e., the combination of surgery and chemotherapy. The audit has also demonstrated wide variation in the percentage of women receiving anticancer treatment for advanced ovarian cancer, be it surgery or chemotherapy across the 19 geographical regions for organisation of cancer delivery (Cancer Alliances). Receipt of treatment also correlates with survival: 5 year Cancer survival varies from 28.6% to 49.6% across England. Here, we take a systems wide approach encompassing both diagnostic pathways and cancer treatment, derived from the whole cohort of women with ovarian cancer to set out recommendations and quality performance indicators (QPI). A multidisciplinary panel established by the British Gynaecological Cancer Society carefully identified QPI against criteria: metrics selected were those easily evaluable nationally using routinely available data and where there was a clear evidence base to support interventions. These QPI will be valuable to other taxpayer funded systems with national data collection mechanisms and are to our knowledge the only population level data derived standards in ovarian cancer. We also identify interventions for Best practice and Research recommendations.

For women achieving clinical remission after the completion of initial treatment for epithelial ovarian cancer, 80% with advanced-stage disease will develop recurrence. However, the standard treatment of women with recurrent platinum-sensitive diseases remains poorly defined. Secondary (SCS), tertiary (TCS) or quaternary (QCS) cytoreduction surgery for recurrence has been suggested to be associated with increased overall survival (OS). We searched five databases for studies reporting death rate, OS, cytoreduction rates, post-operative morbidity/mortality and diagnostic models predicting complete cytoreduction in a platinum-sensitive disease recurrence setting. Death rates calculated from raw data were pooled based on a random-effects model. Meta-regression/linear regression was performed to explore the role of complete or optimal cytoreduction as a moderator. Pooled death rates were 45%, 51%, 66% for SCS, TCS and QCS, respectively. Median OS for optimal cytoreduction ranged from 16–91, 24–99 and 39–135 months for SCS, TCS and QCS, respectively. Every 10% increase in complete cytoreduction rates at SCS corresponds to a 7% increase in median OS. Complete cytoreduction rates ranged from 9–100%, 35–90% and 33–100% for SCS, TCS and QCS, respectively. Major post-operative thirty-day morbidity was reported to range from 0–47%, 13–33% and 15–29% for SCS, TCS and QCS, respectively. Thirty-day post-operative mortality was 0–6%, 0–3% and 0–2% for SCS, TCS and QCS, respectively. There were two externally validated diagnostic models predicting complete cytoreduction at SCS, but none for TCS and QCS. In conclusion, our data confirm that maximal effort higher order cytoreductive surgery resulting in complete cytoreduction can improve survival.

Borderline Ovarian Tumours (BOTs) comprise 15-20 % of all epithelial ovarian tumours. BOTs are characterised by increased mitotic activity but lack infiltrative destructive growth and stromal invasion. Several dilemmas and controversies have been discussed in the literature regarding BOT management including surgical approach (radical versus fertility-sparing surgery), surgical route (open versus laparoscopic surgery) and impact on disease recurrence, follow-up protocols/modalities (imaging, serum biomarkers), role of completion surgery, lymph node dissection during staging, adjuvant chemotherapy, as well as use of Hormone-Replacement Therapy (HRT) post-surgery. We performed a structured narrative review on Medline and Cochrane Library Databases to identify studies pertaining to the management of BOTs. Identifying areas of agreement and outstanding uncertainty are integral to optimise robust treatment regimens for BOT management and improve the Quality of Life (QoL) and clinical outcomes for patients. We discuss a framework of recommendations to counsel, manage and follow-up women diagnosed with BOT.

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.

Background/Objectives: Universal surgical risk calculators are not validated for use and poorly predict postoperative morbidity and mortality for women undergoing gynaecological oncology surgery. This adversely affects communication of risk resulting in poorly informed decisions and missed opportunities for medical optimization to mitigate risk preoperatively. We present the development and external validation of our novel GO SOAR surgical risk calculator for use to preoperatively predict postoperative thirty-day surgical morbidity and mortality in relation to gynaecological oncology surgeries. Methods: New logistic regression models were developed using the GO SOAR1 training cohort (n = 1811) and externally validated in an independent prospective cohort (n = 416) for two outcomes: thirty-day postoperative mortality (alive versus dead) and thirty-day postoperative morbidity (any complication (Clavien–Dindo I–V) versus none). Performance of the GO SOAR models was compared against established all-purpose surgical risk calculators (SORT/POSSUM/P-POSSUM/NSQIP). Model discrimination was assessed with sensitivity calculated at a clinically significant prespecified specificity threshold of 90%. Results: For mortality, AUROC was 0.752 (95% CI 0.570–0.935) for GO SOAR full and 0.795 (95% CI 0.660–0.930) for GO SOAR condensed; corresponding sensitivities at 90% specificity were 57.1% and 42.9%. For morbidity, AUROC was 0.698 (95% CI 0.641–0.755) and 0.703 (95% CI 0.646–0.760) for the full and condensed models, respectively, with sensitivities of 30.4% and 32.1% at 90% specificity. Conclusions: The GO SOAR model using a data-driven, gynaecological-oncology-specific approach at 90% specificity, achieved the highest observed sensitivity among the evaluated calculators. Accurate surgical risk predictions are crucial for major oncological surgery, where complications can diminish quality of life and affect long-term cancer survival. A model such as the GO SOAR surgical risk calculator that uses readily available preoperative data, regardless of income setting, is essential in reducing global disparities in surgical outcomes.