Survival Dynamics in Advanced Ovarian Cancer: R2 Resection Versus No-Surgery Paths Explored.

BACKGROUND: Cytoreductive surgery is critical for optimal tumor clearance in advanced epithelial ovarian cancer (EOC). Despite best efforts, some patients may experience R2 (>1 cm) resection, while others may not undergo surgery at all. We aimed to compare outcomes between advanced EOC patients undergoing R2 resection and those who had no surgery. METHODS: Retrospective data from 51 patients with R2 resection were compared to 122 patients with no surgery between January 2015 and December 2019 at a UK tertiary referral centre. Progression-free survival (PFS) and overall survival (OS) were the study endpoints. Principal Component Analysis and Term Frequency - Inverse Document Frequency scores were utilized for data discrimination and prediction of R>2 cm from computed tomography pre-operative reports, respectively. RESULTS: No statistical significance was observed, except for age (73 vs 67 years in the no- surgery vs R2 group, P: .001). Principal Components explained 34% of data variances. Reasons for no surgery included age, co-morbidities, patient preference, refractory disease, patient deterioration or disease progression, and absence of measurable intra- abdominal disease). The median PFS and OS were 12 and 14 months for no-surgery, vs 14 and 26 months for R2 (P: .138 and P: .001, respectively). Serous histology and performance status independently predicted PFS in both no-surgery and R2 cohorts. In the no-surgery cohort, serous histology independently predicted OS, while in the R2 cohorts, both serous histology and adjuvant chemotherapy were independent prognostic features for OS. The bi-grams "abdominopelvic ascites" and "solid omental" were amongst those best discriminating between R>2 cm and R1-2 cm. CONCLUSIONS: R2 resection and no-surgery cohorts displayed unfavourable prognosis with a notable degree of uniformity. When cytoreduction results in suboptimal results, the survival benefit may still be higher compared to those who underwent no surgery.

The study examined outcomes in advanced epithelial ovarian cancer (EOC) patients who underwent either R2 (suboptimal) surgical resection or received no surgery at all at a UK tertiary referral center. Sophisticated machine learning methodolgies were used to analyze data patterns and predict the extent of resection (>2 cm) from pre-operative CT reports. Reasons for not undergoing surgery included older age, presence of other medical conditions, patient preference, progressive disease, patient decline, or lack of detectable intra-abdominal disease. Factors like serous histology and performance status iinfluenced the risk of recurrence in both groups, while serous histology and adjuvant chemotherapy predicted the risk of death in the R2 group. Word sequences like "omental disease" and "reduced bulk" helped differentiate between R>2 cm and less extensive resections (R1-2 cm). In summary, both R2 resection and no-surgery groups had poor outcomes, but patients who underwent R2 resection generally had better survival compared to those who received no surgery, even when complete tumor removal was not achieved.

Continuity Unveiled: Evaluating Cytoreduction Outcomes for Advanced Ovarian Cancer Amidst the COVID-19 Era at an ESGO Designated Centre of Excellence.

BACKGROUND/AIM: The COVID-19 pandemic brought unprecedented global changes, necessitating adjustments to address public health challenges. The impact on advanced epithelial ovarian cancer (EOC) surgery, marked by increased perioperative risks, and changes in management plans was explored in this study based on promptly published British Gynaecologic Cancer Society (BGCS) and European Society of Gynaecologic Oncology (ESGO) guidelines. PATIENTS AND METHODS: Retrospective data from 332 patients with advanced EOC who underwent cytoreductive surgery at a UK tertiary center were analyzed, and the outcomes were compared between pre-COVID-19 (2018-2019) (n=189) and COVID-19 era (2020-2021) (n=143) cohorts, covering the same timeframe (March to December). Primary outcomes included residual disease (RD) and progression-free survival (PFS), while secondary outcomes were the ESGO quality indicators (QIs) for advanced EOC surgery. Kaplan-Meier curves were produced to illustrate PFS. RESULTS: Complete cytoreduction rates remained comparable at 74.07% and 72.03% for pre-COVID-19 and COVID-19 groups, respectively. Differences were observed in ECOG performance status (p=0.015), Intensive Care Unit (ICU) admissions (p=0.039) with less interval debulking surgeries (p=0.03), lower surgical complexity scores (p=0.02), and longer operative times in the COVID-19 group (p=0.01) compared to the pre-COVID-19 group. The median PFS rates were 37 months and 34 months in the pre-COVID-19 and COVID-19 groups, respectively (p=0.08). The surgical QIs 1-3 remained uncompromised during the COVID-19 era. CONCLUSION: Management modifications prompted by the COVID-19 pandemic did not adversely impact cytoreduction rates or PFS.

Predicting complete cytoreduction for advanced ovarian cancer patients using nearest-neighbor models.

BACKGROUND: The foundation of modern ovarian cancer care is cytoreductive surgery to remove all macroscopic disease (R0). Identification of R0 resection patients may help individualise treatment. Machine learning and AI have been shown to be effective systems for classification and prediction. For a disease as heterogenous as ovarian cancer, they could potentially outperform conventional predictive algorithms for routine clinical use. We investigated the performance of an AI system, the k-nearest neighbor (k-NN) classifier, to predict R0, comparing it with logistic regression. Patients diagnosed with advanced stage, high grade serous ovarian, tubal and primary peritoneal cancer, undergoing surgical cytoreduction from 2015 to 2019, was selected from the ovarian database. Performance variables included age, BMI, Charlson Comorbidity Index, timing of surgery, surgical complexity and disease score. The k-NN algorithm classified R0 vs non-R0 patients using 3-20 nearest neighbors. Prediction accuracy was estimated as percentage of observations in the training set correctly classified. RESULTS: 154 patients were identified, with mean age of 64.4 + 10.5 yrs., BMI of 27.2 + 5.8 and mean SCS of 3 + 1 (1-8). Complete and optimal cytoreduction was achieved in 62 and 88% patients. The mean predictive accuracy was 66%. R0 resection prediction of true negatives was as high as 90% using k = 20 neighbors. CONCLUSIONS: The k-NN algorithm is a promising and versatile tool for R0 resection prediction. It slightly outperforms logistic regression and is expected to improve accuracy with data expansion.