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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.

Abstract 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.