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Introduction/Background* The prognosis of advanced stage high grade serous ovarian cancer patients (HGSOC) is multifactorial, and could be accurately predicted by using Machine Learning (ML) algorithms. We designed a study to support the feature selection of selected clinical variables to define their relative survival impact on two-year prognosis prediction in HGSOC patients, who received surgical treatment. Methodology This was a retrospective analysis of 209 FIGO stage III-IV HGSOC women, who were scheduled for cytoreductive surgery in SJUH, Leeds between Jan 2015 to Dec 2018 with curative or life-prolonging intent. The two-year prognosis estimation was formulated as a binary classification problem. Dataset was split into training (80%) and test (20%) cohorts with repeated random sampling until there was no significant difference (p=0.20) between the two cohorts. A ten-fold cross-validation was applied. Various state-of-the-art supervised ML classifiers were tested, including Support-Vector-Machines (SVMs), K-Nearest Neighbors (KNNs), Ensemble Classifiers, and Naive Bayes, based on a set of performance metrics. These results were directly compared to conventional Logistic Regression (LR). For feature selection, multivariate feature ranking using the MRMR method was carried out. Result(s)* Two hundred nine patients were identified. The model’s mean prediction accuracy reached 73%. We demonstrated that SVM and Ensemble Discriminant algorithms outperformed Logistic Regression in accuracy indices. The probability of achieving a cancer-free state was maximized with a combination of primary cytoreduction, good performance status, and maximal surgical effort (AUC 0.63). Standard chemotherapy, performance status, tumor load, and residual disease were consistently predictive of the two-year overall survival (AUC 0.63-0.66) (figure 1). The model recall and precision were greater than 80%. Conclusion* Appropriate feature selection is required when building a HGSOC model for two-year prognosis prediction. For HGSOC prognosis, one should consider not only the patient’s disease burden but also their overall medical status and ability to undergo extensive surgery, resulting in survival benefits alongside with standard chemotherapy.

Introduction/Background* In advanced stage high grade serous ovarian cancer (HGSOC), the introduction of maximal surgical effort without compromising peri-operative management and subsequent recovery to achieve complete cytoreduction, requires Critical Care Unit (CCU) availability. This paradigm shift prompts the development of tools to accurately predict CCU admission following cytoreductive surgery. Modern data mining technology, such as Machine Learning (ML) could be helpful in accurately predicting CCU admissions to improve standards of care. We developed a framework to improve the accuracy of predicting CCU admission in HGSOC patients by use of ML algorithms (figure 1). Methodology A cohort of 291 advanced stage HGSOC patients, who underwent surgical cytoreduction from Jan 2014 to Dec 2019, was selected from the ovarian database. They were randomly assigned to training (60%) and test (40%) sub-cohorts. Forward selection and backward stepwise regression were employed to screen independent pre- and intra-operative variables. Linear (LDA), Quadratic (QDA), and non-linear distance (ANN and KNN) ML models were employed to derive predictive information. These methods were tested against conventional linear regression (LR). Model performance was evaluated by prediction accuracy, sensitivity, specificity, and F1 scores. Result(s)* We identified 56/291(19.2%) CCU admissions. For the outcome of CCU admission, the prediction accuracies were higher for LDA (0.90) and QDA (0.93) compared with LR (0.84) when all the variables were included in the in-built model. Feature selection identified pre-treatment albumin, surgical complexity score, estimated blood loss, operative time, and bowel resection with stoma formation as the most significant prediction features. With feature selection, the prediction accuracies were higher for LDA (0.89) and KNN (0.86) compared with LR (0.82). Admission to CCU was associated with increased length of stay (P = 0.000), and decreased number of postoperative complications (P = 0.001). Conclusion* Herein, ML algorithms accurately predicted HGSOC patients, who required CCU admission following their cytoreductive surgery. Linear discriminant analysis was consistently more predictive than LR for CCU admission, irrespective of the number of features included in the analysis. Limited, potentially modifiable, mostly intra-operative factors contributing to CCU admission were identified and suggest areas for targeted interventions.

Introduction/Background Accurate prediction of patient prognosis can be especially useful for the selection of best treatment protocols. Machine Learning (ML) can serve this purpose by making predictions based upon generalizable clinical patterns embedded within learning datasets. We hypothesised that use of ML algorithms could improve prognosis estimation in advanced high grade serous ovarian (HGSOC) patients. We aimed to compare the performance of two ML prediction methods for HGSOC prognosis, based on Area Under Curve (AU-ROC) performance for a 2-year prognosis period. Methodology This was a retrospective analysis of 209 FIGO stage III-IV HGSOC women, who were scheduled for cytoreductive surgery in SJUH, Leeds between Jan 2015 to Dec 2018 with curative or life-prolonging intent. Support-Vector-Machine (SVM) and K-Nearest Neigbors (K-NN) were employed to model prognosis. The prognosis estimation problem was formulated as a binary classification problem. For the 2-year prognosis period, two groups were defined using patient survival information; patients who did not relapse or survived beyond two years were labelled in the positive class and patients who relapsed or died before reaching that period were considered in the negative class. The study was restricted to the most common prognostic variables and focused on predictive model comparisons. Dataset was split into training and test cohorts with repeated random sampling until there was no significant difference (p=0.20) between the two cohorts with respect to all variables. Results 172 out of 209 patients with fully curated data were eligible for 2-year prognosis prediction analysis. 104/172 (60%) and 55/172 (32%) patients had disease recurrence or died of disease within two years, respectively. The variable importance for the 2-year progression free survival (PFS) and overall survival (OS) is shown in figure 1. A combination of good performance status, upfront cytoreduction and increased surgical complexity score best predicted 2-year PFS with an accuracy of 63% and 62.1% for the SVM and K-NN classifiers, respectively. SVM best predicted 2-year OS by a combination of Carboplatin/Taxol chemotherapy, low disease score, no residual disease, increased surgical complexity score, and upfront cytoreduction with an accuracy of 71.6% ( AU-ROC: 0.66) (figure 2). Conclusion ML appears to be promising for accurate estimation of HGSOC prognosis. We provide evidence as to what combination of prognosticators leads to the largest impact on the HGSOC two-year prognosis. The cohort is currently expanding to further examine the short term vs long term contribution of the clinical variables from the comparative models Disclosures No disclosures.