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Background Obesity is an important risk factor for the development of endometrial cancer and is associated with poor outcomes. However, the impact of body fat distribution on survival and surgical outcomes in endometrial cancer patients is unclear. Methods This is a retrospective study in women diagnosed with primary endometrial cancer between February 2006 and August 2017 at the Royal Cornwall Hospital who had abdominal CT-scan as part of routine diagnostic work-up. Subcutaneous abdominal fat volumes and visceral abdominal fat volumes were quantified, and visceral fat percentage calculated. Results A total of 302 patients with high grade endometrial cancer were included. The median age was 70 years and median Body Mass Index (BMI) was 29.7 kg/m2. The majority of patients (60%) had endometrioid type histology. High visceral fat percentage was associated with poor overall- and disease-free survival (p Conclusion Obesity with high visceral fat percentage is an independent negative prognostic factor in endometrial cancer and high visceral fat volumes are associated with increased postoperative complication rates. The additional association of high visceral fat with multiple comorbidities might be reflecting an unhealthy macroenvironment.

Introduction/Background In endometrioid endometrial cancer (EEC), current clinical algorithms do not accurately predict patients with lymph node metastasis, leading to both under- and over-treatment. We aimed to develop an integrative model combining protein data with routinely available clinical information and preoperative imaging to identify EEC patients who may benefit from more aggressive surgery including lymphadenectomy. Methodology Using reverse phase protein arrays, expression profiles were generated for 176 proteins in independent training (n=243, Bergen; Norway) and test sets (n=56, Bergen, Norway and n=100, MDACC, Texas). LIMMA analysis identified significantly differently expressed proteins between cases with and without lymph node metastasis. Generalized linear models were then constructed selecting only the most informative proteins in addition to clinical data. Gene expression data from the same tumours were used for confirmatory testing. Results The main model, including fibronectin, cyclin D1 and tumour grade, predicted lymph node metastasis with AUC 0.79 (training); 0.88 (Bergen test set) and 0.83 (RNA expression data). The MRI model, along MRI including fibronectin and grade, resulted in AUC 0.83 (training and Bergen test set). Finally, in grade 1 and 2 EEC, a model was fitted using cyclin D1, SMAD1, fibronectin and beta catenin, with AUC 0.89 (training) and 0.72 (MDACC test set). High levels of fibronectin and cyclin D1 were, associated with metastatic lymph nodes (p Conclusion We show that data-driven integrative models, adding protein markers to readily available clinical information, have potential to significantly improve stratification of patients at risk for lymph node metastasis in EEC, including low-risk EEC. Disclosure Nothing to disclose.