Abstract A12: Analysis of advanced quantitative computed tomography imaging features in predicting the surgical resectability of advanced epithelial ovarian cancer
Authors: Paul A. DiSilvestro, Jessica DiSilvestro, Jonathan S. Ball, Stephen Yip, Anna Schwarzbach, Katherine M. Moxley
Journal: Clinical Cancer Research (2020)

Abstract Background: Ovarian cancer is the fifth leading cause of cancer death in the United States in large part because more than 85% of patients present with metastatic disease. The majority of these are epithelial of high-grade serous (HGSOC) histology where the 5-year overall survival remains poor despite advances in chemotherapy and improved understanding of molecular tumor biology. As a heterogeneous, p53-driven malignancy, HGSOC employs a variety of molecular and metabolomic changes that promote preferential tumor growth within the peritoneal cavity. The miliary spread patterns of ovarian cancers within the peritoneal cavity create distinct clinical challenges, particularly in the preoperative assessment of surgical resectability. Therefore, it is clinically imperative to develop better imaging modalities capable of accurately predicting location, volume, and consistency of tumor implants that will inform treatment planning without exposing the patient to unnecessary harm. To this end, recent research efforts have focused on identifying novel clinical and imaging biomarkers capable of predicting optimal surgical resection, tumor response, and disease specific outcomes. Unfortunately, the clinical utility of such markers in individualizing patient specific treatment strategies remains limited. In the absence of unique molecular biomarkers, the use of advanced quantitative imaging features that utilize novel computer-aided detection (CAD) algorithms and can train artificial intelligence platforms will improve the accuracy with which current imaging modalities predict surgical outcome in HGSOC. Results: Using an established imaging platform established at Tempus Labs, Inc. we evaluated preoperative images of women undergoing cytoreductive surgery for advanced epithelial ovarian cancer. The CT images of forty-five retrospectively collected cases, with complete clinical outcomes data, are undergoing analysis. Lesions have been identified, contoured from CT images, and approved by a trained radiologist. Over 2,000 quantitative imaging (radiomic) features will be extracted from these images, and using the Tempus proprietary imaging platform, feature selection will be performed to identify factors predictive of surgical resectability and compare them to those that do not correlate with surgical outcome. These selected features will be used to model surgical outcomes data. Conclusions: Prior research has demonstrated the utility of quantitative CT image analysis in predicting response to chemotherapy and platinum sensitivity. This same technology, however, has not been applied to the surgical management of women with advanced HGSOC. In this study, we assessed the utility of similar “radiomics algorithms” in order to establish a set of radiomic features that characterize tumors amenable to optimal (R0) surgical resection. The selected features identified in this retrospective training cohort are now be tested in a larger prospective cohort of metastatic HGSOC cancer patients. Citation Format: Paul A. DiSilvestro, Jessica DiSilvestro, Jonathan S. Ball, Stephen Yip, Anna Schwarzbach, Katherine M. Moxley. Analysis of advanced quantitative computed tomography imaging features in predicting the surgical resectability of advanced epithelial ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr A12.