Applying quantitative adiposity feature analysis models to predict benefit of bevacizumab-based chemotherapy in ovarian cancer patients

Yunzhi Wang; Yuchen Qiu; Theresa Thai; Kathleen More; Kai Ding; Hong Liu; Bin Zheng

doi:10.1117/12.2214230

24 March 2016 Applying quantitative adiposity feature analysis models to predict benefit of bevacizumab-based chemotherapy in ovarian cancer patients

Yunzhi Wang, Yuchen Qiu, Theresa Thai, Kathleen More, Kai Ding, Hong Liu, Bin Zheng

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Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97850U (2016) https://doi.org/10.1117/12.2214230
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

How to rationally identify epithelial ovarian cancer (EOC) patients who will benefit from bevacizumab or other antiangiogenic therapies is a critical issue in EOC treatments. The motivation of this study is to quantitatively measure adiposity features from CT images and investigate the feasibility of predicting potential benefit of EOC patients with or without receiving bevacizumab-based chemotherapy treatment using multivariate statistical models built based on quantitative adiposity image features. A dataset involving CT images from 59 advanced EOC patients were included. Among them, 32 patients received maintenance bevacizumab after primary chemotherapy and the remaining 27 patients did not. We developed a computer-aided detection (CAD) scheme to automatically segment subcutaneous fat areas (VFA) and visceral fat areas (SFA) and then extracted 7 adiposity-related quantitative features. Three multivariate data analysis models (linear regression, logistic regression and Cox proportional hazards regression) were performed respectively to investigate the potential association between the model-generated prediction results and the patients’ progression-free survival (PFS) and overall survival (OS). The results show that using all 3 statistical models, a statistically significant association was detected between the model-generated results and both of the two clinical outcomes in the group of patients receiving maintenance bevacizumab (p<0.01), while there were no significant association for both PFS and OS in the group of patients without receiving maintenance bevacizumab. Therefore, this study demonstrated the feasibility of using quantitative adiposity-related CT image features based statistical prediction models to generate a new clinical marker and predict the clinical outcome of EOC patients receiving maintenance bevacizumab-based chemotherapy.

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Yunzhi Wang, Yuchen Qiu, Theresa Thai, Kathleen More, Kai Ding, Hong Liu, and Bin Zheng "Applying quantitative adiposity feature analysis models to predict benefit of bevacizumab-based chemotherapy in ovarian cancer patients", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97850U (24 March 2016); https://doi.org/10.1117/12.2214230

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KEYWORDS

Image segmentation

Computed tomography

Statistical analysis

Computer aided diagnosis and therapy

Feature extraction

Ovarian cancer

Brain-machine interfaces

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