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3 March 2017 Apply radiomics approach for early stage prognostic evaluation of ovarian cancer patients: a preliminary study
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Predicting metastatic tumor response to chemotherapy at early stage is critically important for improving efficacy of clinical trials of testing new chemotherapy drugs. However, using current response evaluation criteria in solid tumors (RECIST) guidelines only yields a limited accuracy to predict tumor response. In order to address this clinical challenge, we applied Radiomics approach to develop a new quantitative image analysis scheme, aiming to accurately assess the tumor response to new chemotherapy treatment, for the advanced ovarian cancer patients. During the experiment, a retrospective dataset containing 57 patients was assembled, each of which has two sets of CT images: pre-therapy and 4-6 week follow up CT images. A Radiomics based image analysis scheme was then applied on these images, which is composed of three steps. First, the tumors depicted on the CT images were segmented by a hybrid tumor segmentation scheme. Then, a total of 115 features were computed from the segmented tumors, which can be grouped as 1) volume based features; 2) density based features; and 3) wavelet features. Finally, an optimal feature cluster was selected based on the single feature performance and an equal-weighed fusion rule was applied to generate the final predicting score. The results demonstrated that the single feature achieved an area under the receiver operating characteristic curve (AUC) of 0.838±0.053. This investigation demonstrates that the Radiomic approach may have the potential in the development of high accuracy predicting model for early stage prognostic assessment of ovarian cancer patients.
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Gopichandh Danala, Yunzhi Wang, Theresa Thai, Camille Gunderson, Katherine Moxley, Kathleen Moore, Robert Mannel, Hong Liu, Bin Zheng, and Yuchen Qiu "Apply radiomics approach for early stage prognostic evaluation of ovarian cancer patients: a preliminary study", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013449 (3 March 2017);

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