Design and Implementation of a Comprehensive Web-Based Survey for Ovarian Cancer Survivorship with an Analysis of Prediagnosis Symptoms via Text Mining

Jiayang Sun, Kath M Bogie, Joe Teagno, Yu-Hsiang Sun, Yu-Hsiang Sam Sun, Rebecca R Carter, Licong Cui, Guo-Qiang Zhang, Guo‐Qiang Zhang
Cancer informatics · 2014 · vol. 13s3(Suppl 3) , pp. CIN.S14034 · doi:10.4137/cin.s14034 · PMID:25861211 · PMC4373720 · W2015800123
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Abstract

Ovarian cancer (OvCa) is the most lethal gynecologic disease in the United States, with an overall 5-year survival rate of 44.5%, about half of the 89.2% for all breast cancer patients. To identify factors that possibly contribute to the long-term survivorship of women with OvCa, we conducted a comprehensive online Ovarian Cancer Survivorship Survey from 2009 to 2013. This paper presents the design and implementation of our survey, introduces its resulting data source, the OVA-CRADLE™ (Clinical Research Analytics and Data Lifecycle Environment), and illustrates a sample application of the survey and data by an analysis of prediagnosis symptoms, using text mining and statistics. The OVA-CRADLE™ is an application of our patented Physio-MIMI technology, facilitating Web-based access, online query and exploration of data. The prediagnostic symptoms and association of early-stage OvCa diagnosis with endometriosis provide potentially important indicators for future studies in this field.

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endometriosis

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