Stratifying Ovarian Cancer Risk Using Personal Health Data - Hart, Gregory R.; Nartowt, Bradley J.; Muhammad, Wazir; Liang, Ying; Huang, Gloria S.; Deng, Jun (Frontiers in Big Data, 2019)
PURPOSE: Screening the general population for ovarian cancer is not recommended by every major medical or public health organization because the harms from screening outweigh the benefit it provides. To improve ovarian cancer detection and survival many are looking at high- risk populations who would benefit from screening. METHODS: We train a neural network on readily available personal health data to predict and stratify ovarian cancer risk. We use two different datasets to train our network: The National Health Interview Survey and Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. RESULTS: Our model has an area under the receiver operating characteristic curve of 0.71. We further demonstrate how the model could be used to stratify patients into different risk categories. A simple 3-tier scheme classifies 23.8% of those with cancer and 1.0% of those without as high-risk similar to genetic testing, and 1.1% of those with cancer and 24.4% of those without as low risk. CONCLUSION: The developed neural network offers a cost-effective and non-invasive way to identify those who could benefit from targeted screening.