Artificial Intelligence May Predict Cancer Survival Better Than Current Methods, Study Suggests

Artificial Intelligence May Predict Cancer Survival Better Than Current Methods, Study Suggests
Using diagnostic images, artificial intelligence is better able to predict ovarian cancer survival than other current prognostic methods, a study suggests. The study, "A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer," was published in Nature Communications. A diagnosis of ovarian cancer is often confirmed with a computerized tomography (CT) scan, which creates a detailed image of a tumor using x-rays. These images can help clinicians make decisions about how far the disease has spread and what treatments might be effective — but they're not very useful for predicting outcomes. In the study, researchers employed a software tool called TEXLab to analyze CT images from 364 women with ovarian cancer between 2004 and 2015. The software assessed four basic aspects of a tumor — structure, shape, size, and genetic makeup — in order to create a novel prognostic indicator called “Radiomic Prognostic Vector” (RPV). The program was fed algorithm data from patients treated in hospital and data available through online datasets. It split patients up into three RPV categories, namely high, medium, and low risk. The researchers compared the patients' RPV scores to blood tests and other prognostic scores currently used, and found their method was up to four times better at predicting deaths f
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