Dr. Kevin M. Boehm et al. published the paper Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer in Nature Cancer. In it they assemble a multimodal dataset of 444 patients with primarily late-stage high-grade serous ovarian cancer and discover quantitative features, such as tumor nuclear size on staining with hematoxylin and eosin and omental texture on contrast-enhanced computed tomography, associated with prognosis. They found that these features contributed complementary prognostic information relative to one another and clinicogenomic features. By fusing histopathological, radiologic and clinicogenomic machine-learning models, we demonstrate a promising path toward improved risk stratification of patients with cancer through multimodal data integration.
You can read the paper in Nature Cancer here.