Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer

Shah and colleagues develop a multimodal data integration framework that interprets genomic, digital histopathology, radiomics and clinical data using machine learning to improve diagnosis of patients with high-grade ovarian serous carcinoma. Patients with high-grade serous ovarian cancer suffer poor prognosis and variable response to treatment. Known prognostic factors for this disease include homologous recombination deficiency status, age, pathological stage and residual disease status after debulking surgery. Recent work has highlighted important prognostic information captured in computed tomography and histopathological specimens, which can be exploited through machine learning. However, little is known about the capacity of combining features from these disparate sources to improve prediction of treatment response. Here, we assembled a multimodal dataset of 444 patients with primarily late-stage high-grade serous ovarian cancer and discovered quantitative features, such as tumor nuclear size on staining with hematoxylin and eosin and omental texture on contrast-enhanced computed tomography, associated with prognosis. We 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.

Automated summary

In this study, a multimodal data integration framework using machine learning was developed to interpret various types of data and improve the diagnosis accuracy of high-grade ovarian serous carcinoma. The research uncovered quantitative features that contributed to prognosis, and demonstrated the potential of integrating data from different sources for risk stratification of cancer patients.