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

ABSTRACT: 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

SUBMITTER: Boehm KM 

PROVIDER: S-EPMC9239907 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature