Endogenous mutational processes are an established determinant of clinical course, with improved response of homologous recombination-deficient (HRD) disease to platinum-based chemotherapy and poly-ADP ribose polymerase (PARP) inhibitors 2, 3, 4. Initial clinical management relies on either primary debulking surgery (PDS) or neoadjuvant chemotherapy followed by interval debulking surgery (NACT-IDS). High-grade serous ovarian cancer (HGSOC) is the most common cause of death from gynecologic malignancies, with a 5-year survival rate of less than 30% for metastatic disease 1. 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. We found that these features contributed complementary prognostic information relative to one another and clinicogenomic features. 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. However, little is known about the capacity of combining features from these disparate sources to improve prediction of treatment response. Recent work has highlighted important prognostic information captured in computed tomography and histopathological specimens, which can be exploited through machine learning. Known prognostic factors for this disease include homologous recombination deficiency status, age, pathological stage and residual disease status after debulking surgery. Patients with high-grade serous ovarian cancer suffer poor prognosis and variable response to treatment. Nature Cancer volume 3, pages 723–733 ( 2022) Cite this article

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

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