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

MSK MIND Consortium

Research output: Contribution to journalArticlepeer-review

73 Scopus citations

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 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.

Original languageEnglish
Pages (from-to)723-733
Number of pages11
JournalNature Cancer
Volume3
Issue number6
DOIs
StatePublished - Jun 2022
Externally publishedYes

Fingerprint

Dive into the research topics of 'Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer'. Together they form a unique fingerprint.

Cite this