Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer
Menée à l'aide de données portant sur 444 patientes atteintes d'un cancer séreux ovarien primitif de haut grade et de stade avancé, cette étude met en évidence l'intérêt de fusionner des données histopathologiques, radiologiques et clinico-génomiques à l'aide d'algorithmes d'apprentissage automatique pour établir avec plus de précision un pronostic et améliorer la stratification des patientes
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.
Nature Cancer , article en libre accès, 2022