Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer.

ABSTRACT: High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomi

SUBMITTER: Crispin-Ortuzar M 

PROVIDER: S-EPMC10598212 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature