Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer.

ABSTRACT: A major challenge in radiomics is assembling data from multiple centers. Sharing data between hospitals is restricted by legal and ethical regulations. Distributed learning is a technique, enabling training models on multicenter data without data leaving the hospitals ("privacy-preserving" distributed learning). This study tested feasibility of distributed learning of radiomics data for prediction of two year overall survival and HPV status in head and neck cancer (HNC) patients. Pretreatment CT images were collected from 1174 HNC patients in 6 different cohorts. 981 radiomic features were extracted using Z-Rad software implementation. Hierarchical clustering was performed to preselect features. Classification was done using logistic regression. In the validation dataset, the receiver oper

SUBMITTER: Bogowicz M 

PROVIDER: S-EPMC7066122 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature