CT-based radiomics model with machine learning for predicting primary treatment failure in diffuse large B-cell Lymphoma.

ABSTRACT: Biomarkers which can identify Diffuse Large B-Cell Lymphoma (DLBCL) likely to be refractory to first-line therapy are essential for selecting this population prior to therapy initiation to offer alternate therapeutic options that can improve prognosis. We tested the ability of a CT-based radiomics approach with machine learning to predict Primary Treatment Failure (PTF)-DLBCL from initial imaging evaluation. Twenty-six refractory patients were matched to 26 non-refractory patients, yielding 180 lymph nodes for analysis. Manual 3D delineation of the total node volume was performed by two independent readers to test the reproducibility. Then, 1218 hand-crafted radiomic features were extracted. The Random Forests machine learning approach was used as a classifier for constructing the predicti

SUBMITTER: Santiago R 

PROVIDER: S-EPMC8348197 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature