Machine Learning Radiomics Signature for Differentiating Lymphoma versus Benign Splenomegaly on CT.

ABSTRACT:

Background

We aimed to develop and validate a preoperative CT-based radiomics signature for differentiating lymphoma versus benign splenomegaly.

Methods

We retrospectively analyzed CT studies from 139 patients (age range 26-93 years, 43% female) between 2011 and 2019 with histopathological diagnosis of the spleen (19 lymphoma, 120 benign) and divided them into developing (n = 79) and testing (n = 60) datasets. The volumetric radiomic features were extracted from manual segmentation of the whole spleen on venous-phase CT imaging using PyRadiomics package. LASSO regression was applied for feature selection and development of the radiomic signature, which was interrogated with the complete blood cell count and differential count. All p values < 0.05 were co

SUBMITTER: Cheng JA 

PROVIDER: S-EPMC10742777 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature