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

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

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 diagnos...

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Bibliographic Details
Main Authors: Jih-An Cheng, Yu-Chun Lin, Yenpo Lin, Ren-Chin Wu, Hsin-Ying Lu, Lan-Yan Yang, Hsin-Ju Chiang, Yu-Hsiang Juan, Ying-Chieh Lai, Gigin Lin
Format: Article
Language:English
Published: MDPI AG 2023-12-01
Series:Diagnostics
Subjects:
computer-aided diagnosis
quantitative imaging biomarkers
radiomics
lymphoma
splenomegaly
Online Access:https://www.mdpi.com/2075-4418/13/24/3632

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https://www.mdpi.com/2075-4418/13/24/3632

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