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

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
Source :
Diagnostics, Vol 13, Iss 24, p 3632 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

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 considered to be significant. Results: Seven features were selected for constructing the radiomic signature after feature selection, including first-order statistics (10th percentile and Robust Mean Absolute Deviation), shape-based (Surface Area), and texture features (Correlation, MCC, Small Area Low Gray-level Emphasis and Low Gray-level Zone Emphasis). The radiomic signature achieved an excellent diagnostic accuracy of 97%, sensitivity of 89%, and specificity of 98%, distinguishing lymphoma versus benign splenomegaly in the testing dataset. The radiomic signature significantly correlated with the platelet and segmented neutrophil percentage. Conclusions: CT-based radiomics signature can be useful in distinguishing lymphoma versus benign splenomegaly and can reflect the changes in underlying blood profiles.

Details

Language :
English
ISSN :
13243632 and 20754418
Volume :
13
Issue :
24
Database :
Directory of Open Access Journals
Journal :
Diagnostics
Publication Type :
Academic Journal
Accession number :
edsdoj.1d90bfd6cbb469db2e2fe102c375fac
Document Type :
article
Full Text :
https://doi.org/10.3390/diagnostics13243632