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

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*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

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.

Original languageEnglish
Article number3632
JournalDiagnostics
Volume13
Issue number24
DOIs
StatePublished - 12 2023

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Keywords

  • computer-aided diagnosis
  • lymphoma
  • quantitative imaging biomarkers
  • radiomics
  • splenomegaly

Fingerprint

Dive into the research topics of 'Machine Learning Radiomics Signature for Differentiating Lymphoma versus Benign Splenomegaly on CT'. Together they form a unique fingerprint.

Cite this