ArticleDiagnostics (Basel, Switzerland)2023
Machine Learning Radiomics Signature for Differentiating Lymphoma versus Benign Splenomegaly on CT.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 3 citations in OpenAlex.
- Splenic FDG PET uptake and CT volume as prognostic biomarkers in diffuse large B cell lymphoma.La Radiologia medica · 2026Article
- Diagnosing breast masses in BI-RADS 4: construction and interpretation of an intralesional and perilesional radiomics nomogram based on digital breast tomosynthesis.BMC medical imaging · 2026Article
- Distinguishing lymphoma from benign lymphadenopathy in fever of unknown origin using a diagnostic model based on PET/CT and clinical-laboratory parameters.EJNMMI research · 2026Article
- Radiological Approach to Splenomegaly: Etiologies, Pathophysiologies, and Diagnostic Strategies.Korean journal of radiology · 2026Review
- PET/CT radiomics-biomarker diagnostic model for identifying lymphoma in fever of unknown origin.Frontiers in immunology · 2026Article
- A dataset for splenomegaly and its related findings in CT imaging.Data in brief · 2025Article
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundWe aimed to develop and validate a preoperative CT-based radiomics signature for differentiating lymphoma versus benign splenomegaly.
methodsWe 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 (
resultsSeven 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.
conclusionsCT-based radiomics signature can be useful in distinguishing lymphoma versus benign splenomegaly and can reflect the changes in underlying blood profiles.
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