ArticleCancers2022
Radiomics Features of the Spleen as Surrogates for CT-Based Lymphoma Diagnosis and Subtype Differentiation.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 22 citations in OpenAlex.
- Impact of Different Energy Levels of Virtual Monoenergetic Reconstructions on Radiomic Features Stability in Organic Phantom Imaging Using Photon-Counting CT.Tomography (Ann Arbor, Mich.) · 2026Article
- Article
- Discrimination of myelofibrosis and liver cirrhosis using a spleen ultrasound-based radiomics nomogram model.Experimental and therapeutic medicine · 2025Article
- Non-Hodgkin's lymphoma classification using 3D radiomics machine learning models for precision imaging in oncology.BMC medical imaging · 2025Article
- Deep learning-derived splenic radiomics, genomics, and coronary artery disease.medRxiv : the preprint server for health sciences · 2024Article
- The value of radiomics features of the spleen as surrogates for differentiating subtypes of common pediatric lymphomas.Quantitative imaging in medicine and surgery · 2024Article
- Distinguishing diffuse large B-cell lymphoma from Hodgkin's lymphoma in children using an enhanced computed tomography radiomics approach.Translational pediatrics · 2024Article
- Machine Learning Radiomics Signature for Differentiating Lymphoma versus Benign Splenomegaly on CT.Diagnostics (Basel, Switzerland) · 2023Article
- Photon-counting detector CT-based virtual monoenergetic reconstructions: repeatability and reproducibility of radiomics features of an organic phantom and human myocardium.European radiology experimental · 2023Article
- Tumor classification of gastrointestinal liver metastases using CT-based radiomics and deep learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Comparison of radiomics models and dual-energy material decomposition to decipher abdominal lymphoma in contrast-enhanced CT.International journal of computer assisted radiology and surgery · 2023Article
- Radiomics Applications in Spleen Imaging: A Systematic Review and Methodological Quality Assessment.Diagnostics (Basel, Switzerland) · 2023Review
- Phantom-based radiomics feature test-retest stability analysis on photon-counting detector CT.European radiology · 2023Article
- Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.Military Medical Research · 2023Review
- Two-Stage Deep Learning Model for Automated Segmentation and Classification of Splenomegaly.Cancers · 2022Article
- Influence of local aortic calcification on periaortic adipose tissue radiomics texture features-a primary analysis on PCCT.The international journal of cardiovascular imaging · 2022Article
- The Potential and Emerging Role of Quantitative Imaging Biomarkers for Cancer Characterization.Cancers · 2022Review
- Article
- Comparison Study of Myocardial Radiomics Feature Properties on Energy-Integrating and Photon-Counting Detector CT.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
12 authors at 5 institutions in 2 countries.
Funding
Abstract
The spleen is often involved in malignant lymphoma, which manifests on CT as either splenomegaly or focal, hypodense lymphoma lesions. This study aimed to investigate the diagnostic value of radiomics features of the spleen in classifying malignant lymphoma against non-lymphoma as well as the determination of malignant lymphoma subtypes in the case of disease presence-in particular Hodgkin lymphoma (HL), diffuse large B-cell lymphoma (DLBCL), mantle-cell lymphoma (MCL), and follicular lymphoma (FL). Spleen segmentations of 326 patients (139 female, median age 54.1 +/- 18.7 years) were generated and 1317 radiomics features per patient were extracted. For subtype classification, we created four different binary differentiation tasks and addressed them with a Random Forest classifier using 10-fold cross-validation. To detect the most relevant features, permutation importance was analyzed. Classifier results using all features were: malignant lymphoma vs. non-lymphoma AUC = 0.86 (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.