ArticleCancers2023
Characterization of Mediastinal Bulky Lymphomas with FDG-PET-Based Radiomics and Machine Learning Techniques.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Diagnostic Value ofTechnology in cancer research & treatmentPooled it
- Metabolic heterogeneity-based radiomic model predicts treatment outcomes of primary mediastinal B-cell lymphoma patients in the IELSG37 study.British journal of haematology · 2026Trial
- Enhanced Lymphoma Subtype Classification and Prognosis Using Machine Learning withNuclear medicine and molecular imaging · 2026Article
- ¹⁸F-FDG PET radiomics and machine learning for virtual biopsy and treatment decisions in lymphoma: a multicenter study.Physical and engineering sciences in medicine · 2026Article
- Preoperative risk stratification of mediastinal masses using a predictive model based on CT radiomics and clinical data.Frontiers in surgery · 2026Article
- 18F-FDG PET/CT Radiomics for Predicting Therapy Response in Primary Mediastinal B-Cell Lymphoma: A Bi-Centric Pilot Study.Cancers · 2025Article
- Primary Mediastinal B-Cell Lymphoma and [18F]FDG PET/CT: What We Learned and What Is New.Hematology reports · 2025Review
- Molecular pathology of lymphoma and its treatment strategies: from mechanistic elucidation to precision medicine.Frontiers in immunology · 2025Review
- The Role of Artificial Intelligence and Radiomics in the Management of Lymphomas by PET/CT: The Clairvoyance in Clinic.Cancer management and research · 2025Review
- Review
- Prediction of Histological Grade of Oral Squamous Cell Carcinoma Using Machine Learning Models Applied toBiomedicines · 2024Article
- Clinical application ofJapanese journal of radiology · 2024Review
Corrections and comments
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Authors and funding
15 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study tested the diagnostic value of 18F-FDG PET/CT (FDG-PET) volumetric and texture parameters in the histological differentiation of mediastinal bulky disease due to classical Hodgkin lymphoma (cHL), primary mediastinal B-cell lymphoma (PMBCL) and grey zone lymphoma (GZL), using machine learning techniques.
methodsWe reviewed 80 cHL, 29 PMBCL and 8 GZL adult patients with mediastinal bulky disease and histopathological diagnoses who underwent FDG-PET pre-treatment. Volumetric and radiomic parameters were measured using FDG-PET both for bulky lesions (BL) and for all lesions (AL) using LIFEx software (threshold SUV ≥ 2.5). Binary and multiclass classifications were performed with various machine learning techniques fed by a relevant subset of radiomic features.
resultsThe analysis showed significant differences between the lymphoma groups in terms of SUVmax, SUVmean, MTV, TLG and several textural features of both first- and second-order grey level. Among machine learning classifiers, the tree-based ensembles achieved the best performance both for binary and multiclass classifications in histological differentiation.
conclusionsOur results support the value of metabolic heterogeneity as an imaging biomarker, and the use of radiomic features for early characterization of mediastinal bulky lymphoma.
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