ArticleCancers2023
Combination of FDG PET/CT Radiomics and Clinical Parameters for Outcome Prediction in Patients with Hodgkin's Lymphoma.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Deep learning-based PET/CT mixture-of-experts model for relapse risk stratification in relapsed/refractory classical hodgkin lymphoma: a multicenter study.European journal of nuclear medicine and molecular imaging · 2026Article
- A deep learning framework for lesion-level treatment response prediction in hodgkin lymphoma using PET/CT tensor radiomics.EJNMMI physics · 2026Article
- Beyond PD-1: Mechanisms of Resistance to Checkpoint Blockade in Classical Hodgkin Lymphoma and Next-Generation Immune Strategies.European journal of haematology · 2026Review
- Article
- Article
- Automatic Lugano staging for risk stratification in lymphoma: a multicenter PET radiomics and machine learning study with survival analysis.Nuclear medicine communications · 2025Article
- Texture analysis and metabolic parameters ofEuropean journal of nuclear medicine and molecular imaging · 2025Article
- Current and Future Applications of PET Radiomics in Radiation Oncology.PET clinics · 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
- Coefficient of variation and texture analysis of 18F-FDG PET/CT images for the prediction of outcome in patients with multiple myeloma.Annals of hematology · 2024Article
- Predictive value of delta-radiomic features for prognosis of advanced non-small cell lung cancer patients undergoing immune checkpoint inhibitor therapy.Translational lung cancer research · 2024Article
- Semiquantitative 2-[Frontiers in medicine · 2024Review
- Machine Learning Radiomics Signature for Differentiating Lymphoma versus Benign Splenomegaly on CT.Diagnostics (Basel, Switzerland) · 2023Article
- Methodological evaluation of original articles on radiomics and machine learning for outcome prediction based on positron emission tomography (PET).Nuklearmedizin. Nuclear medicine · 2023Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe aim of the study is to evaluate the prognostic value of a joint evaluation of PET and CT radiomics combined with standard clinical parameters in patients with HL.
methodsOverall, 88 patients (42 female and 46 male) with a median age of 43.3 (range 21-85 years) were included. Textural analysis of the PET/CT images was performed using freely available software (LIFE X). 65 radiomic features (RF) were evaluated. Univariate and multivariate models were used to determine the value of clinical characteristics and FDG PET/CT radiomics in outcome prediction. In addition, a binary logistic regression model was used to determine potential predictors for radiotherapy treatment and odds ratios (OR), with 95% confidence intervals (CI) reported. Features relevant to survival outcomes were assessed using Cox proportional hazards to calculate hazard ratios with 95% CI.
resultsalbumin (
conclusionWe evaluated the value of baseline clinical parameters as well as combined PET and CT radiomics in HL patients for survival and the prediction of the need for radiotherapy treatment. We found that different combinations of all three factors/features were independently predictive of the here evaluated endpoints.
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