ReviewMethods (San Diego, Calif.)2021
Functional imaging using radiomic features in assessment of lymphoma.
Review in Methods (San Diego, Calif.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- Radiogenomics in Lymphoma and Multiple Myeloma: A Systematic Review of Current Evidence and Future Directions.Journal of clinical medicine · 2026Review
- Identification of superficial invasive and indolent lymphomatous lymph nodes by multiple ultrasonographic vascular imaging.Scientific reports · 2025Article
- Development and validation of multiple machine learning algorithms for differentiating primary central nervous system lymphoma from adult-type diffuse glioma: an interpretable and multicenter study.Frontiers in oncology · 2025Article
- The Role of Artificial Intelligence and Radiomics in the Management of Lymphomas by PET/CT: The Clairvoyance in Clinic.Cancer management and research · 2025Review
- Clinical scoring systems, molecular subtypes and baseline [Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- The Diagnostic Performance of 2-[Cancers · 2024Review
- Leptomeningeal Metastasis: A Review of the Pathophysiology, Diagnostic Methodology, and Therapeutic Landscape.Current oncology (Toronto, Ont.) · 2023Review
- Article
- CT radiomics to predict Deauville score 4 positive and negative Hodgkin lymphoma manifestations.Scientific reports · 2022Article
- Role of Radiomics-Based Baseline PET/CT Imaging in Lymphoma: Diagnosis, Prognosis, and Response Assessment.Molecular imaging and biology · 2022Review
- Bruton's tyrosine kinase inhibitors in the treatment of primary central nervous system lymphoma: A mini-review.Frontiers in oncology · 2022Review
- Two-Year Event-Free Survival Prediction in DLBCL Patients Based onFrontiers in oncology · 2022Article
- Novel insights into the biomarkers and therapies for primary central nervous system lymphoma.Therapeutic advances in medical oncology · 2022Review
- Automatic classification of lymphoma lesions in FDG-PET-Differentiation between tumor and non-tumor uptake.PloS one · 2022Article
- Artificial Intelligence in Lymphoma PET Imaging:: A Scoping Review (Current Trends and Future Directions).PET clinics · 2022Article
- Positron emission tomography and magnetic resonance imaging in primary central nervous system lymphoma-a narrative review.Annals of lymphoma · 2021Article
- Prognostic Value of Baseline Radiomic Features ofDiagnostics (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Lymphomas are typically large, well-defined, and relatively homogeneous tumors, and therefore represent ideal targets for the use of radiomics. Of the available functional imaging tests, [18F]FDG-PET for body lymphoma and diffusion-weighted MRI (DWI) for central nervous system (CNS) lymphoma are of particular interest. The current literature suggests that two main applications for radiomics in lymphoma show promise: differentiation of lymphomas from other tumors, and lymphoma treatment response and outcome prognostication. In particular, encouraging results reported in the limited number of presently available studies that utilize functional imaging suggest that (1) MRI-based radiomics enables differentiation of CNS lymphoma from glioblastoma, and (2) baseline [18F]FDG-PET radiomics could be useful for survival prognostication, adding to or even replacing commonly used metrics such as standardized uptake values and metabolic tumor volume. However, due to differences in biological and clinical characteristics of different lymphoma subtypes and an increasing number of treatment options, more data are required to support these findings. Furthermore, a consensus on several critical steps in the radiomics workflow -most importantly, image reconstruction and post processing, lesion segmentation, and choice of classification algorithm- is desirable to ensure comparability of results between research institutions.
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