ReviewCancer management and research2025
The Role of Artificial Intelligence and Radiomics in the Management of Lymphomas by PET/CT: The Clairvoyance in Clinic.
Review in Cancer management and research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Optimal peritumoral regions and fusion strategies for prediction of the double-expressor subtype in diffuse large B-cell lymphoma: a multi-region radiomics study.Frontiers in oncology · 2026Article
- Artificial intelligence based quantification of T lymphocyte infiltrate predicts prognosis in high grade breast cancer using deep learning and statistical validation.Discover oncology · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lymphomas are a hematopoietic malignancies that encompass over 90 subtypes. Traditionally, they have been categorized into two main groups, non-Hodgkin lymphoma (NHL) and Hodgkin lymphoma (HL). Based on morphology and immunohistochemistry, HL can be classified into nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) and classical HL (cHL). NHL represents the most common form of lymphoma, including more than 50 subtypes, such as mantle cell lymphoma (MCL), follicular lymphoma (FL), marginal zone lymphoma (MZL), and the most common, diffuse large B-cell lymphoma (DLBCL). Medical imaging plays a pivotal role in lymphoma management, with positron emission tomography/computed tomography (PET/CT) serving as an indispensable tool. 2-Deoxy-2-[fluorine-18]fluoro-D-glucose (18F-FDG) PET/CT is extensively utilized in lymphoma management, having demonstrated its value in providing crucial data for precise disease burden quantification, treatment response evaluation, and prognostic assessment. Radiomics is an innovative approach that entails the computer-aided extraction of quantitative, searchable data from medical images and its association with biological and clinical outcomes. The rapid advancement of radiomics research has opened new avenues for cancer diagnosis and therapy. Our findings indicate that artificial intelligence based PET/CT radiomics has demonstrated significant potential in lymphoma diagnosis, subtyping, staging, treatment selection, and survival prognosis assessment, offering clinicians powerful decision-support tools. However, challenges remain, such as the lack of standardized image quality in machine learning applications.
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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.