| Literature DB >> 34809864 |
Navid Hasani1, Sriram S Paravastu2, Faraz Farhadi2, Fereshteh Yousefirizi3, Michael A Morris4, Arman Rahmim5, Mark Roschewski6, Ronald M Summers7, Babak Saboury8.
Abstract
Malignant lymphomas are a family of heterogenous disorders caused by clonal proliferation of lymphocytes. 18F-FDG-PET has proven to provide essential information for accurate quantification of disease burden, treatment response evaluation, and prognostication. However, manual delineation of hypermetabolic lesions is often a time-consuming and impractical task. Applications of artificial intelligence (AI) may provide solutions to overcome this challenge. Beyond segmentation and detection of lesions, AI could enhance tumor characterization and heterogeneity quantification, as well as treatment response prediction and recurrence risk stratification. In this scoping review, we have systematically mapped and discussed the current applications of AI (such as detection, classification, segmentation as well as the prediction and prognostication) in lymphoma PET. Published by Elsevier Inc.Entities:
Keywords: Artificial intelligence; Deep learning; Detection; Lymphoma; Positron emission tomography (PET); Radiomics; Radiophenomics; Segmentation
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Year: 2022 PMID： 34809864 PMCID： PMC8735853 DOI： 10.1016/j.cpet.2021.09.006
Source DB: PubMed Journal: PET Clin ISSN： 1556-8598