Artificial Intelligence in Lymphoma PET Imaging:: A Scoping Review (Current Trends and Future Directions).

Autor:	Hasani N; Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA; University of Queensland Faculty of Medicine, Ochsner Clinical School, New Orleans, LA 70121, USA., Paravastu SS; Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA., Farhadi F; Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA., Yousefirizi F; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada., Morris MA; Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland-Baltimore Country, Baltimore, MD, USA., Rahmim A; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada; Department of Radiology, BC Cancer Research Institute, University of British Columbia, 675 West 10th Avenue, Vancouver, British Columbia, V5Z 1L3, Canada., Roschewski M; Lymphoid Malignancies Branch, Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA., Summers RM; Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA. Electronic address: rsummers@mail.cc.nih.gov., Saboury B; Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland-Baltimore Country, Baltimore, MD, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. Electronic address: babak.saboury@nih.gov.
Jazyk:	angličtina
Zdroj:	PET clinics [PET Clin] 2022 Jan; Vol. 17 (1), pp. 145-174.
DOI:	10.1016/j.cpet.2021.09.006
Abstrakt:	Malignant lymphomas are a family of heterogenous disorders caused by clonal proliferation of lymphocytes. 18 F-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. Competing Interests: Disclosure This research was supported by the Intramural Research Program of the NIH, Clinical Center and NIDCR. The opinions expressed in this publication are the author's own and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. (Published by Elsevier Inc.)
Databáze:	MEDLINE
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