Artificial intelligence in immunotherapy PET/SPECT imaging.

Autor: McGale JP; Department of Radiology, New York-Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA. jm4782@cumc.columbia.edu., Chen DL; Department of Molecular Imaging and Therapy, Fred Hutchinson Cancer Center, Seattle, WA, USA.; Department of Radiology, University of Washington, Seattle, WA, USA., Trebeschi S; Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands.; GROW School of Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands., Farwell MD; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA., Wu AM; Department of Immunology and Theranostics, Beckman Research Institute of City of Hope, Duarte, CA, USA., Cutler CS; Collider Accelerator Department, Brookhaven National Laboratory, Upton, NY, USA., Schwartz LH; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA., Dercle L; Department of Radiology, New York-Presbyterian Hospital, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA. ld2752@cumc.columbia.edu.
Jazyk: angličtina
Zdroj: European radiology [Eur Radiol] 2024 Sep; Vol. 34 (9), pp. 5829-5841. Date of Electronic Publication: 2024 Feb 15.
DOI: 10.1007/s00330-024-10637-3
Abstrakt: Objective: Immunotherapy has dramatically altered the therapeutic landscape for oncology, but more research is needed to identify patients who are likely to achieve durable clinical benefit and those who may develop unacceptable side effects. We investigated the role of artificial intelligence in PET/SPECT-guided approaches for immunotherapy-treated patients.
Methods: We performed a scoping review of MEDLINE, CENTRAL, and Embase databases using key terms related to immunotherapy, PET/SPECT imaging, and AI/radiomics through October 12, 2022.
Results: Of the 217 studies identified in our literature search, 24 relevant articles were selected. The median (interquartile range) sample size of included patient cohorts was 63 (157). Primary tumors of interest were lung (n = 14/24, 58.3%), lymphoma (n = 4/24, 16.7%), or melanoma (n = 4/24, 16.7%). A total of 28 treatment regimens were employed, including anti-PD-(L)1 (n = 13/28, 46.4%) and anti-CTLA-4 (n = 4/28, 14.3%) monoclonal antibodies. Predictive models were built from imaging features using univariate radiomics (n = 7/24, 29.2%), radiomics (n = 12/24, 50.0%), or deep learning (n = 5/24, 20.8%) and were most often used to prognosticate (n = 6/24, 25.0%) or describe tumor phenotype (n = 5/24, 20.8%). Eighteen studies (75.0%) performed AI model validation.
Conclusion: Preliminary results suggest broad potential for the application of AI-guided immunotherapy management after further validation of models on large, prospective, multicenter cohorts.
Clinical Relevance Statement: This scoping review describes how artificial intelligence models are built to make predictions based on medical imaging and explores their application specifically in the PET and SPECT examination of immunotherapy-treated cancers.
Key Points: • Immunotherapy has drastically altered the cancer treatment landscape but is known to precipitate response patterns that are not accurately accounted for by traditional imaging methods. • There is an unmet need for better tools to not only facilitate in-treatment evaluation but also to predict, a priori, which patients are likely to achieve a good response with a certain treatment as well as those who are likely to develop side effects. • Artificial intelligence applied to PET/SPECT imaging of immunotherapy-treated patients is mainly used to make predictions about prognosis or tumor phenotype and is built from baseline, pre-treatment images. Further testing is required before a true transition to clinical application can be realized.
(© 2024. The Author(s), under exclusive licence to European Society of Radiology.)
Databáze: MEDLINE
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