Deep Learning PSMA PET/CT Attenuation Correction | Oncotarget

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May 7, 2024

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  • Oncotarget published this trending research paper on May 7, 2024 in Volume 15, entitled, “Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN” by researchers from the Artificial Intelligence Resource, National Cancer Institute, National Institutes of Health, Bethesda, MD; Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD; Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD; Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD; Center for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD; Genitourinary Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD; Center for Immuno-Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD. DOI - https://doi.org/10.18632/oncotarget.28583 Correspondence to - Stephanie A. Harmon - stephanie.harmon@nih.gov Abstract Purpose: Sequential PET/CT studies oncology patients can undergo during their treatment follow-up course is limited by radiation dosage. We propose an artificial intelligence (AI) tool to produce attenuation-corrected PET (AC-PET) images from non-attenuation-corrected PET (NAC-PET) images to reduce need for low-dose CT scans. Methods: A deep learning algorithm based on 2D Pix-2-Pix generative adversarial network (GAN) architecture was developed from paired AC-PET and NAC-PET images. 18F-DCFPyL PSMA PET-CT studies from 302 prostate cancer patients, split into training, validation, and testing cohorts (n = 183, 60, 59, respectively). Models were trained with two normalization strategies: Standard Uptake Value (SUV)-based and SUV-Nyul-based. Scan-level performance was evaluated by normalized mean square error (NMSE), mean absolute error (MAE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Lesion-level analysis was performed in regions-of-interest prospectively from nuclear medicine physicians. SUV metrics were evaluated using intraclass correlation coefficient (ICC), repeatability coefficient (RC), and linear mixed-effects modeling. Results: Median NMSE, MAE, SSIM, and PSNR were 13.26%, 3.59%, 0.891, and 26.82, respectively, in the independent test cohort. ICC for SUVmax and SUVmean were 0.88 and 0.89, which indicated a high correlation between original and AI-generated quantitative imaging markers. Lesion location, density (Hounsfield units), and lesion uptake were all shown to impact relative error in generated SUV metrics (all p < 0.05). Conclusion: The Pix-2-Pix GAN model for generating AC-PET demonstrates SUV metrics that highly correlate with original images. AI-generated PET images show clinical potential for reducing the need for CT scans for attenuation correction while preserving quantitative markers and image quality. Sign up for free Altmetric alerts about this article - https://oncotarget.altmetric.com/details/email_updates?id=10.18632%2Foncotarget.28583 Subscribe for free publication alerts from Oncotarget - https://www.oncotarget.com/subscribe/ Keywords - cancer, deep learning, PSMA PET, attenuation correction About Oncotarget Oncotarget (a primarily oncology-focused, peer-reviewed, open access journal) aims to maximize research impact through insightful peer-review; eliminate borders between specialties by linking different fields of oncology, cancer research and biomedical sciences; and foster application of basic and clinical science. Oncotarget is indexed and archived by PubMed/Medline, PubMed Central, Scopus, EMBASE, META (Chan Zuckerberg Initiative) (2018-2022), and Dimensions (Digital Science). To learn more about Oncotarget, please visit https://www.oncotarget.com and connect with us: Facebook - https://www.facebook.com/Oncotarget/ X - https://twitter.com/oncotarget Instagram - https://www.instagram.com/oncotargetjrnl/ YouTube - https://www.youtube.com/@OncotargetJournal LinkedIn - https://www.linkedin.com/company/oncotarget Pinterest - https://www.pinterest.com/oncotarget/ Reddit - https://www.reddit.com/user/Oncotarget/ Spotify - https://open.spotify.com/show/0gRwT6BqYWJzxzmjPJwtVh Media Contact MEDIA@IMPACTJOURNALS.COM 18009220957

    Analytical TechniquesCancer ResearchImaging/Microscopy

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