51. Validation of Deep Learning-based Augmentation for Reduced 18 F-FDG Dose for PET/MRI in Children and Young Adults with Lymphoma.
- Author
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Theruvath AJ, Siedek F, Yerneni K, Muehe AM, Spunt SL, Pribnow A, Moseley M, Lu Y, Zhao Q, Gulaka P, Chaudhari A, and Daldrup-Link HE
- Abstract
Purpose: To investigate if a deep learning convolutional neural network (CNN) could enable low-dose fluorine 18 (
18 F) fluorodeoxyglucose (FDG) PET/MRI for correct treatment response assessment of children and young adults with lymphoma., Materials and Methods: In this secondary analysis of prospectively collected data (ClinicalTrials.gov identifier: NCT01542879), 20 patients with lymphoma (mean age, 16.4 years ± 6.4 [standard deviation]) underwent18 F-FDG PET/MRI between July 2015 and August 2019 at baseline and after induction chemotherapy. Full-dose18 F-FDG PET data (3 MBq/kg) were simulated to lower18 F-FDG doses based on the percentage of coincidence events (representing simulated 75%, 50%, 25%, 12.5%, and 6.25%18 F-FDG dose [hereafter referred to as 75%Sim , 50%Sim , 25%Sim , 12.5%Sim , and 6.25%Sim , respectively]). A U.S. Food and Drug Administration-approved CNN was used to augment input simulated low-dose scans to full-dose scans. For each follow-up scan after induction chemotherapy, the standardized uptake value (SUV) response score was calculated as the maximum SUV (SUVmax ) of the tumor normalized to the mean liver SUV; tumor response was classified as adequate or inadequate. Sensitivity and specificity in the detection of correct response status were computed using full-dose PET as the reference standard., Results: With decreasing simulated radiotracer doses, tumor SUVmax increased. A dose below 75%Sim of the full dose led to erroneous upstaging of adequate responders to inadequate responders (43% [six of 14 patients] for 75%Sim ; 93% [13 of 14 patients] for 50%Sim ; and 100% [14 of 14 patients] below 50%Sim ; P < .05 for all). CNN-enhanced low-dose PET/MRI scans at 75%Sim and 50%Sim enabled correct response assessments for all patients. Use of the CNN augmentation for assessing adequate and inadequate responses resulted in identical sensitivities (100%) and specificities (100%) between the assessment of 100% full-dose PET, augmented 75%Sim , and augmented 50%Sim images., Conclusion: CNN enhancement of PET/MRI scans may enable 50%18 F-FDG dose reduction with correct treatment response assessment of children and young adults with lymphoma. Keywords: Pediatrics, PET/MRI, Computer Applications Detection/Diagnosis, Lymphoma, Tumor Response, Whole-Body Imaging, Technology AssessmentClinical trial registration no: NCT01542879 Supplemental material is available for this article. © RSNA, 2021., Competing Interests: Disclosures of Conflicts of Interest: A.J.T. No relevant relationships. F.S. No relevant relationships. K.Y. No relevant relationships. A.M.M. No relevant relationships. S.L.S. Pharmaceutical company funding to institution to support clinical trials. A.P. No relevant relationships. M.M. No relevant relationships. Y.L. Grant to institution from Stanford University; paid consultant for Nektar and Gilead; grants to institution from NIH, Merck, and Abeona Therapeutics. Q.Z. No relevant relationships. P.G. Stockholder in and employee of Subtle Medical. A.C. Consulting fee from Subtle Medical, SkopeMR, Chondrometrics, Image Analysis Group, Edge Analytics, and Culvert Engineering; payment for writing and reviewing manuscript from Subtle Medical; paid board member at Brain Key and Chondrometrics; grants/grants pending to institution from GE Healthcare and Philips; money from patent co-ownership from LVIS; stock/stock options in Subtle Medical, LVIS, and Brain Key; travel/accommodations/meeting expenses unrelated to activities listed from Paracelsus Medical Private University (PMU). H.E.D.L. Grant to institution from Andrew MdConough B+ Foundation and National Institutes of Health., (2021 by the Radiological Society of North America, Inc.)- Published
- 2021
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