Validation of Deep Learning-based Augmentation for Reduced F-FDG Dose for PET/MRI in Children and Young Adults with Lymphoma.

Purpose: To investigate if a deep learning convolutional neural network (CNN) could enable low-dose fluorine 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]) underwent F-FDG PET/MRI between July 2015 and August 2019 at baseline and after induction chemotherapy. Full-dose F-FDG PET data (3 MBq/kg) were simulated to lower F-FDG doses based on the percentage of coincidence events (representing simulated 75%, 50%, 25%, 12.5%, and 6.25% F-FDG dose [hereafter referred to as 75%, 50%, 25%, 12.5%, and 6.25%, 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 (SUV) 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 SUV increased. A dose below 75% of the full dose led to erroneous upstaging of adequate responders to inadequate responders (43% [six of 14 patients] for 75%; 93% [13 of 14 patients] for 50%; and 100% [14 of 14 patients] below 50%; < .05 for all). CNN-enhanced low-dose PET/MRI scans at 75% and 50% 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%, and augmented 50% images.

Conclusion: CNN enhancement of PET/MRI scans may enable 50% F-FDG dose reduction with correct treatment response assessment of children and young adults with lymphoma. Pediatrics, PET/MRI, Computer Applications Detection/Diagnosis, Lymphoma, Tumor Response, Whole-Body Imaging, Technology AssessmentClinical trial registration no: NCT01542879 © RSNA, 2021.