Validation of Deep Learning-based Augmentation for Reduced 18F-FDG Dose for PET/MRI in Children and Young Adults with Lymphoma

Abstract

Purpose: To investigate if a deep learning convolutional neural network (CNN) could enable low-dose fluorine 18 (¹⁸F) 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 ¹⁸F-FDG PET/MRI between July 2015 and August 2019 at baseline and after induction chemotherapy. Full-dose ¹⁸F-FDG PET data (3 MBq/kg) were simulated to lower ¹⁸F-FDG doses based on the percentage of coincidence events (representing simulated 75%, 50%, 25%, 12.5%, and 6.25% ¹⁸F-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 (SUV_max) 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_max 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% ¹⁸F-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.

Keywords: Computer Applications Detection/Diagnosis; Lymphoma; PET/MRI; Pediatrics; Technology Assessment; Tumor Response; Whole-Body Imaging.

Graphical abstract

Figure 1:

Low-dose and deep learning–enhanced PET images in a 14-year-old boy with Hodgkin lymphoma. (A) Full-dose fluorine 18 (¹⁸F) fluorodeoxyglucose (FDG) PET image (3 MBq/kg) shows hypermetabolic mediastinal and left infra- and supraclavicular lymph nodes (arrows). (B–F) Simulated 75% (75% Sim) (B), 50% (50% Sim) (C), 25% (25% Sim) (D), 12.5% (25% Sim) (E), and 6.25% (6.25% Sim) (F) dose ¹⁸F-FDG PET images and (G) corresponding MRI scan obtained with liver acquisition with volume acceleration sequence show increased noise and reduced contrast between tumor (arrows) and liver (arrowheads in G) with simulated reduced radiotracer dose. (H–L) Convolutional neural network (CNN)–augmented low-dose ¹⁸F-FDG PET images at 75% (H), 50% (I), 25% (J), 12.5% (K), and 6.25% (L) dose show reduced noise and improved contrast between tumor and liver compared with non-CNN–augmented PET images.

Figure 2:

Tumor maximum standardized uptake value (SUV_max) increases with decreasing simulated fluorine 18 (¹⁸F) fluorodeoxyglucose (FDG) doses. (A) Baseline scans before therapy. Plot shows mean SUV_max of target tumors (n = 73) and mean standardized uptake values (SUV_mean) of liver (n = 20) and mediastinal blood pool (med bl pool) (n = 20) on PET images with decreasing simulated ¹⁸F-FDG dose levels. The SUV_max of all target lesions (circle) increases with decreasing ¹⁸F-FDG dose levels, while the SUV_mean of the liver (square) and mediastinal blood pool (triangle) remains stable. (B) Follow-up scans after induction chemotherapy. Plot shows mean SUV_max of target tumors (n = 68) and SUV_mean of liver (n = 20) and mediastinal blood pool (n = 20) on PET images with decreasing simulated ¹⁸F-FDG dose levels. For tumors with low metabolic activity, as typically noted after chemotherapy, increasing noise on low-dose images can change the relationship of tumor SUV_max to the SUV_mean of reference tissues. Whiskers are 95% CIs.

Figure 3:

Response status on (A) conventional and (B) deep learning (DL)–enhanced simulated fluorine 18 (¹⁸F) fluorodeoxyglucose (FDG) PET scans. (A) Bar chart shows that reducing the radiotracer dose to less than or equal to 75% leads to a significant underestimation of tumor therapy response and erroneous upstaging of adequate responders to inadequate responders on simulated reduced-dose ¹⁸F-FDG PET images (P < .05). (B) Bar chart shows that simulated dose reduction with DL-enhanced ¹⁸F-FDG PET scans enables dose reduction up to 50% with correct therapy response assessment of all patients.