RT Journal Article
SR Electronic
T1 VALIDATION OF CONVOLUTIONAL NEURAL NETWORK FOR FAST DETERMINATION OF WHOLE-BODY METABOLIC TUMOR BURDEN IN PEDIATRIC LYMPHOMA
JF Journal of Nuclear Medicine Technology
JO J. Nucl. Med. Technol.
FD Society of Nuclear Medicine
SP jnmt.121.262900
DO 10.2967/jnmt.121.262900
A1 Elba Etchebehere
A1 Rebeca Andrade
A1 Mariana Camacho
A1 Mariana Lima
A1 Anita Brink
A1 Juliano Julio Cerci
A1 Helen Nadel
A1 Chandrasekhar Bal
A1 Venkatesh Rangarajan
A1 Thomas Pfluger
A1 Olga Kagna
A1 Omar Alonso
A1 Fatima K. Begum
A1 Kahkashan Bashir Mir
A1 Vincent Peter Magboo
A1 Leon J Menezes
A1 Diana Paez, Dr
A1 Thomas Pascual, Dr
YR 2022
UL http://tech.snmjournals.org/content/early/2022/04/19/jnmt.121.262900.abstract
AB INTRODUCTION: 18F-FDG PET/CT whole-body tumor burden in lymphoma is not routinely performed due to the lack of fast quantification methods. Although the semi-automatic method is fast, it still lacks the necessary speed required to quantify tumor burden in daily clinical practice. PURPOSE: To evaluate the performance of the convolutional neural networks (CNN) software to localize neoplastic lesions in whole-body 18F-FDG PET/CT images of pediatric lymphoma patients. METHODS: This retrospective image data set, derived from the data pool under the IAEA (CRP# E12017), included 102 baseline staging 18F-FDG PET/CTs of pediatric lymphoma patients (mean age 11 yrs). Images were quantified to determine the whole-body (wb) tumor burden (wbMTV and wbTLG) using a semi-automatic (SEMI) software and an CNN-based software. Both were displayed as wbMTVSEMI & wbTLGSEMI and wbMTVCNN & TLGCNN. The intraclass correlation coefficient (ICC) was applied to evaluate concordance between the CNN-based software and the SEMI software. RESULTS: Twenty-six patients were excluded from the analyses because the software was unable to perform calculation. In the remaining 76 patients, wbMTVCNN and wbMTVSEMI whole-body tumor burden metrics were highly correlated (ICC=0.993; 95%CI: 0.989 -0.996; p-value<0.0001) as were wbTLGCNN and wbTLGSEMI (ICC=0.999; 95%CI: 0.998-0.999; p-value<0.0001). However, the time spent calculating these metrics was significantly (<0.0001) faster by CNN (mean = 19 seconds; 11 - 50 seconds) compared to the semi-automatic method (mean = 21.6 minutes; 3.2 – 62.1 minutes), especially in patients with advanced disease. CONCLUSION: Determining whole-body tumor burden in pediatric lymphoma patients using CNN is fast and feasible in clinical practice.