Etchebehere E, Andrade R, Camacho M, Lima M, Brink A, Cerci JJ, Nadel H, Bal C, Rangarajan V, Pfluger T, Kagna O, Alonso O, Begum FK, Mir KB, Magboo VP, Menezes LJ, Paez D, Pascual T. VALIDATION OF
CONVOLUTIONAL NEURAL NETWORK FOR FAST DETERMINATION OF WHOLE-BODY METABOLIC TUMOR BURDEN IN PEDIATRIC LYMPHOMA.
J Nucl Med Technol 2022;
50:256-262. [PMID:
35440476 DOI:
10.2967/jnmt.121.262900]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 02/10/2022] [Indexed: 11/16/2022] Open
Abstract
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.
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