TY - JOUR T1 - Validation of Convolutional Neural Networks for Fast Determination of Whole-Body Metabolic Tumor Burden in Pediatric Lymphoma JF - Journal of Nuclear Medicine Technology JO - J. Nucl. Med. Technol. SP - 256 LP - 262 DO - 10.2967/jnmt.121.262900 VL - 50 IS - 3 AU - Elba Etchebehere AU - Rebeca Andrade AU - Mariana Camacho AU - Mariana Lima AU - Anita Brink AU - Juliano Cerci AU - Helen Nadel AU - Chandrasekhar Bal AU - Venkatesh Rangarajan AU - Thomas Pfluger AU - Olga Kagna AU - Omar Alonso AU - Fatima K. Begum AU - Kahkashan Bashir Mir AU - Vincent Peter Magboo AU - Leon J. Menezes AU - Diana Paez AU - Thomas NB Pascual Y1 - 2022/09/01 UR - http://tech.snmjournals.org/content/50/3/256.abstract N2 - 18F-FDG PET/CT quantification of whole-body tumor burden in lymphoma is not routinely performed because of the lack of fast methods. Although the semiautomatic method is fast, it is not fast enough to quantify tumor burden in daily clinical practice. Our purpose was to evaluate the performance of convolutional neural network (CNN) software in localizing neoplastic lesions in whole-body 18F-FDG PET/CT images of pediatric lymphoma patients. Methods: The retrospective image dataset, derived from the data pool of the International Atomic Energy Agency (coordinated research project E12017), included 102 baseline staging 18F-FDG PET/CT studies of pediatric lymphoma patients (mean age, 11 y). The images were quantified to determine the whole-body tumor burden (whole-body metabolic tumor volume [wbMTV] and whole-body total lesion glycolysis [wbTLG]) using semiautomatic software and CNN-based software. Both were displayed as semiautomatic wbMTV and wbTLG and as CNN wbMTV and wbTLG. The intraclass correlation coefficient (ICC) was applied to evaluate concordance between the CNN-based software and the semiautomatic software. Results: Twenty-six patients were excluded from the analysis because the software was unable to perform calculations for them. In the remaining 76 patients, CNN and semiautomatic wbMTV tumor burden metrics correlated strongly (ICC, 0.993; 95% CI, 0.989 − 0.996; P < 0.0001), as did CNN and semiautomatic wbTLG (ICC, 0.999; 95% CI, 0.998–0.999; P < 0.0001). However, the time spent calculating these metrics was significantly (<0.0001) less by CNN (mean, 19 s; range, 11–50 s) than by the semiautomatic method (mean, 21.6 min; range, 3.2–62.1 min), 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. ER -