Validation of Convolutional Neural Networks for Fast Determination of Whole-Body Metabolic Tumor Burden in Pediatric Lymphoma.

Autor:	Etchebehere E; University of Campinas, Campinas, Brazil; elba@hc.unicamp.br.; Medicina Nuclear de Campinas, Campinas, Brazil., Andrade R; University of Campinas, Campinas, Brazil., Camacho M; Medicina Nuclear de Campinas, Campinas, Brazil., Lima M; University of Campinas, Campinas, Brazil.; Medicina Nuclear de Campinas, Campinas, Brazil., Brink A; University of Cape Town, Cape Town, South Africa., Cerci J; QUANTA Diagnóstico e Terapia, Curitiba, Brazil., Nadel H; University of British Columbia, Vancouver, British Columbia, Canada., Bal C; All India Institute of Medical Sciences, New Delhi, India., Rangarajan V; Tata Memorial Centre, Mumbai, India., Pfluger T; Ludwig-Maximillian University of Munich, Munich, Germany., Kagna O; Rambam Health Care Campus, Haifa, Israel., Alonso O; Centro Uruguayo de Imagenología Molecular, Montevideo, Uruguay., Begum FK; National Institute of Nuclear Medicine and Allied Sciences, Dhaka, Bangladesh., Mir KB; Nuclear Medicine, Oncology and Radiotherapy Institute, Islamabad, Pakistan., Magboo VP; University of the Philippines, Manila, Philippines., Menezes LJ; Institute of Nuclear Medicine, London, United Kingdom; and., Paez D; Nuclear Medicine and Diagnostic Imaging Section, Division of Human Health, International Atomic Energy Agency, Vienna, Austria., Pascual TN; Nuclear Medicine and Diagnostic Imaging Section, Division of Human Health, International Atomic Energy Agency, Vienna, Austria.
Jazyk:	angličtina
Zdroj:	Journal of nuclear medicine technology [J Nucl Med Technol] 2022 Sep; Vol. 50 (3), pp. 256-262. Date of Electronic Publication: 2022 Apr 19.
DOI:	10.2967/jnmt.121.262900
Abstrakt:	18 F-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 18 F-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 18 F-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. (© 2022 by the Society of Nuclear Medicine and Molecular Imaging.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu