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Research ArticleBasic Science Investigation

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

Elba Etchebehere, Rebeca Andrade, Mariana Camacho, Mariana Lima, Anita Brink, Juliano Cerci, Helen Nadel, Chandrasekhar Bal, Venkatesh Rangarajan, Thomas Pfluger, Olga Kagna, Omar Alonso, Fatima K. Begum, Kahkashan Bashir Mir, Vincent Peter Magboo, Leon J. Menezes, Diana Paez and Thomas NB Pascual
Journal of Nuclear Medicine Technology September 2022, 50 (3) 256-262; DOI: https://doi.org/10.2967/jnmt.121.262900
Elba Etchebehere
1University of Campinas, Campinas, Brazil;
2Medicina Nuclear de Campinas, Campinas, Brazil;
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Rebeca Andrade
1University of Campinas, Campinas, Brazil;
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Mariana Camacho
2Medicina Nuclear de Campinas, Campinas, Brazil;
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Mariana Lima
1University of Campinas, Campinas, Brazil;
2Medicina Nuclear de Campinas, Campinas, Brazil;
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Anita Brink
3University of Cape Town, Cape Town, South Africa;
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Juliano Cerci
4QUANTA Diagnóstico e Terapia, Curitiba, Brazil;
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Helen Nadel
5University of British Columbia, Vancouver, British Columbia, Canada;
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Chandrasekhar Bal
6All India Institute of Medical Sciences, New Delhi, India;
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Venkatesh Rangarajan
7Tata Memorial Centre, Mumbai, India;
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Thomas Pfluger
8Ludwig‐Maximillian University of Munich, Munich, Germany;
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Olga Kagna
9Rambam Health Care Campus, Haifa, Israel;
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Omar Alonso
10Centro Uruguayo de Imagenología Molecular, Montevideo, Uruguay;
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Fatima K. Begum
11National Institute of Nuclear Medicine and Allied Sciences, Dhaka, Bangladesh;
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Kahkashan Bashir Mir
12Nuclear Medicine, Oncology and Radiotherapy Institute, Islamabad, Pakistan;
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Vincent Peter Magboo
13University of the Philippines, Manila, Philippines;
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Leon J. Menezes
14Institute of Nuclear Medicine, London, United Kingdom; and
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Diana Paez
15Nuclear Medicine and Diagnostic Imaging Section, Division of Human Health, International Atomic Energy Agency, Vienna, Austria
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Thomas NB Pascual
15Nuclear Medicine and Diagnostic Imaging Section, Division of Human Health, International Atomic Energy Agency, Vienna, Austria
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Abstract

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.

  • 18F-FDG PET/CT
  • whole-body tumor burden
  • pediatric
  • lymphoma

Footnotes

  • Published online Apr. 19, 2022.

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Journal of Nuclear Medicine Technology: 50 (3)
Journal of Nuclear Medicine Technology
Vol. 50, Issue 3
September 1, 2022
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Validation of Convolutional Neural Networks for Fast Determination of Whole-Body Metabolic Tumor Burden in Pediatric Lymphoma
Elba Etchebehere, Rebeca Andrade, Mariana Camacho, Mariana Lima, Anita Brink, Juliano Cerci, Helen Nadel, Chandrasekhar Bal, Venkatesh Rangarajan, Thomas Pfluger, Olga Kagna, Omar Alonso, Fatima K. Begum, Kahkashan Bashir Mir, Vincent Peter Magboo, Leon J. Menezes, Diana Paez, Thomas NB Pascual
Journal of Nuclear Medicine Technology Sep 2022, 50 (3) 256-262; DOI: 10.2967/jnmt.121.262900

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Validation of Convolutional Neural Networks for Fast Determination of Whole-Body Metabolic Tumor Burden in Pediatric Lymphoma
Elba Etchebehere, Rebeca Andrade, Mariana Camacho, Mariana Lima, Anita Brink, Juliano Cerci, Helen Nadel, Chandrasekhar Bal, Venkatesh Rangarajan, Thomas Pfluger, Olga Kagna, Omar Alonso, Fatima K. Begum, Kahkashan Bashir Mir, Vincent Peter Magboo, Leon J. Menezes, Diana Paez, Thomas NB Pascual
Journal of Nuclear Medicine Technology Sep 2022, 50 (3) 256-262; DOI: 10.2967/jnmt.121.262900
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Keywords

  • 18F-FDG PET/CT
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