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Weisman AJ, Kim J, Lee I, et al. Automated quantification of baseline imaging PET metrics on FDG PET/CT images of pediatric Hodgkin lymphoma patients. EJNMMI Phys. 2020;7(1):76doi: 10.1186/s40658-020-00346-3.
Weisman, A. J., Kim, J., Lee, I., McCarten, K. M., Kessel, S., Schwartz, C. L., Kelly, K. M., Jeraj, R., Cho, S. Y., & Bradshaw, T. J. (2020). Automated quantification of baseline imaging PET metrics on FDG PET/CT images of pediatric Hodgkin lymphoma patients. EJNMMI physics, 7(1), 76. https://doi.org/10.1186/s40658-020-00346-3
Weisman, Amy J, et al. "Automated quantification of baseline imaging PET metrics on FDG PET/CT images of pediatric Hodgkin lymphoma patients." EJNMMI physics vol. 7,1 (2020): 76. doi: https://doi.org/10.1186/s40658-020-00346-3
Weisman AJ, Kim J, Lee I, McCarten KM, Kessel S, Schwartz CL, Kelly KM, Jeraj R, Cho SY, Bradshaw TJ. Automated quantification of baseline imaging PET metrics on FDG PET/CT images of pediatric Hodgkin lymphoma patients. EJNMMI Phys. 2020 Dec 14;7(1):76. doi: 10.1186/s40658-020-00346-3. PMID: 33315178; PMCID: PMC7736382.
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EJNMMI Phys. 2020 Dec 14;7(1):76. doi: 10.1186/s40658-020-00346-3.

Automated quantification of baseline imaging PET metrics on FDG PET/CT images of pediatric Hodgkin lymphoma patients.

EJNMMI physics

Amy J Weisman, Jihyun Kim, Inki Lee, Kathleen M McCarten, Sandy Kessel, Cindy L Schwartz, Kara M Kelly, Robert Jeraj, Steve Y Cho, Tyler J Bradshaw

Affiliations

  1. Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA.
  2. Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA.
  3. Department of Nuclear Medicine Korea Cancer Centre Hospital, Korea Institute of Radiological and Medical Sciences, Seoul, Korea.
  4. IROC-Rhode Island, Lincoln, RI, USA.
  5. Medical College of Wisconsin, Milwaukee, WI, USA.
  6. Department of Pediatrics, Roswell Park Comprehensive Cancer Center, University at Buffalo Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, USA.
  7. University of Wisconsin Carbone Comprehensive Cancer Center, Madison, WI, USA.
  8. Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA. [email protected].

PMID: 33315178 PMCID: PMC7736382 DOI: 10.1186/s40658-020-00346-3

Abstract

RESULTS: Median Dice similarity coefficient of patient contours between automated and physician contours was 0.86 (IQR 0.78-0.91). Automated SUV

CONCLUSIONS: An automated method using an ensemble of multi-resolution pathway 3D CNNs was able to quantify PET imaging features of lymphoma on baseline FDG PET/CT images with excellent agreement to reference physician PET segmentation. Automated methods with faster throughput for PET quantitation, such as MTV and TLG, show promise in more accessible clinical and research applications.

Keywords: Convolutional neural networks; Imaging biomarkers; PET; Pediatric lymphoma

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