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Predicting standardized uptake value of brown adipose tissue from CT scans using convolutional neural networks

Author

Listed:
  • Ertunc Erdil

    (ETH Zurich)

  • Anton S. Becker

    (ETH Zurich
    Memorial Sloan Kettering Cancer Center
    University Hospital Zurich
    NYU Grossman School of Medicine)

  • Moritz Schwyzer

    (University Hospital Zurich)

  • Borja Martinez-Tellez

    (University of Almería
    Instituto de Salud Carlos III
    Leiden University Medical Center)

  • Jonatan R. Ruiz

    (Sport and Health University Research Institute (iMUDS), University of Granada
    Ibs.Granada
    Instituto de Salud Carlos III)

  • Thomas Sartoretti

    (University Hospital Zurich)

  • H. Alberto Vargas

    (Memorial Sloan Kettering Cancer Center)

  • A. Irene Burger

    (University Zurich Hospital
    University of Zurich)

  • Alin Chirindel

    (University Hospital of Basel)

  • Damian Wild

    (University Hospital of Basel)

  • Nicola Zamboni

    (ETH Zürich)

  • Bart Deplancke

    (Ecole Polytechnique Fédérale de Lausanne (EPFL)
    Swiss Institute of Bioinformatics)

  • Vincent Gardeux

    (Ecole Polytechnique Fédérale de Lausanne (EPFL)
    Swiss Institute of Bioinformatics)

  • Claudia Irene Maushart

    (University Hospital Basel and University of Basel)

  • Matthias Johannes Betz

    (University Hospital Basel and University of Basel)

  • Christian Wolfrum

    (ETH Zurich)

  • Ender Konukoglu

    (ETH Zurich
    The LOOP Zürich - Medical Research Center)

Abstract

The standard method for identifying active Brown Adipose Tissue (BAT) is [18F]-Fluorodeoxyglucose ([18F]-FDG) PET/CT imaging, which is costly and exposes patients to radiation, making it impractical for population studies. These issues can be addressed with computational methods that predict [18F]-FDG uptake by BAT from CT; earlier population studies pave the way for developing such methods by showing some correlation between the Hounsfield Unit (HU) of BAT in CT and the corresponding [18F]-FDG uptake in PET. In this study, we propose training convolutional neural networks (CNNs) to predict [18F]-FDG uptake by BAT from unenhanced CT scans in the restricted regions that are likely to contain BAT. Using the Attention U-Net architecture, we perform experiments on datasets from four different cohorts, the largest study to date. We segment BAT regions using predicted [18F]-FDG uptake values, achieving 23% to 40% better accuracy than conventional CT thresholding. Additionally, BAT volumes computed from the segmentations distinguish the subjects with and without active BAT with an AUC of 0.8, compared to 0.6 for CT thresholding. These findings suggest CNNs can facilitate large-scale imaging studies more efficiently and cost-effectively using only CT.

Suggested Citation

  • Ertunc Erdil & Anton S. Becker & Moritz Schwyzer & Borja Martinez-Tellez & Jonatan R. Ruiz & Thomas Sartoretti & H. Alberto Vargas & A. Irene Burger & Alin Chirindel & Damian Wild & Nicola Zamboni & B, 2024. "Predicting standardized uptake value of brown adipose tissue from CT scans using convolutional neural networks," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-52622-w
    DOI: 10.1038/s41467-024-52622-w
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    References listed on IDEAS

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