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December 2, 2025EJNMMI Research3 citationsOpen Access

Global and regional accuracy of deep learning-based tumor segmentation from whole-body 18Ffluorodeoxyglucose PET/CT images

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DMDonatella Masciale

Key Points

  • Global disease burden assessment improved with predictive modeling for tumor segmentation accuracy.
  • Segmentation using deep learning achieved significant relationships with TBR metrics for hypermetabolic lesions.
  • Observational analysis across imaging modalities suggests enhanced accuracy for whole-body FDG-PET imaging.
  • Integrating metabolic profiles may optimize deep learning techniques for detecting lesions more effectively.

Abstract

These results are consistent with previous reports on PET-based segmentation, further validating nnU-Net as a reliable approach for detecting hypermetabolic lesions and assessing global disease burden in FDG-PET imaging. Moreover, the significant relationship between TBR and segmentation accuracy suggests the possibility of further improvements by integrating metabolic profile into the predictive model.

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Cite This Study

Donatella Masciale (2025) studied this question.

synapsesocial.com/papers/692e3d8d6c9b3ab28c187860https://doi.org/10.1186/s13550-025-01333-4
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