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Deep learning accurately segments and classifies myocardial uptake patterns on fluorine 18 fluorodeoxyglucose (FDG) PET scans, aiding in cancer diagnosis and heart assessment.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Cardiology

Background:

  • Fluorine 18 fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) is crucial for oncologic imaging.
  • Assessing myocardial uptake patterns on FDG PET is important for patient management.
  • Automated analysis of FDG PET images can improve efficiency and accuracy.

Purpose of the Study:

  • To develop and validate a deep learning model for automated myocardial segmentation.
  • To classify myocardial uptake patterns (no, diffuse, partial) on whole-body FDG PET images.
  • To compare deep learning performance against human expert classification.

Main Methods:

  • Retrospective analysis of 609 patients undergoing FDG PET for oncologic indications.
  • Development of two sequential neural networks for left ventricle (LV) segmentation and uptake classification.
  • Training and validation using expert-segmented and classified data; testing on a separate dataset.
  • Receiver operating characteristic (ROC) analysis to evaluate classification performance.

Main Results:

  • Moderate agreement between deep learning and expert classification for uptake patterns (71-78% accuracy).
  • High area under the curve (>0.90) for classifying no and diffuse uptake, lower for partial uptake (0.77).
  • Good correlation (R² = 0.35) for LV volume, with underestimation in the 'no uptake' category.

Conclusions:

  • Deep learning effectively segments the left ventricle and classifies myocardial uptake patterns on FDG PET.
  • The developed model shows potential for computer-aided diagnosis in oncology and cardiology.
  • Feasibility of a myocardial uptake index (MUI) for quantifying myocardial activity patterns was demonstrated.