Predicting clinical outcomes using 18F-FDG PET/CT-based radiomic features and machine learning algorithms in patients with esophageal cancer.

Journal: Nuclear medicine communications
Published Date: Jun 4, 2025

Abstract

OBJECTIVE: This study evaluated the relationship between 18F-fluorodeoxyglucose PET/computed tomography (18F-FDG PET/CT) radiomic features and clinical parameters, including tumor localization, histopathological subtype, lymph node metastasis, mortality, and treatment response, in esophageal cancer (EC) patients undergoing chemoradiotherapy and the predictive performance of various machine learning (ML) models.

Authors

  • Gozde Mutevelizade
    Department of Nuclear Medicine.
  • Nazim Aydin
    Department of Nuclear Medicine.
  • Ozge Duran Can
    Department of Radiation Oncology, School of Medicine, Manisa Celal Bayar University, Uncubozkoy.
  • Orkun Teke
    Department of Technical Sciences Vocational School XRLab, Manisa Celal Bayar University, Muradiye, Manisa.
  • Ahmet Furkan Suner
    Republic of Türkiye Ministry of Health, Caycuma District Health Directorate, Cancer Early Diagnosis, Screening and Training Center (KETEM), Caycuma, Zonguldak, Turkey.
  • Merve Erdugan
    Department of Radiation Oncology, School of Medicine, Manisa Celal Bayar University, Uncubozkoy.
  • Elvan Sayit
    Department of Nuclear Medicine.

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