Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study.

Journal: Journal of applied clinical medical physics
PMID: 38436611

Abstract

PURPOSE: Predicting recurrence following stereotactic body radiotherapy (SBRT) for non-small cell lung cancer provides important information for the feasibility of the individualized radiotherapy and allows to select the appropriate treatment strategy based on the risk of recurrence. In this study, we evaluated the performance of both machine learning models using positron emission tomography (PET) and computed tomography (CT) radiomic features for predicting recurrence after SBRT.

Authors

  • Hikaru Nemoto
    Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendai, Japan.
  • Masahide Saito
    Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan.
  • Yoko Satoh
  • Takafumi Komiyama
    Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan.
  • Kan Marino
    Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan.
  • Shinichi Aoki
    Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan.
  • Hidekazu Suzuki
    Department of Gastroenterology, Tokai University School of Medicine, Isehara, Kanagawa, Japan.
  • Naoki Sano
    Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan.
  • Hotaka Nonaka
    Department of Radiology, Fuji City General Hospital, Fuji, Shizuoka, Japan.
  • Hiroaki Watanabe
    Department of Radiology, Yamanashi Central Hospital, Kofu, Yamanashi, Japan.
  • Satoshi Funayama
    Department of Radiology, Faculty of Medicine, University of Yamanashi, Chuo-city, Yamanashi 409-3898, Japan.
  • Hiroshi Onishi
    Department of Radiology, University of Yamanashi, Yamanashi, Japan.

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