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

Hikaru Nemoto, Masahide Saito, Yoko Satoh, Takafumi Komiyama, Kan Marino, Shinichi Aoki, Hidekazu Suzuki, Naoki Sano, Hotaka Nonaka, Hiroaki Watanabe, Satoshi Funayama, Hiroshi Onishi

研究成果: ジャーナルへの寄稿学術論文査読

1 被引用数 (Scopus)

抄録

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. Methods: Planning CT and PET images of 82 non-small cell lung cancer patients who performed SBRT at our hospital were used. First, tumors were delineated on each CT and PET of each patient, and 111 unique radiomic features were extracted, respectively. Next, the 10 features were selected using three different feature selection algorithms, respectively. Recurrence prediction models based on the selected features and four different machine learning algorithms were developed, respectively. Finally, we compared the predictive performance of each model for each recurrence pattern using the mean area under the curve (AUC) calculated following the 0.632+ bootstrap method. Results: The highest performance for local recurrence, regional lymph node metastasis, and distant metastasis were observed in models using Support vector machine with PET features (mean AUC = 0.646), Naive Bayes with PET features (mean AUC = 0.611), and Support vector machine with CT features (mean AUC = 0.645), respectively. Conclusions: We comprehensively evaluated the performance of prediction model developed for recurrence following SBRT. The model in this study would provide information to predict the recurrence pattern and assist in making treatment strategies.

本文言語英語
論文番号e14322
ジャーナルJournal of applied clinical medical physics
25
7
DOI
出版ステータス出版済み - 07-2024
外部発表はい

All Science Journal Classification (ASJC) codes

  • 放射線
  • 器械工学
  • 放射線学、核医学およびイメージング

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