Clinical utility of texture analysis of 18F-FDG PET/CT in patients with Stage I lung cancer treated with stereotactic body radiotherapy

نویسندگان

  • Kazuya Takeda
  • Kentaro Takanami
  • Yuko Shirata
  • Takaya Yamamoto
  • Noriyoshi Takahashi
  • Kengo Ito
  • Kei Takase
  • Keiichi Jingu
چکیده

We evaluated the reproducibility and predictive value of texture parameters and existing parameters of 18F-FDG PET/CT images in Stage I non-small-cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT). Twenty-six patients with Stage I NSCLC (T1-2N0M0) were retrospectively analyzed. All of the patients underwent an 18F-FDG PET/CT scan before treatment and were treated with SBRT. Each tumor was delineated using PET Edge (MIM Software Inc., Cleveland, OH), and texture parameters were calculated using open-source code CGITA. From 18F-FDG PET/CT images, three conventional parameters, including maximum standardized uptake value (SUV), metabolic tumor volume (MTV) and total lesion glycolysis (TLG), and four texture parameters, including entropy and dissimilarity (derived from a co-occurrence matrix) and high-intensity large-area emphasis (HILAE) and zone percentage (derived from a size-zone matrix) were analyzed. Reproducibility was evaluated using two independent delineations conducted by two observers. The ability to predict local control (LC), progression-free survival (PFS) and overall survival (OS) was tested for each parameter. All of the seven parameters except zone percentage showed good reproducibility, with intraclass correlation coefficient values >0.8. In univariate analysis, only HILAE was a significant predictor for LC. Histology, dose fractionation, and maximum SUV were associated with PFS, and histology and dose fractionation were associated with OS. We showed that texture parameters derived from 18F-FDG PET/CT were reproducible and potentially beneficial for predicting LC in Stage I lung cancer patients treated with SBRT.

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عنوان ژورنال:

دوره 58  شماره 

صفحات  -

تاریخ انتشار 2017