Development and validation of an [18F]FDG-PET/CT radiomic model for predicting progression-free survival for patients with stage II – III thoracic esophageal squamous cell carcinoma who are treated with definitive chemoradiotherapy. (1st February 2023)

• Record Type: Journal Article
• Authors: Takahashi, Noriyoshi; Tanaka, Shohei; Umezawa, Rei; Takanami, Kentaro; Takeda, Kazuya; Yamamoto, Takaya; Suzuki, Yu; Katsuta, Yoshiyuki; Kadoya, Noriyuki; Jingu, Keiichi
• Is Part Of: Acta oncologica. Volume 62:Number 2(2023)
• Page Start: 159
• Page End: 165
• Publication Date: 2023-02-01
• Subjects: PET -- radiomics -- esophageal cancer -- radiation therapy -- LASSO cox regression model; Oncology -- Periodicals; Cancer -- Treatment -- Periodicals; 616.992
• DOI: 10.1080/0284186X.2023.2178859
• Languages: English
• ISSNs: 0284-186X

Abstract

Abstract: Background: Radiomics is a method for extracting a large amount of information from images and used to predict treatment outcomes, side effects and diagnosis. In this study, we developed and validated a radiomic model of [ 18 F]FDG-PET/CT for predicting progression-free survival (PFS) of definitive chemoradiotherapy (dCRT) for patients with esophageal cancer. Material and Methods: Patients with stage II – III esophageal cancer who underwent [ 18 F]FDG-PET/CT within 45 days before dCRT between 2005 and 2017 were included. Patients were randomly assigned to a training set (85 patients) and a validation set (45 patients). Radiomic parameters inside the area of standard uptake value ≥ 3 were calculated. The open-source software 3D slicer and Pyradiomics were used for segmentation and calculating radiomic parameters, respectively. Eight hundred sixty radiomic parameters and general information were investigated. In the training set, a radiomic model for PFS was made from the LASSO Cox regression model and Rad-score was calculated. In the validation set, the model was applied to Kaplan-Meier curves. The median value of Rad-score in the training set was used as a cutoff value in the validation set. JMP was used for statistical analysis. RStudio was used for the LASSO Cox regression model. p < 0.05 was defined as significant. Results: The median follow-up periods were 21.9 months for all patients and 63.4 months for survivors. The 5-year PFS rate was 24.0%. In the training set, the LASSO Cox regression model selects 6 parameters and made a model. The low Rad-score group had significantly better PFS than that the high Rad-score group ( p = 0.019). In the validation set, the low Rad-score group had significantly better PFS than that the high Rad-score group ( p = 0.040). Conclusions: The [ 18 F]FDG-PET/CT radiomic model could predict PFS for patients with esophageal cancer who received dCRT.