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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 [18F]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 [18F]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 [18F]FDG-PET/CT radiomic model could predict PFS for patients with esophageal cancer who received dCRT.
Acta Oncologica – Taylor & Francis
Published: Feb 1, 2023
Keywords: PET; radiomics; esophageal cancer; radiation therapy; LASSO cox regression model
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