A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods

Objective This study evaluates the ability of several machine learning (ML) algorithms, developed using volumetric and texture data extracted from baseline 18 F-FDG PET/CT studies performed initial staging of patient with esophageal cancer (EC), to predict survival and histopathology. Methods The initial staging 18 F-FDG PET/CT images obtained on newly diagnosed EC patients between January 2008 and June 2019 were evaluated using LIFEx software. A region of interest (ROI) of the primary tumor was created and volumetric and textural features were obtained. A significant relationship between these features and pathological subtypes, 1-year, and 5-year survival was investigated. Due to the nonhomogeneity of the data, nonparametric test (The Mann–Whitney U test) was used for each feature, in pairwise comparisons of independent variables. A p value of