A CT-based radiomics signature for evaluating tumor infiltrating Treg cells and outcome prediction of gastric cancer

BACKGROUND: Tumor infiltrating regulatory T (TITreg) cells are highly infiltrated in gastric cancer (GC) and associated with worse prognosis of GC patients. We aim to develop and validate a radiomics signature for evaluation of TITreg cells and outcome prediction of GC patients. METHODS: A total of 165 GC patients from three independent cohorts were enrolled in this retrospective study. The abundance of TITreg cells were evaluated by using multispectral immunohistochemical analysis and CIBERSORT algorithm. The radiomics features were extracted by using PyRadiomics software and the radiomics signature was generated by using the least absolute shrinkage and selection operator (LASSO) logistic regression model. The receiver operator characteristic (ROC) curves were applied to assess the performance of radiomics signature for estimating TITreg cells. Univariable and multivariable Cox regression analysis were used for identifying risk factor of overall survival (OS). The prognostic value of the radiomics signature and the TITreg cells were evaluated by using the Kaplan-Meier method and log-rank test. RESULTS: Six robust features were selected for building the radiomics signature. The radiomics signature showed good ability for estimating TITreg in the training, validation and testing cohort, with area under the curve (AUC) of 0.884, 0.869 and 0.847, respectively. Multivariable Cox regression analysis showed that the radiomics signature was an independent risk factor of unfavorable OS of GC patients. CONCLUSIONS: The proposed CT-based radiomics signature is a promising non-invasive biomarker of TITreg cells and outcome prediction of GC patients.