T stage prediction of colorectal tumor based on multiparametric functional images

BACKGROUND: Recent studies have shown radiomics parameters of functional imaging have predictive values in many diseases. This study was to investigate the value of radiomics parameters of both computed tomography (CT) and magnetic resonance imaging (MRI) in predicting T stage of colorectal cancer (CRC). METHODS: Imaging findings of CT and MRI (both cT1-W and T2-W) and clinical information were collected from 29 patients. A total of 330 radiomics parameters were computed from manually annotated medical images, and a lasso regression model with 10-fold cross validation was employed to predict the T stage with radiomics parameters. RESULTS: The lasso regression model showed good performance with area under the curve (AUC) of 0.85. A total of three parameters from MRI were used in this model, while no CT findings were included in this model. The 3 selected parameters were from first-order parameters’ group, which include energy and totalenergy from both cT1-W and T2-W. These parameters indicate the magnitude of pixels in the medical images. CONCLUSIONS: This study indicates that some radiomics parameters of functional images have predictive values in T staging of CRC. Also, MRI may be more valuable than CT based on the image findings with lasso regression.