Artificial intelligence powered radiomics model for the assessment of colorectal tumor immune microenvironment

Zhou <i>et al</i>'s investigation on the creation of a non-invasive deep learning (DL) method for colorectal tumor immune microenvironment evaluation using preoperative computed tomography (CT) radiomics published in the <i>World Journal of Gastrointestinal Oncology</i> is thorough and scientific. The study analyzed preoperative CT images of 315 confirmed colorectal cancer patients, using manual regions of interest to extract DL features. The study developed a DL model using CT images and histopathological images to predict immune-related indicators in colorectal cancer patients. Pathological (tumor-stroma ratio, tumor-infiltrating lymphocytes infiltration, immunohistochemistry, tumor immune microenvironment and immune score) parameters and radiomics (CT imaging and model construction) data were combined to generate artificial intelligence-powered models. Clinical benefit and goodness of fit of the models were assessed using receiver operating characteristic, area under curve and decision curve analysis. The developed DL-based radiomics prediction model for non-invasive evaluation of tumor markers demonstrated potential for personalized treatment planning and immunotherapy strategies in colorectal cancer patients. The study, involving a small group from a single medical center, lacks inclusion/exclusion criteria and should include clinicopathological features for valuable therapeutic practice insights in colorectal cancer patients.