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

Zhou '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 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.