5. Conclusions
tumor, CRC
REGRESSION, TUMOR
In this study, we built four sets of machine learning models and a conventional logistic regression model to predict the lack of DNA MMR in patients with CRC based on simple clinicopathological indicators. Our results show that machine learning models can be incorporated with accurate and consistent predictive behavior. In fact, machine learning models show better performance at identifying dMMR than the conventional logistic regression methods. To the best of our knowledge, this study is the first to propose a machine learning approach to analyze and model the MMR status of patients based on simple clinicopathology and tumor markers. In addition, our single-center sample was sufficiently large to draw conclusions with some reference values. In future studies, we aim to incorporate TIL and Crohn's-like response features into the prediction model, refine some of the features such as the location of the tumor, and add an external validation group to improve the predictive power of the model.