4. Discussion
MSI, tumor, colorectal cancer, CRC
REGRESSION, TUMOR, COLORECTAL CANCER
CRC remains a major healthcare burden with a high mortality rate worldwide [In previous studies, [Few studies have built machine learning models based on simple clinicopathological indicators to predict dMMR or MSI status in patients with CRC. Notably, several studies have been reported to use deep learning methods based on pathologically stained images to identify the MMR/MSI status in patients with CRC. Echle et al. [The development of scoring systems has also been effective in predicting the dMMR or MSI status in patients with CRC. Jenkins et al. [Our previously established scoring model incorporated nine clinicopathological features: age, location, tumor diameter, degree of differentiation, number of sample lymph nodes, PNI, number of positive lymph nodes, CA72-4, and CEA. The machine learning model built in this study excluded the CEA feature and included the three features NSE, N staging, and TNM staging, which were the results of a series of feature screenings. Compared with similar studies, we included a relatively large number of features, but the predictive performance of the model did not correlate with the number of indicators included. Of these, tumor location and size contributed most to the model. The comparison revealed that four characteristics—age, tumor location, Crohn's-like reaction, and TIL—were frequently included in the scoring model and had high coefficients. The difference is that although in the current study the tumor location is divided into rectum and colon, it was not additionally classified into proximal, distal, or left and right; nor were the two features Crohn's-like reaction and TIL included.We analyzed the strengths and weaknesses of each model. Support vector machines have the advantage of being able to perform linear and nonlinear classification and regression but struggle to deal effectively with complex and large data. RF and gradient boosting (e.g., XGBoost) have the advantage of being able to understand the importance of each feature for prediction, explain how decisions are made, and are easier to train and tune. The disadvantage is that these models are unsuitable for regression [Current research on the prediction of MMR status in colorectal cancer has two main approaches. One is to build a deep learning model to predict the MMR status by identifying the pathologically stained sections. The second is to build scoring models, filter the final incorporated features of the models through univariate and multivariate analyses, and then predict the MMR status. The novelty of this study is the combination of artificial intelligence methods and simple clinicopathological indicators to predict the status of MMR in patients with CRC.