Dataset partitioning and feature screening
A total of 238 patients were randomly assigned to the 2 datasets with a ratio of 7:3, with 165 people in the training set and 73 people in the test set. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was invented by Robert Tibshirani in 1996 and it had already become a standard method for feature selection in the field of bio-information (Vidyasagar First, we standardized 2048 deep features using Z-score to ease the convergence process. Then, we fed the Lasso–Cox model with the data in the training set and randomly assigned 100 penalty parameters lambda for the training. Last, we utilized the tenfold cross-validation method to determine the standard error of lambda and used it as the optimal penalty parameter to select the optimal deep features. The evaluation index was C-index. In the process of selecting clinical features, we chose each feature individually to establish a Cox model and used AUROC (the area under the receiver operator characteristic curve) for evaluation. The features with a score above 0.6 were viewed as valuable (Fig. Extraction of clinical and pathological characteristics. 