Statistical analysis
In baseline characteristics, continuous variables with normal
distribution were expressed as mean ± standard deviation (SD) or median
if not normally distributed which were using the t-test, and categorical
variables were analyzed using chi-square test. All tests were two-sided;
P<0.05 was considered significant.
Based on the Cox proportional-hazards model, we combined with the
nonlinear regression model which contains a variety of machine learning
to obtain a nonlinear survival model. In the non-linear part of building
RSI and CLI, we used a variety of machine learning methods to compare,
such as Gradient Boosting, Support Vector Machine, Random Forest,
K-Nearest Neighbor, and Neural Network. The best cut-off for dividing
patients into low-risk and high-risk groups was picked using X-tile
plots based on the association with patients’ survival time. The
Kaplan-Meier curve analysis and the log-rank test was used to estimate
the cumulative survival curves of recurrence during the follow-up
period. The prognostic effect of the risk score model was observed by
using the receiver operating characteristic (ROC) curve with area under
curve (AUC) value and Harrell’s concordance index (C-index). All
analyses were conducted using R (version 4.2.2).