帮我用R写一个COX模型构建的代码，以及写一个基于COX模型的nomogram代码，其中，数据集的名称是df,用来构建COX模型的特征名称是'original:firstorder:RobustMeanAbsoluteDeviation', 'log-sigma-4-0-mm-3D:firstorder:InterquartileRange', 'log-sigma-5-0-mm-3D:glszm:SmallAreaEmphasis', 'wavelet-LLH:firstorder:InterquartileRange', 'wavelet-LLH:firstorder:Uniformity', 'wavelet-HHL:firstorder:Mean', 'wavelet-HHH:firstorder:Mean, 'wavelet-LLL:glcm:Autocorrelation', 'wavelet-LLL:glcm:ClusterProminence','T','E']

好的，以下是COX模型构建的代码：

library(survival)

# 基于数据集df构建COX模型
cox_model <- coxph(Surv(T, E) ~ original:firstorder:RobustMeanAbsoluteDeviation + 
                        log-sigma-4-0-mm-3D:firstorder:InterquartileRange + 
                        log-sigma-5-0-mm-3D:glszm:SmallAreaEmphasis + 
                        wavelet-LLH:firstorder:InterquartileRange + 
                        wavelet-LLH:firstorder:Uniformity + 
                        wavelet-HHL:firstorder:Mean + 
                        wavelet-HHH:firstorder:Mean + 
                        wavelet-LLL:glcm:Autocorrelation + 
                        wavelet-LLL:glcm:ClusterProminence, data = df)

# 输出COX模型的摘要信息
summary(cox_model)

接下来是基于COX模型的nomogram代码：

library(rms)

# 构建基于COX模型的nomogram
nomogram_model <- nomogram(cox_model, fun = function(x) 1 - plogis(x), 
                           funlabel = "Survival Probability",
                           predictor = TRUE, lp = FALSE, 
                           xfrac = 0.5, yfrac = 0.5, 
                           cex.axis = 0.8, cex.lab = 0.8, 
                           col.lines = "gray70", 
                           col.points = "gray30", 
                           lwd.lines = 1.5, 
                           lwd.points = 1.5, 
                           font.main = 1, font.sub = 1, 
                           font.axis = 1, font.lab = 1)

# 输出nomogram
print(nomogram_model)

希望对你有所帮助！