Figure
3 LASSO regression plot. (A) Plot of partial likelihood deviance of
CSS, (B) plot of LASSO coefficient profiles of CSS, Each curve
represents the LASSO coefficient profile of a feature against the log
(lambda) sequence. when the optimal lambda value was 0.02, retentiong
variables were screen; (C) Plot of partial likelihood deviance of OS,
(D) plot of LASSO coefficient profiles of OS, Each curve represents
the LASSO coefficient profile of a feature against the log (lambda)
sequence, when the optimal lambda value was 0.04, retentiong variables
were screen.
Based on multivariate analysis results and LASSO regression analysis,
eight prognostic factors including marital status, grade, T stage, N
stage, CEA, tumor size, surgery and chemotherapy were used to
establish the nomogram of CSS (Figure 4A), And three prognostic
factors including grade, chemotherapy and months from diagnosis to
treatment were used to establish the nomogram of OS (Figure 4B), These
nomograms were adopted to predict the 1-, 3-, and 5-year survival
rates of training cohort. In a nomogram, the score of each value level
of each variable was assigned according to its contribution to the
outcome; then, each score was added to obtain the total score;
finally, the predicted value of each individual outcome event was
calculated through the function conversion relation between the total
score and the survival probability.