LASSO regression and variable selection
tumor
REGRESSION, TUMOR
We have applied LASSO regression to reduce 11 characteristics of CC patients (nonmetastatic) in the training cohort. Initially, we excluded two variables (race [line 3] and tumor differentiation [line 4]) whose coefficients were zero in the regression model. Afterward, the remaining nine variables were entered into the LASSO regression model and were subsequently reduced to seven factors which are age at diagnosis (line 2, coefficient, 0.798), marital status (line 1, coefficient, 0.344), histology (line 5, coefficient, 0.244), 2014 FIGO stage (line 6, coefficient, 0.830), surgery at the primary site (line 7, coefficient, −0.771), RT (line 9, coefficient, 0.584), and CT (line 10, coefficient, 0.093) (Figure The results of the LASSO regression. LASSO, least absolute shrinkage and selection operator.