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. 2023 May 31;136(14):1699–1707. doi: 10.1097/CM9.0000000000002674

Figure 1.

Figure 1

Selection of factors associated with OS using the LASSO Cox regression model. (A) The upper Abscissa is the number of non-zero coefficients in this model, and the ordinate is the coefficient value. LASSO coefficients of 25 candidate variables (age, laterality, different nationalities, tumor size, lymph node metastasis, neoadjuvant therapy, surgery method, grade, molecular subtype, ER, PR, HER2, Ki67, chemotherapy, target, endocrine, radiation, liver metastasis, lung metastasis, bone metastasis, vascular invasion, nerve invasion, and marital, menstrual, and vital status), including dummy variables in the training cohort. (B) The optimal penalization coefficient (λ) in the LASSO model was identified by 10-fold cross-validation and the minimum criterion in the training cohort. The left vertical dotted line represents the minimum error, and the right line represents the cross-validated error within one standard error of the minimum. The upper Abscissa indicates the number of independent variables that still exist in the model. ER: Estrogen receptor; HER2: Human epidermal growth factor receptor-2; LASSO: Least absolute shrinkage and selection operator; OS: Overall survival; PR: Progesterone receptor.