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. 2019 Aug 19;11:7825–7834. doi: 10.2147/CMAR.S217887

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© 2019 Yang et al.

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(A and B) The least absolute shrinkage and selection operator (LASSO) binary logistic regression model for feature selection. The features retained in the previous step were introduced into the LASSO regression model. First, a 10-fold cross-validation method was used to screen the LASSO regression model hyperparameter (λ) and select the model with the smallest error (λ). The retention (not equal to 0) was used to calculate the rad-score, which represents the sum of the product of the feature and the corresponding coefficient. Receiver operating characteristic analysis was used to discriminate the ability of the rad-score to identify invasive and non-invasive adenocarcinoma in the training and validation sets.