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. 2020 Sep 2;93(1115):20200287. doi: 10.1259/bjr.20200287

Figure 1.

Figure 1.

The radiomics feature selection using the least absolute shrinkage and selection operator (LASSO) regression model. (a) Selection of tuning parameter (λ) for NAC sensitivity in the LASSO model via a ten-fold cross-validation based on a minimizing criterion. The vertical black lines define the optimal values of λ; a λ value of 0.067 with log (λ)= - 4.999 was selected. (b) LASSO coefficient profiles for texture features. The vertical line is plotted with 20 radiomics features versus the selected log(λ) value via a ten-fold cross-validation. (c) Selection of the tuning parameter (λ) for the probability of achieving pCR in the LASSO model via a ten-fold cross-validation based on minimizing criteria. The vertical black lines define the optimal value of λ, a value λ of 0.021 with log (λ)= - 3.880 was selected. (d) LASSO coefficient profiles of texture features. The vertical line is plotted with 17 radiomics features versus the selected log(λ) value via a ten-fold cross-validation.