Figure 2. Radiomics Features Selection, Validation, and Clinical Utility of the Proposed Radiomics Signature.

A and B, Feature selection using the least absolute shrinkage and selection operator binary logistic regression model. This method minimizes the sum of residues’ squares, with the sum of the absolute values of the selected features coefficients being less than a tuning parameter (λ). A, The area under the receiver operating characteristic curve (AUC) was plotted vs log(λ). The vertical blue line represents the chosen parameter. B, Each colored line represents the coefficient of each feature. A vertical blue line was drawn at the λ selected, where 20 features had nonzero coefficients. C and D, Receiver operating characteristic curves of the radiomics signature in the training cohort (C) and validation cohort (D). E and F, Plots depicting the calibration of the radiomics signature in terms of agreement between predicted and observed respondents in the training cohort (E) and validation cohort (F). G, Decision curves for predicting potential respondents for neoadjuvant chemotherapy. The orange line represents the assumption that all patients will respond to neoadjuvant treatment, and the horizontal blue line represents the assumption that no patient will respond to treatment.