Construction and validation of prediction model for pCR rate

The included datasets were randomly in a 7:3 ratio divided into training cohort and validation cohort. Subsequently, the predictive model was constructed in training cohort with the combination of pRS and the clinicopathological factors through logistic regression analysis, and the characteristics with independent predictive values for pCR rate were adopted for nomogram using R package rms (version 6.0-0). Receiver operating characteristic (ROC) curves with the calculated area under the curve (AUC), using R package pROC (version 1.16.2) were utilized to validate the discriminative power of this model, while calibration plot was adopted for calibrating capability.

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