Fig. 4

The heatmap, calibration curves and DCA of the six models. a Heatmap comparison of the clinicopathologic, conventional MRI, DCE-MRI radiomics, combine, traditional, and mixed models. b Calibration curves for the clinicopathologic, conventional MRI, DCE-MRI radiomics, combine, traditional, and mixed models based on the LR algorithm. It is the curve with the model-predicted probability of DCISMI as the X-axis and the actual rate acquired by the bootstrapping method as the Y-axis. The degree of agreement between the depicted calibration curve and the 45° straight line reflects the predictive performance of each model. c The DCA for the clinicopathologic, conventional MRI, DCE-MRI radiomics, combine, traditional, and mixed models based on the LR algorithm. The Y-axis represents the net benefit. DCA showed that in six models within reasonable threshold probabilities, the mixed model showed the greatest overall net benefit for upstage and the second was the combine model. The DCE-MRI radiomics model, which showed all but the same net benefit as the traditional model, showed better than the conventional MRI model. The combine model added more net benefit than the traditional model at the range of 0.4 ~ 1.0. The clinicopathologic model added more net benefit than the conventional MRI model and DCE-MRI radiomics model from 0.65 to 1.0 and from 0.7 to 1.0, respectively. Abbreviations: DCA, decision curve analysis; MRI, magnetic resonance imaging; DCE-MRI: dynamic contrast enhanced MRI; LR, logistic regression