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Table 3 Results of clinical model and combined models for pCR prediction

From: Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity

 

Training AUC

p value

Testing AUC

p value

Accuracy

Sensitivity

Specificity

CRD model#

0.769

(0.722–0.816)

 

0.762

(0.679–0.845)

 

0.736

0.578

0.812

CD model

0.754

(0.705–0.802)

< 0.001*

0.755

(0.672–0.839)

0.112

0.660

0.734

0.624

CR model

0.716

(0.665–0.767)

< 0.001*

0.695

(0.656–0.714)

0.005*

0.695

0.656

0.714

RD model

0.709

(0.658–0.761)

< 0.001*

0.693

(0.602–0.784)

< 0.001*

0.629

0.594

0.647

Clinical model

0.642

(0.586–0.697)

< 0.001*

0.691

(0.600-0.782)

< 0.001*

0.619

0.828

0.519

  1. P values were obtained by likelihood ratio test
  2. * indicate statistically significant # indicate reference model for comparison
  3. Accuracy, sensitivity, and specificity were obtained in testing set
  4. CRD model: Clinical-Radiomic-Dynamic model; CD model: Clinical-Dynamic model; CR model: Clinical-Radiomic model; RD model: Radiomic-Dynamic model