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Table 4 The diagnostic performance in differentiating malignancies from BOT based on various MR-based radiomics models

From: Two-dimensional and three-dimensional T2 weighted imaging-based radiomic signatures for the preoperative discrimination of ovarian borderline tumors and malignant tumors

Model

Group

SEN

SPE

PPV

NPV

ACC

AUC(95% CI)

2d_cor

Training

0.708

0.936

0.919

0.759

0.821

0.90(0.85ā€“0.96)

2d_cor

Testing

0.729

0.851

0.833

0.755

0.789

0.82(0.73ā€“0.90)

3d_cor

Training

0.875

0.717

0.764

0.846

0.798

0.85(0.77ā€“0.93)

3d_cor

Testing

0.936

0.717

0.772

0.917

0.828

0.84(0.76ā€“0.93)

2d_sag

Training

0.776

0.902

0.884

0.807

0.840

0.89(0.83ā€“0.96)

2d_sag

Testing

0.729

0.824

0.795

0.764

0.778

0.79(0.69ā€“0.88)

3d_sag

Training

1.000

1.000

1.000

1.000

1.000

1.0(1.0ā€“1.0)

3d_sag

Testing

1.000

0.980

0.980

1.000

0.990

1.0(1.0ā€“1.0)

  1. SEN sensitivity, SPE specificity, PPV positive predictive value, NPV negative positive value, ACC accuracy, AUC area under the curve, CI confidence interval