Skip to main content

Table 3 The characteristic of differernt ROC curves of model PI-RADS v2 and model 1 to 13 in diagnosing PCa and CS PCa

From: Optimizing prostate cancer accumulating model: combined PI-RADS v2 with prostate specific antigen and its derivative data

Test Result Variable(s)

Area

Asymptotic 95% Confidence Interval

sensitivity

specificity

PPV

NPV

Lower Bound

Upper Bound

 

Pca

CS PCa

PCa

CS PCa

PCa

CS PCa

Pca

CS PCa

PCa

CS PCa

PCa

CS PCa

PCa

CS PCa

PIRADS V2

0.821

0.86

0.777

0.82

0.866

0.901

0.758

0.834

0.828

0.83

0.822

0.805

0.766

0.856

model 1

0.891

0.922

0.858

0.894

0.925

0.95

0.716

0.791

0.891

0.892

0.873

0.86

0.749

0.836

model 2

0.849

0.874

0.81

0.839

0.887

0.909

0.705

0.767

0.793

0.794

0.782

0.758

0.719

0.802

model 3

0.847

0.889

0.807

0.854

0.887

0.923

0.814

0.877

0.736

0.732

0.764

0.733

0.79

0.877

model 4

0.891

0.916

0.858

0.887

0.924

0.945

0.918

0.914

0.649

0.778

0.734

0.776

0.883

0.915

model 5

0.877

0.91

0.841

0.88

0.912

0.94

0.847

0.773

0.753

0.881

0.783

0.846

0.824

0.822

model 6

0.884

0.913

0.849

0.882

0.918

0.943

0.814

0.877

0.845

0.83

0.847

0.813

0.812

0.89

model 7

0.892

0.922

0.859

0.894

0.925

0.95

0.836

0.896

0.799

0.784

0.814

0.777

0.822

0.899

model 8

0.896

0.924

0.863

0.896

0.929

0.951

0.842

0.89

0.822

0.84

0.832

0.824

0.831

0.901

model 9

0.886

0.918

0.852

0.888

0.92

0.947

0.842

0.877

0.78

0.804

0.798

0.79

0.823

0.886

model 10

0.892

0.921

0.858

0.893

0.925

0.95

0.836

0.865

0.821

0.856

0.832

0.834

0.847

0.896

model 11

0.898

0.926

0.866

0.899

0.931

0.953

0.88

0.933

0.799

0.773

0.821

0.776

0.863

0.932

model 12

0.889

0.922

0.855

0.894

0.923

0.95

0.847

0.828

0.782

0.866

0.803

0.839

0.829

0.857

model 13

0.889

0.925

0.862

0.898

0.928

0.953

0.847

0.908

0.845

0.825

0.852

0.813

0.84

0.813