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Table 2 Performance of models for predicting discrimination between stages I-II and stage III gastric cancer in training and validation cohorts

From: Development and validation of a Radiopathomics model based on CT scans and whole slide images for discriminating between Stage I-II and Stage III gastric cancer

Model

Task

AUC (95% CI)

Accuracy

Sensitivity

Specificity

PPV

NPV

Precision

Recall

F1 score

LR_Pathomics

Training

0.892 (0.841–0.943)

0.837

0.871

0.803

0.813

0.864

0.813

0.871

0.841

 

Validation

0.680 (0.536–0.825)

0.721

0.886

0.500

0.705

0.765

0.705

0.886

0.785

NaiveBayes_Pathomics

Training

0.809 (0.737–0.881)

0.766

0.914

0.620

0.703

0.880

0.703

0.914

0.795

 

Validation

0.679 (0.540–0.818)

0.689

0.771

0.600

0.711

0.652

0.711

0.771

0.740

SVM_Pathomics

Training

0.949 (0.911–0.987)

0.894

0.914

0.873

0.877

0.912

0.877

0.914

0.895

 

Validation

0.777 (0.652–0.901)

0.787

0.886

0.654

0.775

0.810

0.775

0.886

0.827

LR_Radiomics

Training

0.742 (0.663–0.824)

0.688

0.857

0.521

0.638

0.787

0.638

0.857

0.732

 

Validation

0.720 (0.588–0.852)

0.705

0.743

0.654

0.743

0.654

0.743

0.743

0.466

NaiveBayes_Radiomics

Training

0.752 (0.673–0.831)

0.695

0.843

0.549

0.648

0.780

0.648

0.843

0.733

 

Validation

0.733 (0.607–0.859)

0.705

0.714

0.692

0.758

0.643

0.758

0.714

0.735

SVM_Radiomics

Training

0.799 (0.726–0.873)

0.745

0.800

0.690

0.718

0.778

0.718

0.800

0.757

 

Validation

0.712 (0.579–0.845)

0.705

0.771

0.615

0.730

0.667

0.730

0.771

0.750

LR_Radiopathomics

Training

0.904 (0.858–0.951)

0.830

0.914

0.746

0.780

0.898

0.780

0.914

0.842

 

Validation

0.747 (0.617–0.878)

0.770

0.829

0.692

0.784

0.750

0.784

0.829

0.806

NaiveBayes_Radiopathomics

Training

0.861 (0.801–0.921)

0.787

0.857

0.718

0.750

0.836

0.750

0.857

0.800

 

Validation

0.748 (0.624–0.872)

0.738

0.714

0.769

0.806

0.667

0.806

0.714

0.758

SVM_Radiopathomics

Training

0.953 (0.920–0.986)

0.901

0.914

0.887

0.889

0.913

0.889

0.914

0.901

 

Validation

0.851 (0.745–0.956)

0.787

0.771

0.808

0.844

0.724

0.844

0.771

0.806

  1. LR logistic regression, SVM support vector machine, AUC area under the curve, PPV positive prediction value, NPV negative prediction value