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Table 3 Diagnosis performance of CNN models and different thoracic surgeons in the validation cohort

From: A deep learning model combining circulating tumor cells and radiological features in the multi-classification of mediastinal lesions in comparison with thoracic surgeons: a large-scale retrospective study

Ratings

AUC (95%CI)

Sensitivity (95% CI)

Specificity (95% CI)

Accuracy (95% CI)

PPV (95% CI)

NPV (95% CI)

Benign classification

CNN model

 Monomodal CNN model

0.710(0.664–0.756)

0.702(0.676–0.728)

0.738(0.716–0.762)

0.779(0.743–0.815)

0.692(0.671–0.713)

0.723(0.695–0.751)

 DMFN model

0.941(0.901–0.982)

0.809(0.785–0.833)

0.929(0.894–0.964)

0.927(0.910–0.944)

0.885(0.837–0.932)

0.939(0.884–0.993)

Thoracic surgeons

 Resident physicians

0.52(0.475–0.566)

0.513(0.502–0.524)

0.558(0.515–0.606)

0.69(0.666–0.723)

0.573(0.527–0.619)

0.538(0.818–0.561)

 Attending physicians

0.688(0.649–0.714)

0.607(0.546–0.677)

0.72(0.682–0.763)

0.737(0.696–0.784)

0.693(0.635–0.754)

0.704(0.672–0.734)

 Chief physicians

0.802(0.742–0.863)

0.761(0.720–0.803)

0.813(0.760–0.866)

0.830(0.805–0.856)

0.822(0.784–0.861)

0.811(0.752–0.872)

Management decision

 Resident physicians

0.636(0.568–0.711)

0.627(0.578–0.664)

0.684(0.657–0.707)

0.711(0.685–0.734)

0.643(0.626–0.630)

0.661(0.624–0.707)

 Attending physicians

0.739(0.709–0.772)

0.705(0.685–0.733)

0.768(0.631–0.804)

0.839(0.801–0.870)

0.723(0.694–0.758)

0.718(0.689–0.742)

 Chief physicians

0.921(0.882–0.946)

0.916(0.885–0.946)

0.928(0.895–0.953)

0.907(0.883–0.935)

0.919(0.881–0.942)

0.882(0.854–0.908)

  1. AUC Area under the ROC curve, 95%CI 95% confidence interval, CNN Convolutional neural network, DMFN Deep multimodal fusion network, PPV Positive predictive value, NPV Negative predictive value