Accuracy of the Vancouver Lung Cancer Risk Prediction Model Compared With That of Radiologists


      Risk models have been developed that include the subject’s pretest risk profile and imaging findings to predict the risk of cancer in an objective way. We assessed the accuracy of the Vancouver Lung Cancer Risk Prediction Model compared with that of trainee and experienced radiologists using a subset of size-matched nodules from the National Lung Screening Trial (NLST).


      One hundred cases from the NLST database were selected (size range, 4-20 mm), including 20 proven cancers and 80 size-matched benign nodules. Three experienced thoracic radiologists and three trainee radiologists were asked to estimate the likelihood of cancer in each case, first independently, and then with knowledge of the model’s risk prediction. The results generated by the model alone also were estimated using receiver operating characteristic (ROC) analysis. The area under the ROC curve (AUC) for each viewing condition was calculated, and statistical significance in their differences was tested by using the Dorfman-Berbaum-Metz method.


      Human observers were more accurate (AUC value of 0.85 ± 0.05 [SD]) than was the model (0.77 ± 0.06) in estimating the risk of malignancy ( P = .0010), and use of the model did not improve their accuracy (0.84 ± 0.06). Experienced radiologists performed better than did trainees. Human observers could distinguish benign from malignant nodule morphology more accurately than could the model, which relies mainly on nodule size for risk estimation.


      Experienced and trainee radiologists had superior ability to predict the risk of cancer in size-matched nodules from a screening trial compared with that of the Vancouver model, and use of the model did not improve their accuracy.

      Key Words


      AUC ( area under the ROC curve), BCCA ( British Columbia Cancer Agency), NLST ( National Lung Screening Trial), PanCan ( Pan-Canadian Early Detection of Lung Cancer Study), ROC ( receiver operating characteristic)
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      Linked Article

      • Response
        CHESTVol. 156Issue 4
        • In Brief
          We thank Drs Lam, Mayo, and Tammemagi for their comments on our article.1 We do not disagree with their assertion that the Vancouver model may play a useful role in determining the need for CT follow-up imaging of small nodules in the context of a screening program, or that future incorporation of radiomic features and artificial intelligence could improve the model’s accuracy. As noted in our article, it was necessary to use an enriched population sample with a relatively high proportion of cancers to perform a meaningful observer test, and we adjusted the model’s output to take this approach into account.
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      • Human Observer vs Prediction Model for Lung Nodule Malignancy Risk Estimation
        CHESTVol. 156Issue 4
        • In Brief
          We read with interest the paper by MacMahon et al1 in an issue of CHEST (July 2019) comparing the accuracy of experienced thoracic radiologists and trainees vs the PanCan (Vancouver) Risk Prediction model2 to estimate the likelihood of malignancy of lung nodules found by using screening CT imaging.
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