Extended Data Fig. 4: Results from the full NLST test set and independent test set: reweighted. | Nature Medicine

Extended Data Fig. 4: Results from the full NLST test set and independent test set: reweighted.

From: End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

Extended Data Fig. 4

a,b, Identical to Fig. 4 except that we took into account the biased sampling done in the selection of the NLST data released. This meant that for screening groups 3 (no nodule, some abnormality) and 4 (no nodule, no abnormality) we upweighted each example by the same factor by which they were downsampled. The comparison was performed on n = 6,716 cases, using a two-sided permutation test with 10,000 random resamplings of the data. a, Comparison of model performance to NLST reader performance on the full NLST test set. NLST reader performance was estimated by retrospectively applying Lung-RADS 3 criteria to the NLST reads. b, Sensitivity and specificity comparisons between the model and Lung-RADS retrospectively applied to NLST reads.

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