Extended Data Fig. 3: Results from the reader study—lung cancer screening using current and prior CT volume: reweighted. | Nature Medicine

Extended Data Fig. 3: Results from the reader study—lung cancer screening using current and prior CT volume: reweighted.

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

Extended Data Fig. 3

ae, Identical to Fig. 3, except that we took into account the sampling done in the selection of the 15,000 patient 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. Model performance in the AUC curve and summary tables is based on case-level malignancy score. The term LUMAS buckets refers to operating points selected to represent sensitivity/specificity at the 3+, 4A+ and 4B/X thresholds. a, Performance of model (blue line) versus average radiologist at various Lung-RADS categories (crosses) using a CT volume and a prior CT volume for a patient. The length of the crosses represents the 95% confidence interval. The area highlighted in blue is magnified in b to show the performance of each of the six radiologists at various Lung-RADS categories in this reader study. c, Sensitivity comparison between model and average radiologist. d, Specificity comparison between model and average radiologist. Both sensitivity and specificity analysis were conducted with n = 308 volumes from 308 patients with P values computed using a two-sided permutation test with 10,000 random resamplings of the data. e, Hit rate localization analysis used to measure how often the model correctly localized a cancerous lesion.

Back to article page