Fig. 2: Performance of AI in the prediction and quantification of growth patterns.
a, Segmentation example generated by ANORAK. b, Correlations of growth pattern proportions at the tumor level between AI and pathologists. Growth pattern proportions were not available in the DHMC cohort; thus, plots relevant to proportions were not illustrated for the DHMC (same in d and e). P values were corrected for multiple comparisons using the Benjamini–Hochberg method. c, Performance comparison with pathologists in predicting the predominant pattern per case (the cribriform predominant slide per tumor was not available in the DHMC cohort). d, Growth pattern intratumoral heterogeneity substantially contributed to the discrepancy between AI and pathologists (TRACERx 421, P = 8.467 × 10−7, n = 206; LATTICe-A, P1 < 2.22 × 10−16, P2 = 2.816 × 10−12, P3 < 2.22 × 10−16, n = 845; TCGA, P = 0.0007632, n = 177). Each P value was calculated using a two-sided Wilcoxon rank-sum test and not adjusted for multiple comparisons. The median value is indicated by a thick horizontal line; the first and third quartiles are represented by the box edges; the whiskers indicate 1.5× the interquartile range. e, Performance comparison with pathologists in the prediction of IASLC grading per case.
