Figure 6. (A-B) Plots and linear regressions of (A) pathologist-estimated global HS vs. pathologist-estimated MaS and (B) the algorithm-estimated global HS vs. pathologist-estimated MaS. (C-D) ROCs of dual-variable binary predictions on ≥10% steatosis discrepancy between (C) pathologist-estimated global HS and pathologist-estimated MaS and (D) algorithm-estimated global HS and pathologist-estimated MaS. The Raman and the reflectance channels were applied as dual predictors.
Considering the inherently high correlation with global HS (assessed by the expert pathologist and the algorithm) and the pathological assessments are to the nearest 5% steatosis, we set a compromised steatosis discrepancy threshold at 10%. Information from the reflectance channel was paired with the Raman-estimated fat content for dual-variable predictions. Figure 6(C) shows the ROC curves of binary predictions on ≥10% steatosis discrepancy between pathologist-estimated global HS and pathologist-estimated MaS, with an AUROC of 0.66. This could be due to data points’ degradation (overlapping), as the expert pathologist’s estimations were to the nearest 5%.
The ROC curve of binary predictions on ≥10% steatosis discrepancy between the algorithm-estimated global HS (positive pixel area) and pathologist-estimated MaS presented a better AUROC of 0.80, as shown inFigure 6(D) . This may be attributed to the algorithm-based estimation of global HS, which quantified the areas of ORO-stained lipid droplets in liver specimens, yielding more precise percentage readings.