Figure 5. Boxplots of Raman-estimated fat content vs. (A) grade of pathologist-estimated large droplet macrosteatosis, (B) pathologist-estimated all steatosis in percentage (to the nearest 5%), and (C) the algorithm-estimated Oil Red O-stained pixels over pixel area (of Oil Red O-stained slides) in percentage. The boxes represent the interquartile range, with the horizontal lines inside the box indicating the median fat content values. The blank stars inside the box represent the mean fat content values. The whiskers extend from the boxes to represent the minimum and maximum fat content values, excluding any outliers (the 1.5xIQR rule applied) plotted as individual dots outside the whiskers.
Logistic regression (using the “logit ” command) revealed significant (all p-values < 0.0001) differences in the Raman-estimated fat content among all groups. As shown in Table 2 , the Raman-estimated fat content, as a single predictor (using the “predict ” command), effectively differentiated minimum risk (≤10%), low-risk (10%-30%), high-risk (30%-60%), and maximum-risk (≥60%) MaS and global HS, with areas of the operating characteristic curve (AUROC) between 0.88 and 0.90.
Utilizing both Raman and reflectance signals enhanced the AUROC of almost all binary classifications; however, the differences between the Raman and dual-channel predictions were insignificant (see Table S1 ).
Most specimens in the pathological validation stage had a mixed form of MaS and MiS. As Figure 6(A-B) shows, the degree of MaS of the studied liver specimens was strongly positively correlated with the degrees of pathologist-estimated global HS and the algorithm-estimated positive pixel area, limiting examinations of the investigated Raman system on distinguishing MaS from global HS.