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. 2022 Mar 29;14(7):1740. doi: 10.3390/cancers14071740

Figure 3.

Figure 3

Performance of the HALO-AI lung NET module in the testing set. (A) HALO-AI lung NET predicted area distribution calls against gold standard diagnoses. The diagnosis by the AI algorithm, the expert pathologist and the general pathologist is highlighted on top. The heatmap highlights the predicted area of the respective cancer histology (SCLC, LCNEC or AC) as a fraction of 1 (where 1 highlights that the whole area is of the respective histology). A predicted area of greater than 0.5 (50%) was chosen to define the histology based on the AI algorithm. Samples are clustered by Euclidian distance based on the predicted area. (B–D) ROC analysis for prediction of lung NETs based on the diagnoses of expert and general pathologists. Classification was based on the presence of a histology ((B), SCLC; (C), LCNEC; (D), atypical carcinoid) versus the predicted area as a continuous variable. Analysis for both the expert and the general pathologists is shown. Combined cases were excluded for ROC analysis.