# Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers
## 3 Results  
### TCGA Frozen and FFPE Datasets  
Normal/tumor classifiers for the six different cancer types were trained with the training datasets of the TCGA frozen and FFPE WSIs (Figure 1a). Then, the classifiers were applied to the test datasets to distinguish the tissue image patches from a WSI into normal or tumor patches. Based on the classification results for the whole tissue image patches of all the WSIs in the test datasets, patch-level ROC curves were drawn to describe the classification performance of the classifiers. Figure 2a,b demonstrate the results for the TCGA-BLCA datasets. In the first two pictures of panel a, heatmaps for the representative classification results for the test datasets of the frozen normal and tumor tissues were presented. Then, the image of normal/tumor annotation and the heatmap for the classification results for a representative FFPE tissue from the test datasets were presented. In the first two graphs of panel b, the ROC curves for the patch-level classification results on all the frozen tissue image patches in the test datasets by the classifiers trained on the frozen and FFPE tissues were presented. The AUCs were 0.9995 and 0.9547 for the classifiers trained on the frozen and FFPE tissues, respectively. Then, the ROC curves for the patch-level classification results on the FFPE tissues by the classifiers trained on the FFPE and frozen tissues were presented. In this case, the AUCs were 0.9823 and 0.8118 for the classifiers trained on the FFPE and frozen tissues, respectively. Figure 2c,d adopt the same formats to demonstrate the results for the TCGA-LUAD/LUSC datasets. The AUCs for the patch-level classification results of the  
frozen and FFPE tissues were 0.9920 and 0.9892 for the classifiers trained on the frozen and FFPE tissues, respectively, and were 0.9147 and 0.9334 for the classifiers trained on the FFPE and frozen tissues, respectively. These results indicated that the classifiers could show optimal performance only when the classifiers were applied to the trained tissue preparation modalities. Figure 2: Classification results for the frozen and FFPE tissues of bladder (TCGA-BLCA) and lung (TCGA-LUAD/LUSC) datasets. (**a**) Representative classification results. Heatmaps were drawn for the representative whole slide images (WSIs) of the normal and tumor frozen WSIs of the TCGA-BLCA dataset (left two images). For a representative formalin-fixed paraffin-embedded (FFPE) WSI, both pathologists’ annotation and the heatmap result were presented for the comparison. (**b**) Receiver operating characteristic (ROC) curves for the classification results. Left two graphs: the receiver operating characteristic (ROC) curves for the frozen tissues classified by the classifiers trained on frozen and FFPE tissues. Right two graphs: the ROC curves for the FFPE tissues classified by the classifiers trained on FFPE and frozen tissues. (**c**,**d**) are the same as (**a**,**b**), but the results were for the TCGA-LUAD/LUSC dataset. In case of the TCGA-COAD/READ datasets (Figure 3a,b), the AUCs for the patch-level classification of the frozen tissues were 0.9959 and 0.9413 by the classifiers trained on the frozen and FFPE tissues, respectively. The AUCs for the FFPE tissues were 0.9986 and 0.7806 by the classifiers trained on the FFPE and frozen tissues, respectively. For the TCGA-STAD datasets (Figure 3c,d), the AUCs for the classification results of the frozen and FFPE tissues were 0.9895 and 0.9935 for the classifiers trained on the same preparation modalities and were 0.8952 and 0.8874 for the classifiers trained on different preparation modalities. Figure 3: Classification results for the TCGA-COAD/READ and TCGA-STAD datasets. (**a**) Representative classification results. Heatmaps were drawn for the representative whole slide images (WSIs) of the normal and tumor frozen WSIs of the TCGA-COAD/READ dataset (left two images). For a representative formalin-fixed paraffin-embedded (FFPE) WSI, both pathologists’ annotation and the heatmap result were presented for the comparison.