# Generalizability of Deep Learning System for the Pathologic Diagnosis of Various Cancers
## 4 Discussion  
In the present study, deep learning-based normal/tumor classifiers for the tissue image patches of six different cancer types were separately built for the frozen and FFPE tissue WSIs. As presented in the figures, the AUCs for all the tissue image patches in the test datasets ranged from 0.989 to 0.999 for both frozen and FFPE tissue image patches, demonstrating excellent performance. Particularly, heatmaps of the FFPE WSIs showed that the classification results were well matched with the pathologists' annotations. These results indicated that the deep learning systems can learn the appropriate features for the discrimination of the normal/tumor tissues regardless of cancer types. The heatmaps of the WSIs clearly demarcated the distribution of normal and tumor tissues in the tissue slides. The clear normal/tumor demarcation can be highly informative for current pathology workflow because a clear mark-up of the tumor in the tissue section is often necessary for various molecular tests [1,2,6]. When the heatmap is presented, clear tumor regions can be more precisely selected for molecular tests. This may diminish the possibility of false negative test results by avoiding the selection of improper tissue regions with low tumor contents. Although we focused on the normal/tumor discrimination in this study, deep learning can also be applied for more complicated analysis tasks for cancer tissues, such as assessment of tumor mutational burden [20], detection of microsatellite instability [16,21], and detection of genetic mutations [22,23,24]. The classifiers for these tasks may perform best when the tissue patches for the classification came from the tumor regions because genetic perturbation can be most well presented in the tumor regions. Therefore, the development of reliable normal/tumor tissue classifiers would be prerequisite for various tissue analysis tasks. In the present study, we developed the normal/tumor  
Figure 5: Classification results for the SMH-COAD/READ and SMH-STAD datasets by the classifiers trained on the TCGA-COAD/READ and TCGA-STAD datasets, respectively. (**a**) Results for the SMH-COAD/READ dataset. For a representative formalin-fixed paraffin-embedded (FFPE) WSI, both pathologists’ annotation and the heatmap result were presented for the comparison (left two images). Then, receiver operating characteristic (ROC) curve for the classification results was presented. (**b**) Same as (**a**), but the results were for the SMH-STAD dataset. classifiers for six different cancer types based on the TCGA WSI datasets and demonstrated that the classifiers can discriminate the respective normal/tumor tissues appropriately. However, there are many more cancer types that are not addressed in this study. When we cross-validated the classifiers between the cancer types and preparation modalities, it was clear that the generalizability of the deep learning-based classifiers is limited. Therefore, we concluded that each classifier should be developed separately for each target cancer tissue type to yield the best diagnosis performance. Although the classifiers were not very compatible between cancer types, external validation with the SMH datasets demonstrated that the classifiers could perform well on the same types of cancer tissues obtained from different institutes. The TCGA and SMH datasets may differ in the ethnicity of patients, tissue preparation processes, and tissue scanning devices. Therefore, there could be differences in tissue properties. Nevertheless, the performance of the classifiers trained with the TCGA datasets was comparable between the TCGA and SMH datasets. These results indicated that the data augmentation techniques during the training were strong enough for the deep learning systems to overcome the differences and to learn the general features for the discrimination of the normal/tumor tissues in the trained cancer types. There are also limitations in the current study.