  The annotation of digital pathological slide data for renal cell carcinoma is
of paramount importance for correct diagnosis of artificial intelligence models
due to the heterogeneous nature of the tumor. This process not only facilitates
a deeper understanding of renal cell cancer heterogeneity but also aims to
minimize noise in the data for more accurate studies. To enhance the
applicability of the data, two pathologists were enlisted to meticulously
curate, screen, and label a kidney cancer pathology image dataset from The
Cancer Genome Atlas Program (TCGA) database. Subsequently, a Resnet model was
developed to validate the annotated dataset against an additional dataset from
the First Affiliated Hospital of Zhejiang University. Based on these results,
we have meticulously compiled the TCGA digital pathological dataset with
independent labeling of tumor regions and adjacent areas (RCdpia), which
includes 109 cases of kidney chromophobe cell carcinoma, 486 cases of kidney
clear cell carcinoma, and 292 cases of kidney papillary cell carcinoma. This
dataset is now publicly accessible at http://39.171.241.18:8888/RCdpia/.
Furthermore, model analysis has revealed significant discrepancies in
predictive outcomes when applying the same model to datasets from different
centers. Leveraging the RCdpia, we can now develop more precise digital
pathology artificial intelligence models for tasks such as normalization,
classification, and segmentation. These advancements underscore the potential
for more nuanced and accurate AI applications in the field of digital
pathology.
