RCdpia: A Renal Carcinoma Digital Pathology Image Annotation dataset based on pathologists
This provides a high-quality, publicly accessible dataset for developing more precise AI models in digital pathology, addressing noise and heterogeneity issues for researchers and clinicians, though it is incremental as it builds on existing data from TCGA.
The authors tackled the problem of noisy and heterogeneous annotations in renal carcinoma digital pathology by creating a meticulously curated dataset (RCdpia) with pathologist labels, which includes 887 cases across three cancer types and enabled model validation revealing significant discrepancies in predictive outcomes across datasets.
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.