Publication Date

5-16-2026

Document Type

Article

Publication Title

Digital Health

Volume

12

DOI

10.1177/20552076261453125

Abstract

Introduction: Precisely grading renal cell carcinoma (RCC) through histopathology slides is a requisite to predict the cancer prognosis and select treatments, however, there is a considerable variability in the assessment between observers. While some AI systems mainly rely on transformer-based and nuclei-centric models, they are computationally very demanding so their clinical use is thus limited. Hence, there is a need for practical and easy-to-understand solutions that can be integrated into digital pathology workflows. Methods: We built a hybrid framework that integrates U-Net-based tumor segmentation, convolutional feature extraction, nuclei-aware descriptors, stain normalization, and attention-based multiple instance learning for slide-level RCC grading. The framework was tested on 3 public datasets (TCGA-ccRCC, RCdpia, MMIST-ccRCC) with a cross-dataset validation approach. The metrics used for the performance evaluation were macro-F1 and quadratic weighted kappa (QWK). Results: The designed method yielded a macro-F1 of 0.94, QWK of 0.92, and accuracy of 0.95. Extracting tumor patches and performing aggregation based on attention resulted in the best improvements. The method was equally effective when tested with different datasets. Most of the errors made by the model were those within the clinical grading range variability. Average time for inference was around one minute and ten seconds per slide. Conclusion: By fine-tuning the convolutional pipeline, one can obtain a RCC grading capability that can be rivaled by very few, yet the model is efficient and interpretable, hence it will continue to be a strong candidate decision-support tool in digital pathology to be clinically deployed.

Funding Sponsor

San José State University

Keywords

automated tumor gradin, DenseNet, histopathological imaging, renal cell carcinoma, U-net segmentation

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Department

Electrical Engineering

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