# Development and Evaluation of a Deep Neural Network for Histologic Classification of Renal Cell Carcinoma on Biopsy and Surgical Resection Slides

arXiv: https://arxiv.org/abs/2010.16380 · HTML: https://mlsanity.com/paper/2010.16380/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 24 of 24)

- [Phikon-v2, A large and public feature extractor for biomarker prediction](https://mlsanity.com/paper/2409.09173/) (2024)
- [A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model](https://mlsanity.com/paper/2407.15362/) (2024)
- [A General-Purpose Self-Supervised Model for Computational Pathology](https://mlsanity.com/paper/2308.15474/) (2023)
- [Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology](https://mlsanity.com/paper/2204.04516/) (2022)
- [2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification](https://mlsanity.com/paper/2412.00678/) (2024)
- [Domain Generalization in Computational Pathology: Survey and Guidelines](https://mlsanity.com/paper/2310.19656/) (2023)
- [Masked Pre-Training of Transformers for Histology Image Analysis](https://mlsanity.com/paper/2304.07434/) (2023)
- [Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy](https://mlsanity.com/paper/2306.07999/) (2023)
- [Multi-Resolution Pathology-Language Pre-training Model with Text-Guided Visual Representation](https://mlsanity.com/paper/2504.18856/) (2025)
- [CARE: A Molecular-Guided Foundation Model with Adaptive Region Modeling for Whole Slide Image Analysis](https://mlsanity.com/paper/2602.21637/) (2026)
- [Streamline pathology foundation model by cross-magnification distillation](https://mlsanity.com/paper/2509.23097/) (2025)
- [CAMP: Continuous and Adaptive Learning Model in Pathology](https://mlsanity.com/paper/2407.09030/) (2024)
- [Ensemble learning of pathology foundation models for precision oncology](https://mlsanity.com/paper/2508.16085/) (2025)
- [HistoPerm: A Permutation-Based View Generation Approach for Improving Histopathologic Feature Representation Learning](https://mlsanity.com/paper/2209.06185/) (2022)
- [Ethical Challenges and Evolving Strategies in the Integration of Artificial Intelligence into Clinical Practice](https://mlsanity.com/paper/2412.03576/) (2024)
- [LatentExplainer: Explaining Latent Representations in Deep Generative Models with Multimodal Large Language Models](https://mlsanity.com/paper/2406.14862/) (2024)
- [Computational pathology in renal disease: a comprehensive perspective](https://mlsanity.com/paper/2210.10162/) (2022)
- [NephroNet: A Novel Program for Identifying Renal Cell Carcinoma and Generating Synthetic Training Images with Convolutional Neural Networks and Diffusion Models](https://mlsanity.com/paper/2302.05830/) (2023)
- [DPSeq: A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers using Sequencer Architecture](https://mlsanity.com/paper/2305.01968/) (2023)
- [Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records](https://mlsanity.com/paper/2410.09880/) (2024)
- [Renal Cell Carcinoma subtyping: learning from multi-resolution localization](https://mlsanity.com/paper/2411.09471/) (2024)
- [From Patches to Evidence Balls: Class-Conditioned Evidence Retrieval for Few-Shot Whole Slide Image Classification](https://mlsanity.com/paper/2608.01104/) (2026)
- [From Multi-Resolution Cells to Gigapixel Whole Slide Images Foundation Model for Computational Pathology](https://mlsanity.com/paper/2608.03508/) (2026)
- [Cross-Modal Contrastive Learning from Histopathology and CT for Automated Renal Cell Carcinoma Grading](https://mlsanity.com/paper/2609.26920/) (2026)

## Abstract (arXiv)

Renal cell carcinoma (RCC) is the most common renal cancer in adults. The histopathologic classification of RCC is essential for diagnosis, prognosis, and management of patients. Reorganization and classification of complex histologic patterns of RCC on biopsy and surgical resection slides under a microscope remains a heavily specialized, error-prone, and time-consuming task for pathologists. In this study, we developed a deep neural network model that can accurately classify digitized surgical resection slides and biopsy slides into five related classes: clear cell RCC, papillary RCC, chromophobe RCC, renal oncocytoma, and normal. In addition to the whole-slide classification pipeline, we visualized the identified indicative regions and features on slides for classification by reprocessing patch-level classification results to ensure the explainability of our diagnostic model. We evaluated our model on independent test sets of 78 surgical resection whole slides and 79 biopsy slides from our tertiary medical institution, and 69 randomly selected surgical resection slides from The Cancer Genome Atlas (TCGA) database. The average area under the curve (AUC) of our classifier on the internal resection slides, internal biopsy slides, and external TCGA slides is 0.98, 0.98 and 0.99, respectively. Our results suggest that the high generalizability of our approach across different data sources and specimen types. More importantly, our model has the potential to assist pathologists by (1) automatically pre-screening slides to reduce false-negative cases, (2) highlighting regions of importance on digitized slides to accelerate diagnosis, and (3) providing objective and accurate diagnosis as the second opinion.