Automatic segmentation of clear cell renal cell carcinoma based on deep learning and a preliminary exploration of the tumor microenvironment.

Abstract

Background: Whole-slide imaging (WSI) is increasingly becoming a standard method for diagnosing clear cell renal cell carcinoma (ccRCC). This advanced imaging technique allows for high-resolution examination of tissue sections, improving diagnosis and management of renal cancers. Immunotherapy has emerged as an effective treatment for tumors; however, the differential characteristics of the tumor microenvironment (TME) significantly influence therapeutic outcomes. Understanding the interactions between cancer cells and the TME is essential for optimizing immunotherapeutic strategies. This study aims to investigate the characteristics of the TME in ccRCC using WSI, with the goal of identifying factors that might influence immunotherapy response and improving therapeutic strategies.

Methods: In this study, we proposed a novel method for the automatic segmentation of ccRCC regions based on deep-learning techniques. This method uses advanced convolutional neural networks to effectively distinguish between tumor areas (TAs) and surrounding tissues. Additionally, we employed inverse threshold segmentation to quantitatively analyze the results and spatial distributions of lymphocytes and collagen fibers in immunohistochemical and Masson's trichrome-stained images. This comprehensive approach not only streamlines the diagnostic process but also enhances the precision of histopathological assessments.

Results: Our model had a classification accuracy of 96.67% on image patches and a sensitivity of 94.29%, demonstrating its ability to segment TAs both accurately and efficiently. The distribution of cluster of differentiation (CD)3⁺ and CD8⁺ T lymphocytes, and collagen fibers in patients at different tumor-node-metastasis (TNM) stages was analyzed. The results revealed that a high infiltration of CD3⁺ T cells, particularly CD8⁺ cytotoxic T cells, was more prevalent in patients with advanced-stage tumors. Additionally, the proliferation of collagen fibers in tumors was found to be significantly correlated with tumor growth and metastasis.

Conclusions: Our results underscore the potential of artificial intelligence (AI) technology to provide novel insights to guide ccRCC immunotherapy. By applying deep learning to tumor segmentation and TME analysis, this methodology offers a promising approach to improve the understanding of tumor biology and therapeutic outcomes. Future research should focus on integrating these findings into clinical practice to optimize patient-specific immunotherapeutic strategies, and thus advance treatment protocols and improve the survival rates of ccRCC patients.