Artificial Intelligence-Driven Quantification of Tumor-Stroma Ratio and Fibroblasts Enables Precise Classification of Stroma Quality and Quantity in Predicting Colorectal Cancer Recurrence

Abstract

Introduction: The tumor microenvironment plays a crucial role in the progression and prognosis of colorectal cancer (CRC). Among its components, the tumor-stroma ratio (TSR) and cancer-associated fibroblasts (CAFs) have emerged as significant prognostic markers. However, conventional assessments of TSR and CAF density remain subjective and labor-intensive, limiting their clinical applicability. Methods: We utilized an artificial intelligence (AI)-based whole slide image analysis platform, Lunit SCOPE IO, to objectively quantify TSR and CAF density in tissue samples from 207 treatment-na & iuml;ve patients with stage II and III CRC. Results: Our analysis demonstrated that both TSR (log-rank p < 0.0001) and CAF density (log-rank p = 0.017) were independently associated with disease-free survival (DFS). These AI-derived markers outperformed conventional prognostic factors. Furthermore, integrating TSR and CAF density with existing high-risk criteria enabled reclassification of additional patients as high risk, enhancing DFS prediction and reducing false-negative rates. Conclusion: AI-powered histopathological quantification of TSR and CAF density improves prognostic accuracy in CRC and offers a promising approach for refining risk stratification. These findings support the integration of AI-based pathology into clinical practice to enhance diagnostic precision and patient management.