Inferring CAF-1/p60 Expression from Hematoxylin and Eosin Stained Images in Oral Squamous Cell Carcinoma

Hematoxylin and Eosin (HE) staining remains a standard of histopathological diagnostics due to its efficiency, affordability, and ubiquity. However, its lack of molecular specificity often necessitates complementary immunohistochemistry (IHC) to detect protein-level biomarkers critical for diagnosis and treatment planning. In scenarios where IHC is impractical, due to cost, limited tissue, or logistical constraints, computational alternatives may provide viable solutions. In this study, we investigate the feasibility of predicting CAF-1/p60 protein expression directly from HE-stained slides using deep learning. Leveraging a curated dataset of oral squamous cell carcinoma samples with nucleus-level annotations, we classify nuclei into three categories: (i) Tumor-CAF-1/p60-positive, (ii) Tumor-CAF1/p 6 0-negative, and (iii) stromal. Our results demonstrate that Convolutional Neural Networks (CNNs) can learn morphological cues indicative of protein expression, achieving promising classification performance at the single-cell level. This approach may enable more accessible molecular profiling from routine HE slides, reducing dependency on specialized staining procedures in resource-constrained or time-sensitive settings.