Improving histopathological evaluation of Oral Squamous Cell Carcinoma with and without lymph node metastasis through AI driven algorithm using Foxp3 and CD43 expression

Our study aims to investigate the role of immune suppression in OSCC progression and metastasis by evaluating CD43 & Fox P3 expression, and to develop an AI-based system for automated detection and spatial mapping of Foxp3 + cells in histopathologic H&E stained slides for enhanced diagnostic insight. In PHASE 1 of the study we will do Immuno-histochemical analysis to quantify and compare CD43 & Foxp3 expression in primary tumor tissues as well as metastatic lymph nodes of both metastatic and non-metastatic OSCC cases. On the basis of calculated expression of  Foxp3/CD43 ratio we will assess its correlation with metastatic status and clinicopathologic parameters.In PHASE 2 of the study we will do AI based detection & overlay by developing and validating a deep learning algorithm that detects Foxp3+ cells by training on annotated IHC slides, aligns IHC & H&E images through spatial alignment, and generates interpretable overlays and heat maps of Foxp3+ cell distribution on H&E stained tissue. Finally we will validate the AI model’s performance against manual pathologist annotation using standard metrics.