Immune-Guided AI for Reproducible Regions of Interest Selection in Multiplex Immunofluorescence Pathology Imaging

Selecting regions of interest (ROIs) in whole-slide histology images (WSIs) is a crucial step for spatial molecular profiling. As a general practice, pathologists manually select ROIs within each WSI based on morphological tumor markers to guide spatial profiling, which can be inconsistent and subjective. To enhance reproducibility and avoid inter-pathologist variability, we introduce a novel immune-guided end-to-end pipeline to automate the ROI selection in multiplex immunofluorescence (mIF) WSIs stained with three cell markers (Syto13, CD45, PanCK). First, we estimate immune infiltration (CD45 \(^+\) expression) scores at the grid level in each WSI. Then, we incorporate the Pathology Language and Image Pre-Training (PLIP) foundational model to extract features from each grid and further select a subset of grids representative of the whole slide that comparatively matches pathologists’ assessment. Further, we implement state-of-the-art detection models for ROI detection in each grid, incorporating learning from pathologists’ ROI selection. Our study shows a significant correlation between our automated method and pathologists’ ROI selection across five different types of carcinomas, as evidenced by a significant Spearman’s correlation coefficient (> 0.785, p < 0.001), substantial inter-rater agreement (Cohen’s \(\kappa >\) 0.671), and the ability to replicate the ROI selection made by independent pathologists with excellent average performance (0.968 precision and 0.991 mean average precision at a 0.5 intersection-over-union). By minimizing manual intervention, our solution provides a flexible framework that potentially adapts to various markers, thus enhancing the efficiency and accuracy of digital pathology analyses.