MIPHEI-ViT: Multiplex immunofluorescence prediction from H&E images using ViT foundation models

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: F1 scores: Pan-CK 0.93, α-SMA 0.83, CD3e 0.68, CD20 0.36, CD68 0.28

• Primary Outcome: Cell-type classification accuracy from H&E images measured by F1 scores

• Nnt Or Sensitivity: Sensitivity and specificity not reported; performance varies significantly by marker type

• Confidence Interval: Not reported

Clinical Application

Requires high-quality H&E images and computational infrastructure; may be feasible in major pathology laboratories with digital pathology capabilities Could support Australian pathology services by reducing reliance on expensive multiplex immunofluorescence, particularly relevant for regional centres. Would need TGA consideration for diagnostic use and integration with existing digital pathology workflows Patients with colorectal cancer requiring tissue analysis, particularly in resource-limited settings where multiplex immunofluorescence is not readily available

Abstract

Histopathological analysis is a cornerstone of cancer diagnosis, with Hematoxylin and Eosin (H&E) staining routinely acquired for every patient to visualize cell morphology and tissue architecture. On the other hand, multiplex immunofluorescence (mIF) enables more precise cell type identification via proteomic markers, but has yet to achieve widespread clinical adoption due to cost and logistical constraints. To bridge this gap, we introduce MIPHEI (Multiplex Immunofluorescence Prediction from H&E Images), a U-Net-inspired architecture that leverages a ViT pathology foundation model as an encoder to predict mIF signals from H&E images using rich pretrained representations. MIPHEI targets a comprehensive panel of markers spanning nuclear content, immune lineages (T cells, B cells, myeloid), epithelium, stroma, vasculature, and proliferation. We train our model using the publicly available OrionCRC dataset of restained H&E and mIF images from colorectal cancer tissue, and validate it on five independent datasets: HEMIT, PathoCell, IMMUcan, Lizard and PanNuke. On OrionCRC test set, MIPHEI achieves accurate cell-type classification from H&E alone, with F1 scores of 0.93 for Pan-CK, 0.83 for α-SMA, 0.68 for CD3e, 0.36 for CD20, and 0.28 for CD68, substantially outperforming both a state-of-the-art baseline and a random classifier for most markers. Our results indicate that, for some molecular markers, our model captures the complex relationships between nuclear morphologies in their tissue context, as visible in H&E images and molecular markers defining specific cell types. MIPHEI offers a promising step toward enabling cell-type-aware analysis of large-scale H&E datasets, in view of uncovering relationships between spatial cellular organization and patient outcomes.