Immunohistochemistry prediction using H-score in cancer diagnosis

Quantitative analysis of biomarker expression is crucial for cancer diagnosis and prognosis. Traditional methods rely on immunohistochemistry (IHC) staining, which can take several days, delaying clinical decisions. To address this issue, this study introduces a novel deep-learning-based method that infers the H-score, a measure of biomarker abundance, directly from readily available hematoxylin and eosin (H&E) stained slides, eliminating the need for IHC staining. We tested our method on H&E slides from breast cancer and renal cell carcinoma, finding an average Pearson correlation coefficient of 0.92 compared to existing IHC-based methods. Additionally, the generated H-score heatmaps for whole slide images stained with H&E demonstrated a strong spatial correlation with the ground truth, with cosine similarity exceeding 0.86 for most biomarker heatmaps. The results indicate that our method can reliably infer the expression of cancer biomarkers from H&E slides alone.Clinical relevance- This approach has the potential to streamline pathology workflows and enhance the efficiency and comprehensiveness of biomarker evaluation in clinical practice.