Virtual multiplex immunofluorescence identifies lymphocyte subsets predictive of response to neoadjuvant therapy.

ABSTRACT:

Background

Hematoxylin and eosin (H&E) staining is routine in pathology but lacks cellular specificity. Multiplex immunofluorescence (mIF) captures spatial immune relationships in tumors, but cost and complexity limit clinical application. Novel approaches to yield similar information from readily available tumor histology are needed.

Objectives

Develop and validate a novel deep learning tool capable of translating standard H&E-stained histopathology images into high-fidelity synthetic mIF images that preserve immune cell information predictive of treatment response in breast cancer.

Design

Comparative model evaluation and predictive modeling in a retrospective breast cancer cohort.

Methods

Core-needle biopsies from 17 triple-negative breast cancer cases underw

SUBMITTER: Li A 

PROVIDER: S-EPMC12547128 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature