An Explanatory Deep Learning Pipeline for the Prediction and Visualization of Spatially Resolved Biomarker Expression in Triple-Negative Breast Cancer.

Vibha R Rao¹, Madhumala K Sadanandappa¹, Candice C Black², Scott M Palisoul¹, Adrienne A Workman², Todd A MacKenzie³, Xiaoying Liu², Mary D Chamberlin³, Louis J Vaickus², George J Zanazzi², Shrey S Sukhadia⁴

¹Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA.

²Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA; Geisel School of Medicine, Dartmouth College, Hanover, NH, USA.

³Geisel School of Medicine, Dartmouth College, Hanover, NH, USA.

⁴Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA; Geisel School of Medicine, Dartmouth College, Hanover, NH, USA. Electronic address: shrey.sukhadia@dartmouth.edu.

PubMed DOI

Abstract

Histopathologic evaluation remains central to cancer diagnosis and treatment planning, yet the molecular programs underlying distinct tissue morphologies aren't routinely accessible in clinical workflows. Spatial transcriptomic/proteomic platforms provide region-specific molecular measurements but are limited by cost, throughput, and scalability. Most computational pathology models rely on either…

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