Health AI Central
Peer-Reviewed Publication
Cancers (Basel)2026;18(18)September 9, 2026Journal Article

A Computational Histology Artificial Intelligence Prognostic Biomarker in Non-Small Cell Lung Cancer Using the Cancer Genome Atlas.

Benjamin A Bleiberg1,2, Masaoki Ito3, Melina Marmarelis4, Viswesh Krishna5, Vivek Nimgaonkar5,6, Haochen Zhang5, Trevor J Royce5,7, Yasuhiro Tsutani8, David Kozono9, Charu Aggarwal1,2,4
1Division of Hematology-Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
2Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA.
3Division of Thoracic Surgery, Department of Surgery, Kindai University, Osaka 577-8502, Japan.
4Penn Center for Cancer Care Innovation, University of Pennsylvania, Philadelphia, PA 19104, USA.
5Valar Labs, Palo Alto, CA 94306, USA.
6Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.
7Department of Radiation Oncology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27514, USA.
8Division of Thoracic Surgery, Kindai University, Osaka 589-0014, Japan.
9Dana Farber Cancer Institute, Boston, MA 02215, USA.
PubMed PMC DOI PDF

Abstract

Background/Objectives: Outcomes in non-small cell lung cancer (NSCLC) remain heterogeneous, even within stage and molecular subtypes. We evaluated whether a Computational Histology Artificial Intelligence (CHAI) biomarker, derived solely from the diagnostic hematoxylin-and-eosin (H&E) whole-slide image (WSI), provides prognostic information independent of established clinicopathologic factors. Met…

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