Inferring Clinically Relevant Molecular Subtypes of Pancreatic Cancer from Routine Histopathology Using Deep Learning
By enabling rapid, cost-effective molecular stratification from routine H&E-stained slides, PanSubNet offers a clinically deployable and interpretable tool for genetic subtyping. We are gathering data from two institutions to validate and assess real-world performance, supporting integration into digital pathology workflows and advancing precision oncology for PDAC.
Authors
- Anne M. Noonan (ORCID: https://orcid.org/0000-0001-8083-8492)
- Alejandro H. Marín Levya
- Ashwini Kumar Esnakula (ORCID: https://orcid.org/0000-0001-6029-0124)
- Muhammad Khalid Khan Niazi (ORCID: https://orcid.org/0000-0002-1278-7512)
- Abdul Akbar (ORCID: https://orcid.org/0009-0004-2352-6360)
- W Chen
- Anil Vasdev Parwani (ORCID: https://orcid.org/0000-0002-7616-1666)
- Upender Manne
- Ashish Manne
- Lingbin Meng (ORCID: https://orcid.org/0000-0002-9383-3540)
- Elshad Hassanov
- Susan Tsai
Institutions
- University of Michigan (US)
- The Ohio State University Wexner Medical Center (US)
- University of Alabama at Birmingham (US)
- Michigan Center for Translational Pathology (US)
Publication Details
- Journal
- medRxiv
- Published
- 2026-01-08
- DOI
- https://doi.org/10.64898/2026.01.06.26343539
- Primary Topic
- Pancreatic and Hepatic Oncology Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Pancreatic Cancer Action Network
- Ohio State University
- Pelotonia
- National Institutes of Health
- National Cancer Institute