Developing a Histology-Based Artificial Intelligence Biomarker to Predict Adjuvant Chemotherapy Benefit in Pancreatic Cancer
Audrey Beaufils, Julien De Martino, Xiaofeng Jiang, Théau Blanchard, Nicolas Fraunhoffer, Diana Mendes, Camille Pignolet, Taib Bourega, Asier Rabasco Meneghetti, Srividhya Sainath, Miguel Albuquerque, Nathalie Colnot, Matthieu Tihy, Anthony Turpin, Meher Ben Abdelghani, Alice Wei, Emmanuel Mitry, Thierry Lecomte, James Biagi, Pascal Artru, Ludovic Evesque, Aurélien Lambert, Daniel J Renouf, Marjorie Mauduit, Nelson J Dusetti, Pascal Hammel, Thierry Conroy, Jean-Baptiste Bachet, Louis de Mestier, Vinciane Rebours, Jérôme Cros, Jakob Nikolas Kather, Rémy Nicolle · 2026
Read Developing a Histology-Based Artificial Intelligence Biomarker to Predict Adjuvant Chemotherapy Benefit in Pancreatic Cancer by Audrey Beaufils, Julien De Martino, Xiaofeng Jiang, Théau Blanchard, Nicolas Fraunhoffer, Diana Mendes, Camille Pignolet, Taib Bourega, Asier Rabasco Meneghetti, Srividhya Sainath, Miguel Albuquerque, Nathalie Colnot, Matthieu Tihy, Anthony Turpin, Meher Ben Abdelghani, Alice Wei, Emmanuel Mitry, Thierry Lecomte, James Biagi, Pascal Artru, Ludovic Evesque, Aurélien Lambert, Daniel J Renouf, Marjorie Mauduit, Nelson J Dusetti, Pascal Hammel, Thierry Conroy, Jean-Baptiste Bachet, Louis de Mestier, Vinciane Rebours, Jérôme Cros, Jakob Nikolas Kather, Rémy Nicolle (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13557473.txt.
Description
Audrey Beaufils and a large international group of colleagues built in 2026 an image-based marker that aims to predict which chemotherapy a pancreatic cancer patient will benefit from after surgery. Choice between the two standard regimens, gemcitabine and a modified FOLFIRINOX combination, is usually guided by how fit the patient is rather than by tumour biology. The team trained deep learning models on routine microscope slides from 231 patients treated at several centres.
Separate models learned outcome patterns under each regimen, and their outputs were combined into a score called PANCprAId that estimates personal benefit from one regimen over the other. The score was then checked in a randomised trial cohort of 313 patients, where it separated outcomes within both treatment arms. The authors propose that tumour appearance on ordinary slides carries treatment-relevant information that current practice ignores.
Catalog record
| CREATOR | Audrey Beaufils, Julien De Martino, Xiaofeng Jiang, Théau Blanchard, Nicolas Fraunhoffer, Diana Mendes, Camille Pignolet, Taib Bourega, Asier Rabasco Meneghetti, Srividhya Sainath, Miguel Albuquerque, Nathalie Colnot, Matthieu Tihy, Anthony Turpin, Meher Ben Abdelghani, Alice Wei, Emmanuel Mitry, Thierry Lecomte, James Biagi, Pascal Artru, Ludovic Evesque, Aurélien Lambert, Daniel J Renouf, Marjorie Mauduit, Nelson J Dusetti, Pascal Hammel, Thierry Conroy, Jean-Baptiste Bachet, Louis de Mestier, Vinciane Rebours, Jérôme Cros, Jakob Nikolas Kather, Rémy Nicolle |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 40 KB |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |