Developing a Histology-Based Artificial Intelligence Biomarker to Predict Adjuvant Chemotherapy Benefit in Pancreatic Cancer.

Date publication

juillet 2026

Journal

Journal of clinical oncology : official journal of the American Society of Clinical Oncology

Auteurs

Membres identifiés du Cancéropôle Est :
Dr BEN ABDELGHANI Meher , Pr CONROY Thierry , Dr LAMBERT Aurélien

Tous les auteurs :
Beaufils A, De Martino J, Jiang X, Blanchard T, Fraunhoffer N, Mendes D, Pignolet C, Bourega T, Meneghetti AR, Sainath S, Albuquerque M, Colnot N, Tihy M, Turpin A, Ben Abdelghani M, Wei A, Mitry E, Lecomte T, Biagi J, Artru P, Evesque L, Lambert A, Renouf DJ, Mauduit M, Dusetti NJ, Hammel P, Conroy T, Bachet JB, de Mestier L, Rebours V, Cros J, Kather JN, Nicolle R

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/42507965

Résumé

The choice of adjuvant chemotherapy in pancreatic ductal adenocarcinoma (PDAC) is mainly guided by patients' general condition. We hypothesized that tumor morphology may predict differential treatment benefit and tested whether deep learning applied to histology images could derive a biomarker of relative benefit from gemcitabine (GEM) versus modified FOLFIRINOX (mFOLFIRINOX) in resected PDAC.

Référence

J Clin Oncol. 2026 07 27;:JCO2600327