Developing a Histology-Based Artificial Intelligence Biomarker to Predict Adjuvant Chemotherapy Benefit in Pancreatic Cancer
In brief
Tumor-slide AI distinguished who benefited more from two pancreatic cancer regimens
In an external randomized trial of 313 patients with resected pancreatic cancer, a biomarker built from tumor-slide images identified groups with different relative benefit from gemcitabine versus modified FOLFIRINOX for disease-free and cancer-specific survival. The study supports treatment selection based on tumor features, but does not establish how well the tool guides care in routine practice.
- Journal
- Journal of clinical oncology : official journal of the American Society of Clinical Oncology (Q1)
- Published
- 27 July 2026
- Study design
- Randomized controlled trial
- Evidence level
- Level 1, High (CEBM 1b)
- Authors
- Audrey Beaufils, Julien De Martino, Xiaofeng Jiang, Théau Blanchard, Nicolas Fraunhoffer, Diana Mendes, et al.
- PMID
- 42507965
- DOI
- 10.1200/JCO-26-00327
Why clinicians should know about it
- Picked for Surgical Oncology (top studies of the week, 18 October 2026): Histology AI predicts adjuvant chemo benefit in PDAC
- Picked for Histology (paper of the day, 28 July 2026): High-quality evidence in a top journal
Abstract
PURPOSE: 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. PATIENTS AND METHODS: Standard whole-slide images from a retrospective multicentric series of 231 patients who underwent curative-intent pancreatectomy and received adjuvant mFOLFIRINOX (n = 54) or GEM (n = 177) were used to train regimen-specific histology models on disease-free survival (DFS), which were then combined into PANCprAId, a biomarker estimating personalized relative benefit from adjuvant GEM versus mFOLFIRINOX. External validation was performed in the randomized PRODIGE-24/CCTG PA6 trial (n = 313). RESULTS: In PRODIGE-24/CCTG PA6, the treatment-specific histology scores used to construct PANCprAId stratified outcomes among patients treated with GEM (hazard ratio [HR], 1.69 [95% CI, 1.04 to 2.73]; P = .03) and mFOLFIRINOX (HR, 2.02 [95% CI, 1.4 to 3.0]; P < .001). When combined into PANCprAId, the biomarker identified subgroups with differential relative benefit from adjuvant GEM versus mFOLFIRINOX, with significant treatment interactions for DFS (interaction P = .003) and cancer-specific survival (interaction P = .001). Predicted sensitivity to each regimen was associated with distinct epithelial and stromal features. CONCLUSION: Histology-based deep learning can derive a predictive biomarker of relative benefit from adjuvant GEM versus mFOLFIRINOX in resected PDAC.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.