Development and Validation of a Computational Histology Artificial Intelligence-Powered Predictive Biomarker for Selection of Chemotherapy in Advanced Pancreatic Cancer - PubMed
3 months ago
- #Pancreatic Cancer
- #Predictive Biomarker
- #Artificial Intelligence
- Development of a Computational Histology Artificial Intelligence (CHAI) platform to predict chemotherapy benefit in advanced pancreatic ductal adenocarcinoma (PDAC).
- The CHAI platform analyzed histomorphologic features from hematoxylin and eosin-stained biopsies to create a GvF biomarker.
- The biomarker dichotomized patients into G-pref (gemcitabine-based) or F-pref (fluoropyrimidine-based) treatment groups.
- Validation showed F-pref patients had better outcomes with F-chemo (TNTD: 8.6 vs. 7.5 months; OS: 14.4 vs. 11.7 months).
- G-pref patients had superior TNTD with G-chemo (9.6 vs. 7.2 months) but no OS difference.
- Propensity score-weighted analysis confirmed the biomarker's predictive value (TNTD P < .001; OS P = .005).
- RNA subtypes did not predict differential treatment effects (P = .3).
- The GvF biomarker can guide optimal first-line chemotherapy selection for advanced PDAC.