Abdul Akbar

Abstract Pancreatic ductal adenocarcinoma comprises classical and basal-like molecular subtypes that differ in prognosis and treatment response. However, transcriptomic subtyping is limited by cost, turnaround time, and tissue requirements. We aimed to determine whether these molecular phenotypes can be inferred directly from routine hematoxylin and eosin–stained whole-slide images using deep learning.

Dear Editor, Molecular subtyping of pancreatic ductal adenocarcinoma (PDAC) into basal-like and classical states is a critical prognostic determinant, yet clinical implementation remains limited by the cost and turnaround time of transcriptomic sequencing.1,2,3 Although routine histopathology captures rich morphological features, deep learning models often lack a principled connection to gene-level molecular structure.4,5 We propose a graph-constrained histology model that maps...

Abstract Recurrence remains a major clinical challenge in surgically resected invasive lung adenocarcinoma (ILA), where existing staging and grading systems fail to capture the cellular complexity that underlies tumor aggressiveness. We present PathRosetta, an AI framework that models cellular ecology of tumor microenvironment (TME) from hematoxylin-and-eosin slides.