Pathology

Practical pathology primer for computational researchers to improve collaboration

This guide summarizes pathology workflow, whole-slide imaging, and core diagnostic concepts for computational scientists entering digital pathology. It aims to align terminology and bridge language gaps between pathologists and engineers to enable effective interdisciplinary collaboration. Use this as a reference when onboarding engineers, designing annotation schemas, or planning clinically grounded model tasks. The article is educational and not an empirical validation of specific algorithms or clinical outcomes. 1

MorphoXAI: human-in-the-loop framework providing global and local WSI explanations

MorphoXAI produces global patterns and local slide-region explanations that reflect histomorphology underlying DL predictions on WSIs. Human evaluation found explanations easy to interpret and directly helpful for diagnostic decision-making and pathologist–AI collaboration. The framework was validated across multiple WSI tasks and tissue types, supporting broader interpretability claims. Before clinical deployment, couple MorphoXAI outputs with local expert review and prospective verification. 2

Phase 2 monotherapy cancer trials show lower efficacy than phase 3

Meta-analysis of 130 phase 2 and 52 phase 3 monotherapy arms found pooled objective response 7% in phase 2 versus 24% in phase 3. Median PFS and OS were shorter in phase 2 (3.23 and 9.46 months) than phase 3 (5.43 and 14.44 months). Grade 3–4 adverse event rates were similar between phases, around 25%–30%. Authors conclude phase 2 drug administration should generally be justified by research value, not therapeutic expectation. 3