An Iterative, Pathologist-in-the-Loop Workflow for Generation of Clinical-Grade Synthetic Pathology Images in a Diverse Cohort of Pancreatic Tumors

Background/Objectives: The training of diagnostic pancreatic pathologists is largely limited by the diversity of available pathology images, especially those of rare diseases or conditions. Methods: Using a cohort of seven pancreatic neoplasms, we developed an iterative, pathologist-in-the-loop workflow integrating scalable tile pruning, generative-model optimization, and postprocessing truncation. Two pathologists evaluated synthetic-image quality and subtype representation on a 0–3 scale; the independent pathologist was blinded to image source and truncation condition and also rated curated real training tiles. Results: Untruncated images had lower class-balanced FID than per-class-truncated images (6.64 versus 29.08) and higher recall and coverage, whereas truncation increased precision. The independent pathologist rated per-class-truncated images higher than matched non-truncated images (mean difference 0.80, bootstrap 95% CI 0.57–1.03). Quadratic-weighted Cohen’s κ was 0.599 (95% CI 0.489–0.691); after grouping ratings as 0–1 versus 2–3, raw agreement was 80.7% (95% CI 75.0–86.4%). Conclusions: Per-class truncation improved independently rated image quality and subtype representation. Successful synthetic histology generation requires careful data curation, domain-specific oversight, and independent validation; clinical utility requires separate task-based evaluation.