Ralph H. Hruban

The paper quantifies PanIN burden and pancreatic composition across age to identify high-risk demographics associated with increased precancer burden.

CODAvision provides a user-friendly interface for rapid, customizable medical image segmentation using deep learning, enabling non-experts to train and evaluate models across diverse imaging modalities.

This paper introduces SMILE, a diffusion model for converting H&E stains to multiplex IHC images, showing superior performance in pancreatic islet analysis and breast cancer stain conversion.

This study develops a high-sensitivity spatial proteomics method, SP-Max, to analyze protein expression changes in pancreatic tissues from normal to cancerous states.

This study profiles proteins and glycoproteins in pancreatic neoplasms to identify expression patterns and potential progression markers.

This study shows that 3D assessments are necessary to accurately determine tissue composition and neoplastic content, as 2D methods under-sample spatial heterogeneity.

The paper identifies genomically stable regions in metastatic pancreatic cancer that can be targeted by CRISPR-Cas9 due to preserved PAM sites, suggesting a potential gene therapy approach.

The paper profiles the immune microenvironment of PanINs in 3D at single-cell resolution to understand inflammation's role in cancer progression and finds immune heterogeneity and correlations with PanIN grade.

The paper uses AI-based 3D tissue mapping to show PanIN lesion sizes follow a power-law distribution and identifies growth mechanisms driving pancreatic cancer precursor lesions.

The paper identifies tissue morphology biomarkers in long-term PDAC survivors using digital pathology and machine learning, finding reduced heterogeneity and extra-tumoral space features correlate with better survival.