Michelle R. Downes

SCALE3D improves prostate cancer risk prediction by analyzing 3D cell interactions through supercell graphs, outperforming traditional features and reducing computational demands.

This paper develops a 3D computational pipeline called SIGHT to detect prostate cancer in pathology datasets using generative immunolabeling, achieving an F1 score comparable to pathologist agreement.

CARP3D uses deep learning to prioritize high-risk 2D sections in 3D pathology data, improving disease detection while reducing pathologist workload.

The paper quantifies secreted and extracellular vesicle (EV) urinary proteomes in men with and without prostate cancer, showing their stability and utility in distinguishing malignant from benign lesions.