Purpose: Patients with neuroendocrine prostate cancer (NEPC) are often managed with immunotherapy regimens extrapolated from small-cell lung cancer (SCLC). We sought to evaluate the tumor immune landscape of NEPC compared with other prostate cancer types and SCLC.
Experimental Design: In this retrospective study, a cohort of 170 patients with 230 RNA-sequencing and 104 matched whole-exome sequencing data were analyzed.
Background: Deep learning enables accurate high-resolution mapping of cells and tissue structures that can serve as the foundation of interpretable machine-learning models for computational pathology. However, generating adequate labels for these structures is a critical barrier, given the time and effort required from pathologists.
Results: This article describes a novel collaborative framework for engaging crowds of medical students and pathologists to produce quality labels for cell nuclei.