Benedicte Parm Ulhøi

Abstract Tissue detection is a crucial first step in most digital pathology applications. By applying image segmentation algorithms, all tissue is delineated and background discarded from further analyses, improving both computational efficiency and analytical results. Details of the segmentation algorithm are rarely reported, and there is a lack of studies investigating the downstream effects of a poor segmentation algorithm.

ADT androgen deprivation therapy AR androgen receptor ARSI androgen receptor signaling inhibition BCR biochemical recurrence CAF cancer-associated fibroblasts CRPC castration resistant prostate cancer DEG differentially expressed genes EMT epithelial-mesenchymal transition GSEA gene set enrichment analysis GSVA gene set variation analysis IC immune checkpoint ICI immune checkpoint inhibition KLK kallikrein-related peptidase LE luminal epithelial LR ligand-receptor mCRPC metastatic...

ORIGINAL ARTICLE Mads Sandahl, Corresponding Author madssand@rm.dk orcid.org/0000-0001-7689-4096 Department of Radiology, Aarhus University Hospital, Aarhus, Denmark Department of Clinical Medicine, Aarhus University, Aarhus, Denmark Correspondence Mads Sandahl (MS) Email: madssand@rm.dk Search for more papers by this author Bodil Ginnerup Pedersen, Department of Radiology, Aarhus University Hospital, Aarhus, Denmark Department of Clinical Medicine, Aarhus University, Aarhus, Denmark Search...