Proof-of-concept study shows 3D method improves assessment of glandular features over traditional pathology<\/p>\n<\/h3>\n
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TUESDAY, Dec. 21, 2021 (HealthDay News) — A new technique, image translation-assisted segmentation in 3D, can improve prediction of aggressive prostate cancer, according to a study published online Dec. 1 in Cancer Research<\/em>.<\/p>\n Weisi Xie, from the University of Washington in Seattle, and colleagues developed a new workflow for nondestructive 3D pathology and computational analysis of whole prostate biopsies labeled with a rapid and inexpensive fluorescent analog of standard hematoxylin and eosin staining. The computational 3D approach was applied to 300 ex vivo<\/em> biopsies extracted from 50 archived radical prostatectomy specimens, of which 118 biopsies contained cancer.<\/p>\n The researchers report that the 3D features in cancer biopsies were superior to corresponding 2D features for risk stratification of low- to intermediate-risk prostate cancer patients based on their clinical biochemical recurrence outcomes over five years. The 3D images provided more information than a 2D image regarding details of complex tree-like structure of the glands throughout the tissue, which increased the likelihood that the computer would correctly predict a cancer’s aggressiveness. <\/p>\n “An end-to-end pipeline for deep learning-assisted computational 3D histology analysis of whole prostate biopsies shows that nondestructive 3D pathology has the potential to enable superior prognostic stratification of prostate cancer patients,” the authors write.<\/p>\n Several authors disclosed financial ties to the biotechnology industry, and one also disclosed ties to the pharmaceutical industry.<\/p>\n Abstract\/Full Text (subscription or payment may be required)<\/a><\/p>\n <\/i><\/p>\n