(HealthDay News) — A new technique, image translation-assisted segmentation in 3D, can improve prediction of aggressive prostate cancer, according to a study published online in Cancer Research.

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 biopsies extracted from 50 archived radical prostatectomy specimens, of which 118 biopsies contained cancer.

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.


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“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.

Several authors disclosed financial ties to the biotechnology industry, and one also disclosed ties to the pharmaceutical industry.

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