Health
3D imaging methods may help physicians better determine the aggression of prostate cancer
December 9, 2021
A team led by the University of Washington has developed a new non-destructive method that images the entire 3D biopsy instead of slicing. 3D images provided more information than 2D images. Specifically, it is a detail about the tree-like structure of the gland throughout the tissue. Shown here are screenshots of gland volume rendering in two 3D biopsy samples from the prostate (yellow: gland outer wall, red: fluid-filled space within the gland). The cancer sample (top) shows smaller, more dense glands compared to the benign tissue sample (bottom).Xie et al./Cancer Research
Prostate cancer The most common cancer for men And for American men, it’s the second leading cause of death.
Some prostate cancers are slow-growing and may be monitored over time, while other prostate cancers need immediate treatment. To determine how aggressive someone’s cancer is, doctors look for abnormalities in slices of biopsy tissue on slides. However, this 2D technique makes it difficult to properly diagnose boundary cases.
Now, a team led by the University of Washington has developed a new non-destructive method of imaging the entire 3D biopsy, not just slices. In a principle-based experiment, researchers imaged 300 3D biopsies (6 biopsies per patient) taken from 50 patients and used 3D and 2D results on a computer to help patients with advanced cancer. Predicted the possibility of suffering from. The 3D capabilities made it easy for computers to identify cases that are likely to recur within five years.
team Publish these results December 1st of Cancer Research.
“For the first time, we have shown that the ability to test 100% of biopsies in 3D is more informative and accurate compared to traditional pathology, where a small portion of each biopsy is tested in 2D on a microscope slide. “The senior author said. Jonathan Liu, UW Professor of Mechanical Engineering and Biotechnology. “This is a number of clinical studies demonstrating the value of non-destructive 3D pathology for clinical decision making, including determining a subset of patients in need of aggressive treatment or who respond best to a particular drug. It’s exciting because it’s the first of its kind. “
Researchers used prostate specimens from patients who underwent surgery more than 10 years ago, allowing teams to understand the outcome of each patient and use that information to train computers and predict outcomes. I did. In this study, half of the samples contained more aggressive cancers.
To create a 3D sample, researchers extract a “biopsy core” (a plug of cylindrical tissue) from a surgically resected prostate to mimic the typical staining used in 2D methods. Stained the biopsy core.Next, the team will take the entire biopsy core Open top light sheet microscopeUse a sheet of light to optically “slice” and image the tissue sample without destroying it.
3D images provided more information than 2D images. Specifically, it details the complex tree-like structure of the glands throughout the tissue. These additional features increase the likelihood that the computer will correctly predict cancer aggression.
Shown here is a video of the volume rendering of the glands in two 3D biopsy samples from the prostate (yellow: outer wall of the gland, red: fluid-filled space within the gland, purple: researchers What we called the “glandular skeleton”). A rod-shaped model of a space filled with fluid in the gland). The cancer sample (top) shows smaller, more dense glands compared to the benign tissue sample (bottom). Credits: Xie et al./Cancer Research
Researchers used new AI techniques, including deep learning image conversion technology, to help manage and interpret the large datasets generated by this project.
“For the past decade or so, our laboratory has focused primarily on building optical imaging devices, including microscopes for a variety of clinical applications, but the next big thing for clinical adoption. We have begun to face challenges: how to manage and interpret large datasets from patient specimens, “says Liu. “This paper represents the first work in our lab to develop a new computational pipeline for analyzing feature-rich datasets. Continuing to improve imaging and computational analysis techniques, and greater. We hope that conducting large-scale clinical research can transform the field of pathology and benefit many types of patients. “
The lead author of this paper is Weisi Xie, Doctor of Mechanical Engineering, University of Washington.Other co-authors of this paper Robert Serafin, Gangao, When Lindsey Burner, All UW Mechanical Engineering PhD students. Kevin Bishop, UW Bioengineering PhD student. Nicholas Ledder, Clinical Laboratory of UW School of Medicine Clinical Instructor in Medicine and Pathology. Hongyi Huang, UW research staff in mechanical engineering. Chenyi Mao, UW PhD student in Chemistry. Nadia Postpuna, Research Scientist in the Department of Clinical Laboratory Medicine and Pathology at the UW School of Medicine. Kang Seo Yeon, Professor of UW Assistant Teaching, Faculty of Mechanical Engineering. Qinghua Han, an undergraduate student at the University of Washington studying biotechnology. Jonathan Light, Professor of Urology at the Washington School of Medicine. C. Dark Keane When Lawrence True, Professor of both clinical laboratory medicine and pathology at the UW School of Medicine. Joshua Vaughan, Associate Professor of Chemistry UW. Adam Glazer, Senior Scientist at Allen Institute, who completed this research as a postdoctoral fellow in mechanical engineering at the University of Washington. Can you do it, Ping Fu Fu, Andrew Yanovchik When Anant Madabushi, All at Case Western Reserve University; Patrick Leo At Genentech, who completed this study as a PhD student at Case Western Reserve University. Sarah Holy of the Canary Foundation.
This study was funded by the Department of Defense’s Prostate Cancer Research Program. National Cancer Institute; National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; VA Merit Review Award; National Science Foundation; Nancy and Buster Albaboard Foundation. Prostate Cancer Foundation Young Researcher Award.
Nicholas Reder, Adam Glaser, Lawrence True and Jonathan Liu are co-founders and shareholders of the UW Spinout. Lightspeed Microscopy Inc. This company is licensed for the technology used in this white paper.
Please contact Liu for more information. [email protected]..
Grant numbers: W81XWH-18-10358, W81XWH-19-1-0589, W81XWH-15-1-0558, W81XWH-20-1-0851, K99 CA24068, R01CA244170, U24CA199374, R01CA249992, R01CA202752, R01CA208236, R01CA216579, R01CA220581 R01CA257612 , U01CA239055, U01CA248226, U54CA254566, R01HL151277, R01EB031002, R43EB028736, R01MH115767, IBX004121A, 1934292 HDR: I-DIRSE-FW, DGE-1762114, DGE-1762114
day to day): Faculty of Engineering •• Department of Biological Engineering •• Department of Laboratory Medicine and Pathology •• Department of mechanical engineering •• Jonathan Liu •• Lawrence True •• School of medicine
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