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December 9, 2021
Prostate cancer is the most common cancer in men and, for men in the United States, it is the second leading cause of death.
Some prostate cancers can grow slowly and can be monitored over time, while others need to be treated immediately. To determine how aggressive a person’s cancer is, doctors look for abnormalities in slices of tissue biopsied on a slide. But this 2D method makes the correct diagnosis of borderline cases difficult.
Today, a team led by the University of Washington has developed a new non-destructive method that images entire 3D biopsies instead of a single slice. In a proof-of-principle experiment, researchers imaged 300 3D biopsies taken from 50 patients – six biopsies per patient – and used a computer to use the 3D and 2D results to predict the likelihood of a patient having cancer. aggressive. The 3D capabilities made it easier for the computer to identify the cases most likely to recur within five years.
The team published these results December 1 in cancer research.
“We show for the first time that compared to traditional pathology – where a small fraction of each biopsy is examined in 2D on microscope slides – the ability to examine 100% of a biopsy in 3D is more informative and accurate “said the lead author Jonathan liu, a UW professor of mechanical engineering and bioengineering. âThis is exciting because this is, hopefully, the first of many clinical studies that will demonstrate the value of non-destructive 3D pathology for clinical decision-making, such as determining which patients require aggressive treatment or which under. – sets of patients would respond best to certain drugs. . “
The researchers used prostate samples from patients who had surgery more than 10 years ago, so the team knew each patient’s outcome and could use that information to train a computer to predict those outcomes. In this study, half of the samples contained more aggressive cancer.
To create 3D samples, the researchers extracted “biopsy nuclei” – cylindrical-shaped plugs of tissue – from surgically removed prostates, then stained the biopsy nuclei to mimic the typical staining used in the 2D method. Next, the team imaged each entire biopsy core using an open-top lumen sheet microscope, which uses a lumen sheet to optically “slice” and image a tissue sample without destroying it.
The 3D images provided more information than a 2D image – in particular, details about the complex tree structure of glands throughout the tissue. These additional features increased the likelihood that the computer correctly predicted the aggressiveness of cancer.
Here is a video of a volume rendering of the glands in two 3D prostate biopsy samples (yellow: the outer walls of the gland; red: the fluid-filled space inside the gland; purple: what the researchers called it the “skeleton of the gland,” a stick-shaped model of the fluid-filled spaces inside the glands). The cancer sample (top) shows smaller, denser glands than the benign tissue sample (bottom). Credit: Xie et al./Cancer Research
The researchers used new AI methods, including deep learning image transformation techniques, to help manage and interpret the large datasets generated by this project.
âOver the past decade or so, our laboratory has focused primarily on building optical imaging devices, including microscopes, for a variety of clinical applications. However, we started to face the next big challenge towards clinical adoption: how to manage and interpret the massive datasets that we acquire from patient samples, âsaid Liu. âThis article represents the first study in our lab to develop a new computational pipeline to analyze our feature-rich datasets. As we continue to refine our imaging technologies and computational analysis methods, and complete larger clinical studies, we hope that we can help transform the field of pathology for the benefit of many types of patients. “
The main author of this article is Weisi Xie, doctoral student in mechanical engineering at UW. The other co-authors of this article are Robert SÃ©rafin, Gan Gao, and Lindsey barner, all doctoral students in mechanical engineering at UW; Kevin bishop, doctoral student in bioengineering at UW; Nicolas reder, clinical instructor in the department of laboratory medicine and pathology at the UW School of Medicine; Hongyi Huang, UW Mechanical Engineering Research Staff; Chenyi Mao, a UW doctoral student in the chemistry department; Nadia postupna, Research Scientist in the Department of Laboratory Medicine and Pathology at the UW School of Medicine; Soyoung Kang, assistant professor at UW in the department of mechanical engineering; Qinghua Han, an undergraduate student at UW studying bioengineering; Jonathan wright, professor in the department of urology at the UW School of Medicine; C. Dirk Keene and Laurent True, both professors in the Department of Laboratory Medicine and Pathology at the UW School of Medicine; Joshua Vaughan, associate professor of chemistry at UW; Adam glaser, a senior scientist at the Allen Institute who completed this research as a postdoctoral researcher in mechanical engineering at UW; Can Koyuncu, Pingfu fu, Andrew Janowczyk and Anant Madabhushi, all at Case Western Reserve University; Patrick Leo to Genentech, who completed this research as a doctoral student at Case Western Reserve University; and Sarah Hawley at the Canary Foundation.
This research was funded by the Department of Defense Prostate Cancer Research Program; the National Cancer Institute; the National Institute of Heart, Lungs and Blood; the National Institute of Biomedical Imaging and Bioengineering; the National Institute of Mental Health; the VA Merit Review Award; the National Science Foundation; the Nancy and Buster Alvord endowment; and the Prostate Cancer Foundation Young Investigator Award.
Nicholas Reder, Adam Glaser, Lawrence True and Jonathan Liu are co-founders and shareholders of the UW spin-out Lightspeed Microscopy Inc. This company has licensed the technology used in this document.
For more information, contact Liu at [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, R01CA R01CA2162365, R01CA2082365, R01CA2082365 , R01CA257612, U01CA239055, U01CA248226, U54CA254566, R01HL151277, R01EB031002, R43EB028736, R01MH115767, IBX004121A, 1934292 HDR: I-DIRSE-FW, DGE-1762114, DGE-1762114, DGE
Tag (s): College of Engineering â¢ Department of Bioengineering â¢ Department of Laboratory Medicine and Pathology â¢ Department of Mechanical Engineering â¢ Jonathan Liu â¢ Lawrence True â¢ School of Medicine