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Clinical Laboratories and Pathology Groups

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Clinical Laboratories and Pathology Groups

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US and UK Researchers Simultaneously Develop New Tests to Detect Prostate Cancer

Though still in trials, early results show tests may be more accurate than traditional clinical laboratory tests for detecting prostate cancer

Within weeks of each other, different research teams in the US and UK published findings of their respective efforts to develop a better, more accurate clinical laboratory prostate cancer test. With cancer being a leading cause of death among men—second only to heart disease according to the Centers for Disease Control and Prevention (CDC)—new diagnostics to identify prostate cancer would be a boon to precision medicine treatments for the deadly disease and could save many lives.

Researchers at the University of East Anglia (UEA) in Norwich, England, were working to improve the accuracy of the widely-used and accepted prostate-specific antigen (PSA) test. By contrast, researchers at Cedars-Sinai Cancer in Los Angeles, pursued a new liquid biopsy approach to identifying prostate cancer that uses nanotechnology.

Thus, these are two different pathways toward the goal of achieving earlier, more accurate diagnosis of prostate cancer, the holy grail of prostate cancer diagnosis.

Dmitry Pshezhetskiy, PhD

“There is currently no single test for prostate cancer, but PSA blood tests are among the most used, alongside physical examinations, MRI scans, and biopsies,” said Dmitry Pshezhetskiy, PhD (above), Professorial Research Fellow at University of East Anglia and one of the authors of the UEA study. “However, PSA blood tests are not routinely used to screen for prostate cancer, as results can be unreliable. Only about a quarter of people who have a prostate biopsy due to an elevated PSA level are found to have prostate cancer. There has therefore been a drive to create a new blood test with greater accuracy.” With the completion of the US and UK studies, clinical laboratories may soon have a new diagnostic test for prostate cancer. (Photo copyright: University of East Anglia.)

East Anglia’s Research into a More Accurate Blood Test

Scientists at the University of East Anglia (UEA) worked with researchers from Imperial College in London, Imperial College NHS Trust, and Oxford BioDynamics to develop a new precision medicine blood test that can detect prostate cancer with greater accuracy than current methods.

The epigenetic blood test they developed, called Prostate Screening EpiSwitch (PSE), can identify cancer-specific chromosome conformations in blood samples. The test works in tandem with the standard prostate-specific antigen (PSA) blood test to diagnose prostate cancer, according to an Oxford BioDynamics press release.

The researchers evaluated their test in a pilot study involving 147 patients. They found their testing method had a 94% accuracy rate, which is higher than that of PSA testing alone. They discovered their test significantly improved the overall detection of prostate cancer in men who are at risk for the disease. 

“When tested in the context of screening a population at risk, the PSE test yields a rapid and minimally invasive prostate cancer diagnosis with impressive performance,” Dmitry Pshezhetskiy, PhD, Professorial Research Fellow at UEA and one of the authors of the study told Science Daily. “This suggests a real benefit for both diagnostic and screening purposes.”

The UK scientists hope their test can eventually be used in everyday clinical practice as there is a need for a highly accurate method for prostate cancer screening that does not subject patients to unnecessary, costly, invasive procedures. 

The UEA researchers published their findings in the peer-reviewed journal Cancers, titled, “Circulating Chromosome Conformation Signatures Significantly Enhance PSA Positive Predicting Value and Overall Accuracy for Prostate Cancer Detection.”

Cedars-Sinai’s Research into Nanotechnology Cancer Testing

Researchers from Cedars-Sinai Cancer took a different approach to diagnosing prostate cancer by developing a nanotechnology-based liquid biopsy test that detects the disease even in microscopic amounts.  

Their test isolates and identifies extracellular vesicles (EVs) from blood samples. EVs are microscopic non-reproducing protein and genetic material shed by all cells. Cedars-Sinai’s EV Digital Scoring Assay accurately extracts EVs from blood and analyzes them faster than similar currently available tests.

“This research will revolutionize the liquid biopsy in prostate cancer,” said oncologist Edwin Posadas, MD, Medical Director of the Urologic Oncology Program and co-director of the Experimental Therapeutics Program in Cedars-Sinai Cancer in a press release. “The test is fast, minimally invasive and cost-effective, and opens up a new suite of tools that will help us optimize treatment and quality of life for prostate cancer patients.”

The researchers tested blood samples from 40 patients with prostate cancer. They found that their EV test could distinguish between cancer localized to the prostate and cancer that has spread to other parts of the body.

Microscopic cancer deposits, called micrometastases, are not always detectable, even with advanced imaging methods. When these deposits spread outside the prostate area, focused radiation cannot prevent further progression of the disease. Thus, the ability to identify cancer by locale within the body could lead to new precision medicine treatments for the illness.

“[The EV Digital Scoring Assay] would allow many patients to avoid the potential harms of radiation that isn’t targeting their disease, and instead receive systemic therapy that could slow disease progression,” Posadas explained.

The Cedars-Sinai researchers published their findings in Nano Today, titled, “Prostate Cancer Extracellular Vesicle Digital Scoring Assay: A Rapid Noninvasive Approach for Quantification of Disease-relevant mRNAs.”

Other Clinical Laboratory Tests for Prostate Cancer Under Development

According to the American Cancer Society, the number of prostate cancer cases is increasing. One out of eight men will be diagnosed with the illness during his lifetime. Thus, developers have been working on clinical laboratory tests to accurately detect the disease and save lives for some time.

In “University of East Anglia Researchers Develop Non-Invasive Prostate Cancer Urine Test,” Dark Daily reported on a urine test also developed by scientists at the University of East Anglia that clinical laboratories can use to not only accurately diagnose prostate cancer but also determine whether it is an aggressive form of the disease.

And in “UPMC Researchers Develop Artificial Intelligence Algorithm That Detects Prostate Cancer with ‘Near Perfect Accuracy’ in Effort to Improve How Pathologists Diagnose Cancer ,” we outlined how researchers at the University of Pittsburgh Medical Center (UPMC) working with Ibex Medical Analytics in Israel had developed an artificial intelligence (AI) algorithm for digital pathology that can accurately diagnose prostate cancer. In the initial study, the algorithm—dubbed the Galen Prostate AI platform—accurately detected prostate cancer with 98% sensitivity and 97% specificity.

More research and clinical trials are needed before the new US and UK prostate cancer testing methods will be ready to be used in clinical settings. But it’s clear that ongoing research may soon produce new clinical laboratory tests and diagnostics for prostate cancer that will steer treatment options and allow for better patient outcomes.  

—JP Schlingman

Related Information:

The New Prostate Cancer Blood Test with 94 Percent Accuracy

Circulating Chromosome Conformation Signatures Significantly Enhance PSA Positive Predicting Value and Overall Accuracy for Prostate Cancer Detection

Invention: A Blood Test to Unlock Prostate Cancer Mysteries

Prostate Cancer Extracellular Vesicle Digital Scoring Assay: A Rapid Noninvasive Approach for Quantification of Disease-relevant mRNAs

Could a Urine Test Detect Pancreatic and Prostate Cancer? Study Shows 99% Success Rate

University of East Anglia Researchers Develop Non-Invasive Prostate Cancer Urine Test

UPMC Researchers Develop Artificial Intelligence Algorithm That Detects Prostate Cancer with ‘Near Perfect Accuracy’ in Effort to Improve How Pathologists Diagnose Cancer

Studies Show Potential for Google’s AI Tools to Improve Pathologist Accuracy and Efficiency

Computer-assisted analysis using Google’s LYNA algorithm shows significant gains in speed required to analyze stained lymph node slides and sensitivity of micrometastases detection in two recent studies

Anatomic pathologists understand the complexities of reviewing slides and samples for signs of cancer’s spread. Two studies involving a new artificial intelligence (AI) algorithm from Google (NASDAQ:GOOGL) claim their “deep learning” LYmph Node Assistant (LYNA) provides increases to both the speed at which pathologists can analyze slides and improved accuracy in detecting metastatic breast cancer within the slide samples used for the studies.

Google’s first study was published in the Archives of Pathology and Laboratory Medicine and investigated the accuracy of the algorithm using digital pathology slides. Google’s second study, published in The American Journal of Surgical Pathology, looked at how pathologists might harness the algorithm to improve workflows and use the tool in a clinical setting.

Medical laboratories and other diagnostics providers are already familiar with the improvement potential of automation and other technology-based approaches to diagnosis and analysis. Google’s LYNA is an example of how AI and machine learning improvements can serve as a supplement to—not a replacement for—the skills of experts at pathology groups and clinical laboratories.

Early research done by Google indicates that integrating LYNA into existing workflows could allow pathologists to spend less time analyzing slides for minute details. Instead, they could focus on other more challenging tasks while the AI analyzes gigapixels worth of slide data to highlight regions of concern in slides and samples for deeper manual inspection.

LYNA Achieves 99% Accuracy in Study of Metastatic Breast Cancer Detection

According to the research cited in a Google AI Blog post, roughly 25% of metastatic lymph node staging classifications would change if subjected to a second pathologic review. They further note that when faced with time constraints, detection sensitivity for small metastases on individual slides can be as low as 38%.

In findings published in Archives of Pathology and Laboratory Medicine, Google researchers analyzed whole slide images from hematoxylin-eosin-stained lymph nodes for 399 patients sourced from the Camelyon16 challenge dataset. Of those slides, researchers used 270 to train LYNA and the remaining 129 for analysis. They then compared the LYNA findings to those of an independent lab using a different scanner.

“LYNA achieved a slide-level area under the receiver operating characteristic (AUC) of 99% and a tumor-level sensitivity of 91% at one false positive per patient on the Camelyon16 evaluation dataset,” the researchers stated. “We also identified [two] ‘normal’ slides that contained micrometastases.”

Google’s algorithm later received an AUC of 99.6% on a secondary dataset.

“Artificial intelligence algorithms can exhaustively evaluate every tissue patch on a slide, achieving higher tumor-level sensitivity than, and comparable slide-level performance to, pathologists,” the researchers continued. “These techniques may improve the pathologist’s productivity and reduce the number of false negatives associated with morphologic detection of tumor cells.”

Left: sample view of a slide containing lymph nodes, with multiple artifacts: the dark zone on the left is an air bubble, the white streaks are cutting artifacts, the red hue across some regions are hemorrhagic (containing blood), the tissue is necrotic (decaying), and the processing quality was poor. Right: LYNA identifies the tumor region in the center (red), and correctly classifies the surrounding artifact-laden regions as non-tumor (blue). (Image and caption copyright: Google AI Blog.)

Faster Analysis through Software Assistance

Rapid diagnosis helps improve cancer outcomes. Yet, manually reviewing and analyzing complex digital slides is time-consuming. Time constraints might also lead to false negatives due to micrometastases or small suspicious regions that slip by pathologists undetected.

The Google research team of the study published in The American Journal of Surgical Pathology sought to gauge the impact LYNA might have on the histopathologic review of lymph nodes for trained pathologists. In their multi-reader multi-case study, researchers analyzed differences in both sensitivity of detecting micrometastases and the average review time per image using both computer-aided detection and unassisted detection for six pathologists across 70 slides.

Using the LYNA algorithm to identify and outline regions likely to contain tumors, the researchers found that sensitivity increased from 83% to 91%. The time to review slides also saw a significant reduction from 116 seconds in the unassisted mode to 61 seconds in the assisted mode—a time savings of roughly 47%.

“Although some pathologists in the unassisted mode were less sensitive than LYNA,” the researchers stated, “all pathologists performed better than the algorithm alone in regard to both sensitivity and specificity when reviewing images with assistance.”

The Future of Digital Pathology using LYNA

While the two studies show positive results, both studies also reveal shortcomings. Google highlighted both limited dataset sizes and simulated diagnostic workflows as potential concerns and areas on which to focus future studies.

Still, Google’s researchers believe that algorithms such as LYNA will help to power the future of diagnostics as healthcare in the digital era continues to mature. “We remain optimistic,” state the authors of the Google AI Blog post, “that carefully validated deep learning technologies and well-designed clinical tools can help improve both the accuracy and availability of pathologic diagnosis around the world.”

While other industries see risk in the growth of AI, both studies performed by researchers at Google show how computer-assisted workflows and machine learning could accentuate and bolster the skills of trained diagnosticians, such as anatomic pathologists and clinical laboratory technicians. By working to compensate for weak points in both human skill and computer reasoning, the outcome could be greater than either AI or humans can achieve separately.

—Jon Stone

Related Information:

Google Creates AI to Detect When Breast Cancer Spreads

Google Deep Learning Tool 99% Accurate at Breast Cancer Detection

Google’s AI Software Seeks to Detect Advanced Breast Cancer Better Than We Have Before

Google’s AI Is Better at Spotting Advanced Breast Cancer than Pathologists

Google AI Claims 99% Accuracy in Metastatic Breast Cancer Detection

Applying Deep Learning to Metastatic Breast Cancer Detection

Assisting Pathologists in Detecting Cancer with Deep Learning

Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women with Breast Cancer