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Clinical Laboratories and Pathology Groups

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News, Analysis, Trends, Management Innovations for
Clinical Laboratories and Pathology Groups

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UK Study Claims AI Reading of CT Scans Almost Twice as Accurate at Grading Some Cancers as Clinical Laboratory Testing of Sarcoma Biopsies

Radiological method using AI algorithms to detect, locate, and identify cancer could negate the need for invasive, painful clinical laboratory testing of tissue biopsies

Clinical laboratory testing of cancer biopsies has been the standard in oncology diagnosis for decades. But a recent study by the Institute of Cancer Research (ICR) and the Royal Marsden NHS Foundation Trust in the UK has found that, for some types of sarcomas (malignant tumors), artificial intelligence (AI) can grade the aggressiveness of tumors nearly twice as accurately as lab tests, according to an ICR news release.

This will be of interest to histopathologists and radiologist technologists who are working to develop AI deep learning algorithms to read computed tomography scans (CT scans) to speed diagnosis and treatment of cancer patients.

“Researchers used the CT scans of 170 patients treated at The Royal Marsden with the two most common forms of retroperitoneal sarcoma (RPS)—leiomyosarcoma and liposarcoma—to create an AI algorithm, which was then tested on nearly 90 patients from centers across Europe and the US,” the news release notes.

The researchers then “used a technique called radiomics to analyze the CT scan data, which can extract information about the patient’s disease from medical images, including data which can’t be distinguished by the human eye,” the new release states.

The scientists published their findings in The Lancet Oncology titled, “A CT-based Radiomics Classification Model for the Prediction of Histological Type and Tumor Grade in Retroperitoneal Sarcoma (RADSARC-R): A Retrospective Multicohort Analysis.”

The research team sought to make improvements with this type of cancer because these tumors have “a poor prognosis, upfront characterization of the tumor is difficult, and under-grading is common,” they wrote. The fact that AI reading of CT scans is a non-invasive procedure is major benefit, they added.

Christina Messiou, MD

“This is the largest and most robust study to date that has successfully developed and tested an AI model aimed at improving the diagnosis and grading of retroperitoneal sarcoma using data from CT scans,” said the study’s lead oncology radiologist Christina Messiou, MD, (above), Consultant Radiologist at The Royal Marsden NHS Foundation Trust and Professor in Imaging for Personalized Oncology at The Institute of Cancer Research, London, in a news release. Invasive medical laboratory testing of cancer biopsies may eventually become a thing of the past if this research becomes clinically available for oncology diagnosis. (Photo copyright: The Royal Marsden.)

Study Details

RPS is a relatively difficult cancer to spot, let alone diagnose. It is a rare form of soft-tissue cancer “with approximately 8,600 new cases diagnosed annually in the United States—less than 1% of all newly diagnosed malignancies,” according to Brigham and Women’s Hospital.

In their published study, the UK researchers noted that, “Although more than 50 soft tissue sarcoma radiomics studies have been completed, few include retroperitoneal sarcomas, and the majority use single-center datasets without independent validation. The limited interpretation of the quantitative radiological phenotype in retroperitoneal sarcomas and its association with tumor biology is a missed opportunity.”

According to the ICR news release, “The [AI] model accurately graded the risk—or how aggressive a tumor is likely to be—[in] 82% of the tumors analyzed, while only 44% were correctly graded using a biopsy.”

Additionally, “The [AI] model also accurately predicted the disease type [in] 84% of the sarcomas tested—meaning it can effectively differentiate between leiomyosarcoma and liposarcoma—compared with radiologists who were not able to diagnose 35% of the cases,” the news release states.

“There is an urgent need to improve the diagnosis and treatment of patients with retroperitoneal sarcoma, who currently have poor outcomes,” said the study’s first author Amani Arthur, PhD, Clinical Research Fellow at The Institute of Cancer Research, London, and Registrar at The Royal Marsden NHS Foundation Trust, in the ICR news release.

“The disease is very rare—clinicians may only see one or two cases in their career—which means diagnosis can be slow. This type of sarcoma is also difficult to treat as it can grow to large sizes and, due to the tumor’s location in the abdomen, involve complex surgery,” she continued. “Through this early research, we’ve developed an innovative AI tool using imaging data that could help us more accurately and quickly identify the type and grade of retroperitoneal sarcomas than current methods. This could improve patient outcomes by helping to speed up diagnosis of the disease, and better tailor treatment by reliably identifying the risk of each patient’s disease.

“In the next phase of the study, we will test this model in clinic on patients with potential retroperitoneal sarcomas to see if it can accurately characterize their disease and measure the performance of the technology over time,” Arthur added.

Importance of Study Findings

Speed of detection is key to successful cancer diagnoses, noted Richard Davidson, Chief Executive of Sarcoma UK, a bone and soft tissue cancer charity.

“People are more likely to survive sarcoma if their cancer is diagnosed early—when treatments can be effective and before the sarcoma has spread to other parts of the body. One in six people with sarcoma cancer wait more than a year to receive an accurate diagnosis, so any research that helps patients receive better treatment, care, information and support is welcome,” he told The Guardian.

According to the World Health Organization, cancer kills about 10 million people worldwide every year. Acquisition and medical laboratory testing of tissue biopsies is both painful to patients and time consuming. Thus, a non-invasive method of diagnosing deadly cancers quickly, accurately, and early would be a boon to oncology practices worldwide and could save thousands of lives each year.

—Kristin Althea O’Connor

Related Information:

AI Twice as Accurate as a Biopsy at Grading Aggressiveness of Some Sarcomas

AI Better than Biopsy at Assessing Some Cancers, Study Finds

AI Better than Biopsies for Grading Rare Cancer, New Research Suggests

A CT-based Radiomics Classification Model for the Prediction of Histological Type and Tumor Grade in Retroperitoneal Sarcoma (RADSARC-R): A Retrospective Multicohort Analysis

Breath Sample Is Used by Opteev Technologies’ Combined COVID/Influenza/RSV Screening Device with 95% Sensitivity and 90% Specificity

Clinical laboratories and point-of-care settings may have a new diagnostic test if this novel handheld device and related technology is validated by clinical trials

Efforts to develop breath analyzers that accurately identify viral infections, such as SARS-CoV-2 and Influenza, have been ongoing for years. The latest example is ViraWarn from Opteev Technologies in Baltimore, Maryland, and its success could lead to more follow-up PCR tests performed at clinical laboratories.

ViraWarn is a pocket-size breath analyzer that detects COVID-19, influenza, and respiratory syncytial virus (RSV) in about a minute, according to an Opteev news release. The technology company just submitted ViraWarn to the US Food and Drug Administration (FDA) for Pre-Emergency Use Authorization (Pre-EUA).

“Breath is one of the most appealing non-invasive sample types for diagnosis of infectious and non-infectious disease,” said Opteev in its FDA Pre-EUA application. “Exhaled breath is very easy to provide and is less prone to user errors. Breath contains a number of biomarkers associated with different ailments that include volatile organic compounds (VOCs), viruses, bacteria, antigens, and nucleic acid.”

Further clinical trials and the FDA Pre-EUA are needed before ViraWarn can be made available to consumers. In the meantime, Opteev announced that the CES (Consumer Electronic Show) had named ViraWarn as a 2023 Innovation Award Honoree in the digital health category. 

Conrad Bessemer

“ViraWarn is designed to allow users an ultra-fast and convenient way to know if they are spreading a dangerous respiratory virus. With a continued increase in COVID-19 and a new surge in RSV and influenza cases, we’re eager to bring ViraWarn to market so consumers can easily blow into a personal device and find out if they are positive or negative,” said Conrad Bessemer (above), Opteev President and Co-Founder, in a news release.

Opteev is a subsidiary of Novatec, a supplier of machinery and sensor technology, and a sister company to Prophecy Sensorlytics, a wearable sensors company. 

The ViraWarn breath analyzer uses a silk-based sensor that “traces the electric discharge of respiratory viruses coupled with an artificial intelligence (AI) processor to filter out any potential inaccuracies,” according to the news release.

Here is how the breath analyzer (mouthpiece, attached biosensor chamber, and attached printed circuit board chamber) is deployed by a user, according to the Opteev website:

  • The user turns on the device and an LED light indicates readiness.
  • The user blows twice into the mouthpiece.
  • A carbon filter stops bacteria and VOCs and allows virus particles to pass through.
  • As “charge carriers,” virus particles have a “cumulative charge.”
  • In a biosensor chamber, virus particles create a change in “electrical resistivity.”
  • Electrical data are forwarded to the AI processor.
  • The AI processer delivers a result.
  • Within 60 seconds, a red signal indicates a positive presence of a virus and a green signal indicates negative one.

“The interaction of the virus with a specially designed liquid semiconductive medium, or a solid polymer semiconductor, generates changes in the conductivity of the electrical biosensor, which can then be picked up by electrodes. Such electrical data can be analyzed using algorithms and make a positive or negative call,” explains an Opteev white paper on the viral screening process.

While the ViraWarn breath analyzer can identify the presence of a virus, it cannot distinguish between specific viruses, the company noted. Therefore, a clinical laboratory PCR test is needed to confirm results.

Other Breath Tests

Opteev is not the only company developing diagnostic tests using breath samples.

In “Will Blowing in a Device Be Useful in Screening for COVID-19? FDA Grants Its First EUA for a Breathalyzer SARS-CoV-2 Screening Test,” Dark Daily reported on the FDA issuing an EUA in 2022 for the InspectIR COVID-19 Breathalyzer, the first test to detect compounds in breath samples linked to SARS-CoV-2 infection, an FDA statement noted.

And in “NIST Scientists Enhance Frequency Comb Breathalyzer Enabling It to Detect Multiple Disease Biomarkers,” we covered how researchers at JILA, a research center jointly operated by the National Institutes of Standards and Technology (NIST) and the University of Colorado Boulder, have developed a breath test that can detect and monitor four disease biomarkers at one time with the potential to identify six more.

For clinical laboratory managers and pathologists, Opteev’s ViraWarn is notable in breath diagnostics development because it is a personal hand-held tool. It empowers people to do self-tests and other disease screenings, all of which would need to be confirmed with medical laboratory testing in the case of positive results.

Further, it is important to understand that consumers are the primary target for this novel diagnostic device. This is consistent with investor-funding companies wanting to develop testing solutions that can be used by consumers. At the same time, a device like ViraWarn could be used by clinical laboratories in their patient service centers to provide rapid test results.  

Donna Marie Pocius

Related Information:

Pocket-Sized Breath Analyzer Detects COVID-19, RSV, Influenza in Under 60 Seconds

COVID-19, RSV, and Influenza Breath Analyzer, ViraWarn, Wins CES 2023 Innovation Award

Baltimore Company Launches Device That Detects COVID-19, Flu

ViraWarn Pre-EUA Application

The Missing Piece in the Fight Against the Pandemic is Finally Here: The Evolution of Screening for COVID-19

FDA Authorizes First COVID-19 Diagnostic Test Using Breath Samples

Will Blowing in a Device Be Useful in Screening for COVID-19? FDA Grants Its First EUA for a Breathalyzer SARS-CoV-2 Screening Test

NIST Scientists Enhance Frequency Comb Breathalyzer Enabling It to Detect Multiple Disease Biomarkers

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