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Clinical Laboratories and Pathology Groups

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Clinical Laboratories and Pathology Groups

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Rice University Researchers Are Developing an Implantable Cancer Therapeutic Device That May Reduce Cancer Deaths by Half

Immunotherapy device could also enable clinical laboratories to receive in vivo biomarker data wirelessly

Researchers from Rice University in Houston and seven other states in the US are working on a new oncotherapy sense-and-respond implant that could dramatically improve cancer outcomes. Called Targeted Hybrid Oncotherapeutic Regulation (THOR), the technology is intended primarily for the delivery of therapeutic drugs by monitoring specific cancer biomarkers in vivo.

Through a $45 million federal grant from the Advanced Research Projects Agency for Health (ARPA-H), the researchers set out to develop an immunotherapy implantable device that monitors a patient’s cancer and adjusts antibody treatment dosages in real time in response to the biomarkers it measures.

It’s not a far stretch to envision future versions of the THOR platform also being used diagnostically to measure biomarker data and transmit it wirelessly to clinical laboratories and anatomic pathologists.

ARPH-A is a federal funding agency that was established in 2022 to support the development of high-impact research to drive biomedical and health breakthroughs. THOR is the second program to receive funding under its inaugural Open Broad Agency Announcement solicitation for research proposals. 

“By integrating a self-regulated circuit, the THOR technology can adjust the dose of immunotherapy reagents based on a patient’s responses,” said Weiyi Peng, MD, PhD (above), Assistant Professor of Biology and Biochemistry at the University of Houston and co-principal investigator on the research, in a UH press release. “With this new feature, THOR is expected to achieve better efficacy and minimize immune-related toxicity. We hope this personalized immunotherapy will revolutionize treatments for patients with peritoneal cancers that affect the liver, lungs, and other organs.” If anatomic pathologists and clinical laboratories could receive biometric data from the THOR device, that would be a boon to cancer diagnostics. (Photo copyright: University of Houston.)

Antibody Therapy on Demand

Omid Veiseh, PhD, Associate Professor of Bioengineering at Rice University and principal investigator on the project, described the THOR device as a “living drug factory” inside the body. The device is a rod-like gadget that contains onboard electronics and a wireless rechargeable battery. It is three inches long and has a miniaturized bioreactor that contains human epithelial cells that have been engineered to produce immune modulating therapies.

“Instead of tethering patients to hospital beds, IV bags, and external monitors, we’ll use a minimally invasive procedure to implant a small device that continuously monitors their cancer and adjusts their immunotherapy dose in real time,” said Veiseh in a Rice University press release. “This kind of ‘closed-loop therapy’ has been used for managing diabetes, where you have a glucose monitor that continuously talks to an insulin pump.

But for cancer immunotherapy, it’s revolutionary.”

The team believes the THOR device will have the ability to monitor biomarkers and produce an antibody on demand that will trigger the immune system to fight cancer locally. They hope the sensor within THOR will be able to monitor biomarkers of toxicity for the purpose of fine-tuning therapies to a patient immediately in response to signals from a tumor. 

“Today, cancer is treated a bit like a static disease, which it’s not,” Veiseh said. “Clinicians administer a therapy and then wait four to six weeks to do radiological measurements to see if the therapy is working. You lose quite a lot of time if it’s not the right therapy. The tumor may have evolved into a more aggressive form.”

The THOR device lasts 60 days and can be removed after that time. It is designed to educate the immune system to recognize a cancer and prevent it from recurring. If the cancer is not fully eradicated after the first implantation, the patient can be implanted with THOR again. 

Use of AI in THOR Therapy

The researchers plan to spend the next two and a half years building prototypes of the THOR device, testing them in rodents, and refining the list of biomarkers to be utilized in the device. Then, they intend to take an additional year to establish protocols for the US Food and Drug Administration’s (FDA) good manufacturing practices requirements, and to test the final prototype on large animals. The researchers estimate the first human clinical trials for the device will begin in about four years. 

“The first clinical trial will focus on refractory recurrent ovarian cancer, and the benefit of that is that we have an ongoing trial for ovarian cancer with our encapsulated cytokine ‘drug factory’ technology,” said Veiseh in the UH press release. 

The group is starting with ovarian cancer because research in this area is lacking and it will provide the opportunity for THOR to activate the immune system against ovarian cancer, which is typically challenging to fight with immunotherapy approaches. If successful in ovarian cancer, the researchers hope to test THOR in other cancers that metastasize within the abdomen, such as:

All control and decision-making will initially be performed by a healthcare provider based on signals transmitted by THOR using a computer or smartphone. However, Veiseh sees the device ultimately being powered by artificial intelligence (AI) algorithms that could independently make therapeutic decisions.

“As we treat more and more patients [with THOR], the devices are going to learn what type of biomarker readout better predicts efficacy and toxicity and make adjustments based on that,” he predicted. “Between the information you have from the first patient versus the millionth patient you treat, the algorithm is just going to get better and better.”

Moving Forward

In addition to UH and Rice University, scientists working on the project come from several institutions, including:

More research and clinical trials are needed before THOR can be used in the clinical treatment of cancer patients. If the device reaches the commercialization stage, Veiseh plans to either form a new company or license the technology to an existing company for further development.

“We know that the further we advance it in terms of getting that human data, the more likely it is that this could then be transferred to another entity,” he told Precision Medicine Online.

Pathologists and clinical laboratories will want to monitor the progress of the THOR technology’s ability to sense changes in cancer biomarkers and deliver controlled dosages of antibiotic treatments.

—JP Schlingman

Related Information:

UH Researcher on Team Developing Sense-and-Respond Cancer Implant Technology

Feds Fund $45M Rice-Led Research That Could Slash US Cancer Deaths by 50%

$45M Awarded to Develop Sense-and-Respond Implant Technology for Cancer Treatment

Implantable Oncotherapeutic Bioreactor Device Lands $45M Government Funding

ARPA-H Fast Tracks Development of New Cancer Implant Tech

ARPA-H Announces Funding for Programs to Support Cancer Moonshot Objectives

ARPA-H Fast Tracks Development of New Cancer Implant Tech

Feds Investing Nearly $115 Million in Three New Cancer Technology Research Projects

Hopkins Engineers Join $45M Project to Develop Sense-and-Respond Cancer Implant Technology

ARPA-H Projects Aim to Develop Novel Cancer Technologies

Closed-Loop Insulin Delivery Systems: Past, Present, and Future Directions

Researchers Create Artificial Intelligence Tool That Accurately Predicts Outcomes for 14 Types of Cancer

Scientists in Italy Develop Hierarchical Artificial Intelligence System to Analyze Bacterial Species in Culture Plates

New artificial intelligence model agrees with interpretations of human medical technologists and microbiologists with extraordinary accuracy

Microbiology laboratories will be interested in news from Brescia University in Italy, where researchers reportedly have developed a deep learning model that can visually identify and analyze bacterial species in culture plates with a high level of agreement with interpretations made by medical technologists.

They initially trained and tested the system to digitally identify pathogens associated with urinary tract infections (UTIs). UTIs are the source for a large volume of clinical laboratory microbiological testing.

The system, known as DeepColony, uses hierarchical artificial intelligence technology. The researchers say hierarchical AI is better suited to complex decision-making than other approaches, such as generative AI.

The researchers published their findings in the journal Nature titled, “Hierarchical AI Enables Global Interpretation of Culture Plates in the Era of Digital Microbiology.”

In their Nature paper, the researchers explained that microbiologists use conventional methods to visually examine culture plates that contain bacterial colonies. The scientists hypothesize which species of bacteria are present, after which they test their hypothesis “by regrowing samples from each colony separately and then employing mass spectroscopy techniques,” to confirm their hypotheses.

However, DeepColony—which was designed for use with clinical laboratory automation systems—looks at high-resolution digital scans of cultured plates and attempts to identify the bacterial strains and analyze them in much the same way a microbiologist would. For example, it can identify species based on their appearance and determine which colonies are suitable for analysis, the researchers explained.

“Working on a large stream of clinical data, and a complete set of 32 pathogens, the proposed system is capable of effectively assisting plate interpretation with a surprising degree of accuracy in the widespread and demanding framework of urinary tract infections,” the study authors wrote. “Moreover, thanks to the rich species-related generated information, DeepColony can be used for developing trustworthy clinical decision support services in laboratory automation ecosystems from local to global scale.”

Alberto Signoroni, PhD

“Compared to the most common solutions based on single convolutional neural networks (CNN), multi-network architectures are attractive in our case because of their ability to fit into contexts where decision-making processes are stratified into a complex structure,” wrote the study’s lead author Alberto Signoroni, PhD (above), Associate Professor of Computer Science, University of Brescia, and his researcher team in their Nature paper. “The system must be designed to generate useful and easily interpretable information and to support expert decisions according to safety-by-design and human-in-the-loop policies, aiming at achieving cost-effectiveness and skill-empowerment respectively.” Microbiologists and clinical laboratory managers will want to follow the further development of this technology. (Photo copyright: University of Brescia.)

How Hierarchical AI Works

Writing in LinkedIn, patent attorney and self-described technology expert David Cain, JD, of Hauptman Ham, LLP, explained that hierarchical AI systems “are structured in layers, each with its own distinct role yet interconnected in a way that forms a cohesive whole. These systems are significant because they mirror the complexity of human decision-making processes, incorporating multiple levels of analysis and action. This multi-tiered approach allows for nuanced problem-solving and decision-making, akin to a seasoned explorer deftly navigating through a multifaceted terrain.”

DeepColony, the researchers wrote, consists of multiple convolutional neural networks (CNNs) that exchange information and cooperate with one another. The system is structured into five levels—labeled 0 through 4—each handling a different part of the analysis:

  • At level 0, the system determines the number of bacterial colonies and their locations on the plate.
  • At level 1, the system identifies “good colonies,” meaning those suitable for further identification and analysis.
  • At level 2, the system assigns each good colony to a bacterial species “based on visual appearance and growth characteristics,” the researchers wrote, referring to the determination as being “pathogen aware, similarity agnostic.”

The CNN used at this stage was trained by using images of 26,213 isolated colonies comprising 32 bacterial species, the researchers wrote in their paper. Most came from clinical laboratories, but some were obtained from the American Type Culture Collection (ATCC), a repository of biological materials and information resources available to researchers.

  • At level 3, the system attempts to improve accuracy by looking at the larger context of the plate. The goal here is to “determine if observed colonies are similar (pure culture) or different (mixed cultures),” the researchers wrote, describing this step as “similarity aware, pathogen agnostic.” This enables the system to recognize variants of the same strain, the researchers noted, and has the effect of reducing the number of strains identified by the system.

At this level, the system uses two “Siamese CNNs,” which were trained with a dataset of 200,000 image pairs.

Then, at level 4, the system “assesses the clinical significance of the entire plate,” the researchers added. Each plate is labeled as:

  • “Positive” (significant bacterial growth),
  • “No significant growth” (negative), or
  • “Contaminated,” meaning it has three or more “different colony morphologies without a particular pathogen that is prevalent over the others,” the researchers wrote.

If a plate is labeled as “positive,” it can be “further evaluated for possible downstream steps,” using MALDI-TOF mass spectrometry or tests to determine susceptibility to antimicrobial measures, the researchers stated.

“This decision-making process takes into account not only the identification results but also adheres to the specific laboratory guidelines to ensure a proper supportive interpretation in the context of use,” the researchers wrote.

Nearly 100% Agreement with Medical Technologists

To gauge DeepColony’s accuracy, the researchers tested it on a dataset of more than 5,000 urine cultures from a US laboratory. They then compared its analyses with those of human medical technologists who had analyzed the same samples.

Agreement was 99.2% for no-growth cultures, 95.6% for positive cultures, and 77.1% for contaminated or mixed growth cultures, the researchers wrote.

The lower agreement for contaminated cultures was due to “a deliberately precautionary behavior, which is related to ‘safety by design’ criteria,” the researchers noted.

Lead study author Alberto Signoroni, PhD, Associate Professor of Computer Science, University of Brescia, wrote in Nature that many of the plates identified by medical technologists as “contaminated” were labeled as “positive” by DeepColony. “We maximized true negatives while allowing for some false positives, so that DeepColony [can] focus on the most relevant or critical cases,” he said.

Will DeepColony replace medical technologists in clinical laboratories any time soon? Not likely. But the Brescia University study indicates the direction AI in healthcare is headed, with high accuracy and increasing speed. The day may not be far off when pathologists and microbiologists regularly employ AI algorithms to diagnose disease.

—Stephen Beale

Related Information:

Hierarchical AI Enables Global Interpretation of Culture Plates in the Era of Digital Microbiology

Hierarchical Deep Learning Neural Network (HiDeNN): An Artificial Intelligence (AI) Framework for Computational Science and Engineering

An AI System Helps Microbiologists Identify Bacteria

This AI Research Helps Microbiologists to Identify Bacteria

Deep Learning Meets Clinical Microbiology: Unveiling DeepColony for Automated Culture Plates Interpretation

University of Southern California Researchers Develop Vaccine That Boosts Immunity and Helps Patients Avoid Deadly Infections While in Hospitals

New vaccine could give clinical laboratories and antimicrobial stewardship programs the tool they need to dramatically reduce hospital-acquired infections

Healthcare providers and clinical laboratories continue to struggle against hospital-acquired infections (HAIs) and ever-evolving antimicrobial resistant (AMR) bacteria. But now, the University of Southern California (USC) has developed and patented an experimental vaccine that has been shown to protect against so-called “superbugs,” such as methicillin-resistant Staphylococcus aureus (MRSA), an AMR bacteria that causes potentially deadly staph infections in hospitals and other healthcare settings.

The innovative approach focuses on bolstering the patient’s immune system itself, rather than relying on proteins to fight infections, according to a USC Today article. 

Developed by senior study author Brad Spellberg, MD, Chief Medical Officer at the Los Angeles General Medical Center, and colleagues, “The experimental vaccine takes an entirely different approach: It gooses the body’s preexisting supply of pathogen-gobbling immune cells called macrophages, which engulf and digest bacteria, fungi, and other bad actors. These activated fighters, found in all tissues, quickly neutralize incoming invaders which might otherwise multiply rapidly and overwhelm the body’s defenses,” USC Today reported. 

“This is very different from developing new antibiotics,” Jun Yan, a doctoral student at Keck School of Medicine and the study’s first author, told USC Today. “This is using our own immune system to fight against different superbugs, which is a different approach than everybody else.”

To develop the vaccine [the USC researchers] formed a biotechnology startup called ExBaq LLC in Bethesda, Md.

They published their findings in the journal Science Translational Medicine title, “A Protein-Free Vaccine Stimulates Innate Immunity and Protects against Nosocomial Pathogens.”

Ishwar K. Puri, PhD

“The pandemic stimulated unprecedented innovation in vaccine development, where federal funding and university-industry partnerships were game changers for translating promising discoveries from academic labs for the good of all,” said Ishwar K. Puri, PhD (above), senior vice president of research and innovation at USC. “We are both pleased and proud of the critical support the USC Stevens Center provided to enable the development of ExBaq’s experimental vaccine that protects vulnerable populations from serious infections.” Clinical laboratories that work with hospitals in the fight against hospital-acquired infections understand the importance of this discovery. (Photo copyright: University of Southern California.)

USC Vaccine Details

The USC team developed a “protein-free vaccine, composed of aluminum hydroxide, monophosphoryl lipid A, and fungal mannan, that stimulates the innate immune system and confers protection,” the researchers wrote in Science Translational Medicine.

“Tested in two independent labs, the vaccine works within 24 hours and lasts for up to 28 days. In lab models, the number of pathogen-eating immune cells in the blood increased dramatically, and survival time of invasive blood and lung infections improved. Early data suggest that a second dose could extend the window to prevent infection,” USC Today reported.

Unlike anything currently available, the new vaccine focuses on boosting the body itself instead of creating antibodies against certain pathogens. A mere dose of the vaccine is described to “provide rapid protection against nine different bacteria and fungi species,” USC Today noted.

“It’s an early warning system. It’s like Homeland Security putting out a terror alert. Everybody, keep your eyes open. Keep an eye out for suspicious packages. You’re alerting the soldiers and tanks of your immune system. The vaccine activates them,” Spellberg told USC Today

“The vaccine acted through stimulation of the innate, rather than the adaptive, immune system, as demonstrated by efficacy in the absence of lymphocytes that were abrogated by macrophage depletion. A role for macrophages was further supported by the finding that vaccination induced macrophage epigenetic alterations that modulated phagocytosis and the inflammatory response to infection. Together, these data show that this protein-free vaccine is a promising strategy to prevent deadly antimicrobial-resistant healthcare-associated infections,” the researchers wrote in Science Translational Medicine.

Great Need for This Protection

According to the federal Centers for Disease Control and Prevention (CDC), 1.7 million infections and 99,000 deaths are caused by HAIs annually.

“Patients who acquire infections from surgery spend, on average, an additional 6.5 days in the hospital, are five times more likely to be readmitted after discharge and twice as likely to die. Moreover, surgical patients who develop infections are 60% more likely to require admission to a hospital’s intensive care unit. Surgical infections are believed to account for up to 10 billion dollars annually in healthcare expenditures,” the CDC reports.

“All hospitalized patients are susceptible to contracting a [hospital-acquired] infection. Some patients are at greater risk than others: young children, the elderly, and persons with compromised immune systems are more likely to get an infection. Other risk factors are long hospital stays, the use of indwelling catheters, failure of healthcare workers to wash their hands, and overuse of antibiotics,” the CDC notes.

Therefore, USC’s new vaccine may be just what the doctor ordered to protect patients in hospitals and other healthcare settings from deadly HAIs.

Looking Ahead

There are currently no vaccines that are FDA-approved that treat “the most serious antibiotic resistant infections,” USC Today reported.

“Even if there were such vaccines, multiple vaccines would have to be deployed simultaneously to protect against the full slate of antibiotic-resistant microbes that cause healthcare-acquired infections,” Brian Luna, PhD, assistant professor of molecular microbiology and immunology at USC’s Keck School of Medicine, told USC Today

Thus, USC’s new vaccine could be a boon to hospital antimicrobial stewardship programs. But so far, it has only been tested on mice.

“The next step is getting guidance from the US Food and Drug Administration (FDA) on the design of a clinical trial. The first such trial would be done in healthy volunteers to find the right dose of vaccine that is safe and triggers the same kind of immune response in people as seen in the mice,” USC Today reported.

ExBaq LLC has begun talking with potential larger partners who might be willing to help develop the vaccine into clinical testing.

For years hospitals and other healthcare settings—such as long-term care facilities, urgent care clinics, and clinical laboratories—have fought an uphill battle against superbugs. So, for a vaccine to be on the horizon that can prevent life-threatening hospital-acquired infections would be a game changer.

With antimicrobial stewardships being a requirement in all hospitals, medical laboratory managers and microbiologists may celebrate this new development and its potential to be a useful tool in fighting antimicrobial resistant bacteria in their facilities.

—Kristin Althea O’Connor

Related Information:

Superbugs Including MRSA Thwarted by Unconventional Vaccine

A Protein-Free Vaccine Stimulates Innate Immunity and Protects Against Nosocomial Pathogens

Superbug Vaccine “Hulkifies” Macrophages in Mouse Model

A Dark Daily Extra! Overcoming the Common Challenges Facing Molecular Diagnostics Labs to Maximize Revenue

This is the second of a three-part series on revenue cycle management for molecular testing laboratories and pathology practices, produced in collaboration with XiFin, Inc.

Second of a 3-part series, this article will detail what molecular diagnostics and pathology groups need to understand about coding, billing, and denial management to maximize revenue and cash flow successfully.

In the first article, we discussed how molecular diagnostics and pathology groups can enhance the patient experience, physician engagement, and payer relations. Now, we will detail how denial management can successfully maximize revenue and cash flow. As we discussed in the last article, revenue cycle management (RCM) is much more than billing.

Today’s rapidly changing environment of directives and expectations from payers, patients, and health systems require deeper understanding, great agility, and strategy in every aspect of business. Creating opportunities to provide better service, adopt state-of-the-art technologies, and build robust processes and partnerships can make or break the long-term success of a laboratory or pathology practice.

Technical assessments are often required to establish clinical validity and utility to achieve payer coverage for novel genetic tests. Achieving payer coverage requires a deep understanding of how-to code tests compliantly and to facilitate reimbursement.

“We recommend that molecular diagnostics laboratories consult with coding experts to fully understand the coding requirements for each genetic test,” says Clarisa Blattner, XiFin Senior Director, Revenue and Payor Optimization.  “Ensuring reimbursement requires knowing payer policies and to track denial trends by payer over time to identify changes.”

Blattner noted that payer policies and behavior are constantly changing. Labs, and their billing partners must stay abreast of changes to avoid lengthy delays that denials and subsequent appeals can cause. Understanding the documentation that is required with claims is invaluable. Knowing these requirements up front and submitting complete claims with all required medical records and documentation of medical necessity goes a long way toward facilitating reimbursement.

Payers are adopting increasingly rigid policies that are often inconsistent with others. Reimbursements continue to be cut while quality reporting requirements rise.

Diagnostics laboratories that conduct genetic testing must also overcome four common challenges:

  1. Achieving and expanding payer coverage with coverage determination that defines reasonable and medically necessary services and tests.
  2. Knowing how to code the tests correctly with medical nomenclature to report services and/or tests to a payer.
  3. Ensuring payment/reimbursement for services/tests based on services/tests rendered and coverage determination.
  4. Maintaining compliance and keeping abreast of billing compliance and having a voice in reform

“We also recommend that laboratories conduct internal audits that reconcile laboratory information system (LIS) data with RCM system data,” Blattner continued. “Labs with a robust business intelligence (BI) solution can proactively identify outliers, such as accessions that exist in one system but not the other.”

Maintain Your Billing System and Maximize Clean Claim Submissions

Laboratories should be sure that these four payer services are being handled appropriately, whether it is by the lab or an RCM partner:

1.         Payer relations: An effective payer relations team monitors denials and coordinates with payers. This team reviews front-end payer rejections, coordinates with clients (i.e., ordering physicians), and identifies and updates edits based on payer policies and behavior changes.

2.         Electronic data interchange (EDI) enrollment: This team handles monitoring and proactive enrollment for electronic submissions and helps ensure bidirectional transaction automation.

3.         EDI analysts: Experts in healthcare EDI who investigate errors, participate in standards development and testing, as well as payer education and coordination.

4.         EDI operations: These specialized technicians configure files and ensure the reconciliation of claim-level submissions.

Efficiently Upload and Store Medical Records and Documentation

Although laboratories do not directly control patient medical records, it is essential to understand that diagnosis codes alone are generally insufficient.

Laboratory sales representatives must work with clients and ordering physicians to ensure medical records have all the information required for payment. Ensuring that the payers expedite payment requires efficient uploading and storing of medical records and documentation:

  • Align with payers on clinical utility evidence requirements, current billing policies, and preferred coding approach.
  • Leverage the support and advocacy of key opinion leaders (KOLs).
  • Collaborate with clinicians on the prior authorization process.
  • Select an RCM partner that helps you maximize process automation and front-end edits.
  • Leverage a business intelligence (BI) system that simplifies the tracking of key performance indicators (KPIs), helps identify payer policy and behavior changes early, and highlights changes in key business trends.

The RCM system must be able to upload and store medical records and documentation. The required medical information typically includes the following:

Who? Ordering/referring provider.

What? What service(s)/test(s) is/are being ordered?

Where? Where is the specimen being sent?

When? What is the date of service (DOS)?

Why? What are the patient’s signs/symptoms, or what prompted the test to be ordered?

How? How are the test results used to manage the patient’s medical condition?

But even after including all of the correct medical information, denials are inevitable. There are important steps labs can take to streamline denial management.

The Importance of Patient Engagement in Maximizing Reimbursement

Patient engagement plays an essential role in facilitating reimbursement and maximizing cash collection. Patients expect transparency and ease of information access from their healthcare encounters, just as they experience in all other areas of their lives. Because most laboratory, pathology, and molecular encounters are not directly patient-facing, proven payment accelerating engagement tools are essential. Dynamic portals, electronic statements, and text messages are essential, especially when it comes to communication regarding errors and patient financial responsibility.

XiFin customer data show a substantive increase in patient payments received in the first 30 days of the dunning cycle after integrating texting and automated calls into the traditional process. For example, a XiFin customer collected 26.6% more of the revenue in the first 30 days after implementing a text reminder between the first and second paper statements. Prior to implementation, the customer followed a traditional three-statement dunning cycle:

  • 42.6% of total payments received occurred after the first statement (within the first 29 days of the dunning cycle).
  • 34.8% occurred after sending the second statement (between days 30-59 of the dunning cycle).
  • 22.6% were received after sending the third and final statement (during days 60-90 of thedunning cycle).
Dunning Cycle PeriodBeforeAfterIncrease/ (Decrease)
Days 1-2942.6%69.2%26.6%
Days 30-5934.8%27.8%-7.0%
Days 60-9022.6%3.0%-19.6%

The convenience of text messaging allows patients to connect to the call center or to the patient portal, where a payment can be made immediately. XiFin customers can customize their dunning cycle, depending on how their specific patient population responds to texts, paper statements, and the timing between billing cycles. Studying the behaviors of patient interactions at the client level, rather than only referencing the status quo of macro-level trending, empowers a more strategic approach to engagement and improving overall patient satisfaction.

Denial Trends Driving Reduced Revenue and Higher Costs

Denials extend time in accounts receivable, contributing to bad debt on services already rendered and laboratory expenses absorbed. Denials also often require the most attention from staff – increasing the cost of billing. Hard denials, such as Medical Necessity, make up the most challenging revenue to collect, comprising about 5-10% of total denials received. In addition to creating delays and revenue loss, denials illustrate how payers administer their policies, even when those policies are unpublished.

Fundamentally, an effective RCM process is rooted in the ability to file clean claims to the degree that is possible. Improving those outcomes requires focus on the exceptions – the dirty claims – the denials.

“At XiFin, we invest in front-end configurations and workflows to catch denials prior to submitting the claim to the payer,” continued Blattner.  “As we monitor denial trends, we build more robust front-end workflows and add automation (such as integrating with insurance discovery and prior authorization vendors) to reduce the associated burden on billing teams.”

In addition, molecular testing coverage continues to expand, reducing non-covered denials. The stabilization of these medical policy-related denials is positive. The jump in demographic denials, however, requires additional consideration.

Paid vs. Denied by Payer Group

Denial patterns vary among payers. The percentage of claims denied also differs by segment, largely due to the type of testing performed.

Of the claims XiFin processes annually (approximately $50 billion in charges), 22.5% are denied. The graphs below demonstrate molecular testing’s higher propensity for denial (27.5% of claims billed), driven by non-covered, medical necessity, and prior authorization requirement challenges.

Routine pathology has closer to a 20% denial rate overall. The average percentage of billed claims that are denied by segment are:

■ Clinical: 13.62%

■ Molecular: 27.19%

■ Pathology: 19.82%

Molecular testing has a higher propensity for denial (27.5% of claims billed), driven by non-covered, medical necessity, and prior authorization requirement challenges. Routine pathology has closer to a 20% denial rate overall.

Clinical laboratory denial rates averaged 13.62% in 2021. As seen in the table below, clinical laboratories saw a significant decline in experimental/investigational denials between 2018 and 2021.

Denial TypeMolecular % of Total Denied 2018Clinical % of  Total Denied 2021Variance (% change 2021 vs. 2018)
Benefit Maximum Reached39.3%29.7%-24.4%
Claim Specific Negotiated Discount17.6%18.1%2.8%
Coordination of Benefits4.1%16.3%298%
Coverage Terminated6.6%13.4%103%
Diagnosis Not Covered11.3%6.4%-43.4%
Duplicate Denial8.3%3.4%-57.8%
Experimental Investigational0.1%2.7%2600%
HSA2.1%2.4%14.3%
Incorrect Payer0.9%1.6%77.8%
Non-Covered2.2%1.1%-50.0%
Patient Cannot be Identified0.7%0.8%14.3%
Patient Enrolled in Hospice0.5%0.5%0.0%
Prior Authorization0.2%0.2%0.0%
Procedure Code Inconsistent with the Modifier Used  or a Required Modifier is Missing1.6%0.1%-87.5%
Procedure Not Paid Separately0.5%0.1%-60.0%
Service Not Payable per Managed Care Contract0.1%0.0%-100%

Molecular claims continue to experience the highest denial rates of any laboratory segment. With an average denial rate of 27%, molecular continues to be a revenue recovery workflow heavy on the back-end. As a percentage of the total denial population, between 2018 and 2021, XiFin experienced increases in patient-coverage denials, such as coordination of benefits (298%), coverage terminated (103%), and experimental/investigational (2600%). Decreases in diagnosis not covered denials (-43.4%) and duplicate denials (-57.8%) are also recognized.

Exome/Genome Testing must be administered by specialized technicians with specific credentials, creating potential backlogs. They can take 8, 12, or even 16 weeks to complete, depending on testing methodologies. This presents a high risk of timely filing denials for the many payers that have adopted 90-day timely filing limits. XiFin recommended practice: Explore amending your payer contracts to extend timely filing limits on these tests.

Denial TypePathology % of Total Denied 2018Pathology % of  Total Denied 2021Variance (% change 2021 vs. 2018)
Prior Authorization28.9%36.1%24.6%
Duplicate Denial21.5%21.2%-1.9%
Non-Covered14.1%10.1%-27.7%
Services Not Prov. By Network/Primary Care Provider8.8%8.5%-3.4%
Procedure Not Paid Separately4.4%5.1%15.9%
Services Not Authorized by Network/Primary Care Provider3.6%3.8%5.6%
Procedure Code Inconsistent with the Modifier Used  or a Required Modifier is Missing1.5%3.3%120%
Coverage Terminated2.2%2.6%18.2%
Coordination of Benefits3.8%2.4%-34.2%
Patient Cannot Be Identified3.1%2.3%-25.8%
Remark Code5.9%2.1%-64.4%
Experimental Investigational1.0%1.2%20.0%
Benefit Maximum Reached0.4%1.0%175%
Patient Enrolled in Hospice0.4%0.1%-75.0%
Incorrect Payer0.0%0.1%100%
Service Not Payable per Managed Care Contract0.2%0.0%-100%

Anatomic pathology denials have increased by approximately 5% from 2018 to 2021. As a percentage of the total denial population, the lack of prior authorization is the highest contributor to this increase, having grown 24.6%. There was an increase in procedure code inconsistent with modifier denials (120% increase) and a decrease in non-covered denials (-27.7%).

Importance of an Efficient and Effective Appeals Process

Front-end edits and configurations help mitigate backend denials. Capturing potential denial-related issues proactively are the most effective way to maintain a manageable AR and improve the propensity to pay. For example, payers that observe National Correct Coding Initiative (NCCI) and Medically Unlikely Edits (MUEs) will consider all Current Procedural Terminology (CPT) codes billed for that patient for the same Date of Service (DOS), even when not billed on the same claim form.

Denials are inevitable if your current billing process does not have edits in place to perform a historical review of charges for the same patient on the same DOS.

Denials are unavoidable, and not all known issues can be addressed on the front end of the process. An example of this is denial code CO252, which is an additional information denial. It indicates the payer is requesting additional documentation (i.e., clinical information, medical records, and test results) before issuing payment – essentially performing an audit to ensure the services billed are reasonable and necessary and medical necessity is justified and documented.

“These are not always complex molecular tests; they can be routine pathology claims,” said Blattner “Each time we receive a CO252 denial it has to be appealed with additional documentation found in the patient’s medical records. Though it is inevitable, we must wait on the denial before we can take action.”

SegmentAppeal-Payments as  % of Total Insurance Payments ReceivedAverage Payment  Amount per Appeal
Clinical0.11%$121
Molecular6.56%$1,420
Pathology1.12%$327
Industry Average3.39% $623

Payment collection per appeal continues to be stable in the pathology (averaging 1-2%) and clinical segments, where appeals are less prolific. Revenue recovered by corrected claims is excluded since these claims follow a separate process and impact denial codes such as CO97 (Procedure or service isn’t paid for separately), CO18 (Duplicate), and CO234 (Procedure not paid separately). Further, a single appeal process is not sufficient. A robust appeals process here becomes critical. Specifically in molecular testing, appeals carry a heightening impact on revenue collection. In 2020, appeals accounted for 5% of the total revenue generated by XiFin customers. In 2021, that increased to 6.5%.

Appeal Success Rates by Payer Group by Segment

The next four charts show appeal success rates by payer group for 2021, overall and by market segment for clinical, molecular, and pathology. The fifth chart illustrates the incremental impact of multiple appeal attempts by market segment.

This assessment only includes activity related to revenue recovery through an appeals process. Some denials can be addressed by filing of a corrected claim and can be a much more efficient process. Although ideal, corrected claims are not always possible, depending on denial type and individual payer preferences.

 % of Total  Appeals Filed% of Appeals Paid after 1st Attempt% of Appeals Paid after 2nd Attempt% of Appeals Paid after 3rd AttemptAvg Payment per Appeal
Clinical 17.4%17.8%9.9% $ 276
Additional Information70.1%20.9%20.3%10.0% $ 258
COVID Medical Necessity8.9%3.9%50.0%  $ 78
Medical Necessity4.8%30.4%18.4%  $ 553
Out of Network6.9%4.4%2.4%  $ 594
Prior Authorization0.0%14.3%0.0%  $ 421
Underpayment9.3%6.9%6.3%  $ 10

The clinical laboratory segment maintains the lowest volume of denials. But this does not negate the need for robust editing processes. Implementing robust front-end logic and leveraging intelligent automation to correct potential issues dramatically streamlines the process from submission to payment, especially in the high-volume clinical laboratory segment.

 % of Total  Appeals Filed% of Appeals Paid after 1st Attempt% of Appeals Paid after 2nd Attempt% of Appeals Paid after 3rd AttemptAvg Payment per Appeal
Molecular 21.4%17.2%19.4% $1,420
Additional Information47.7%23.9%20.7%23.3% $1,285
Medical Necessity23.0%17.6%14.0%12.8% $1,518
Prior Authorization11.4%18.9%11.7%13.1% $1,944
Experimental and Investigational / Non-Covered5.6%13.2%9.0%9.0%$4,234
COVID Underpayment3.8%44.7%24.6%10.7% $52
Timely Filing3.5%10.1%8.3%18.9% $551
Out of Network3.5%14.0%10.8%8.4% $2,513
Underpayment1.1%31.2%17.8%15.3% $2,154
COVID Medical Necessity0.4%46.4%27.0%0.0% $124

Appeal Trends: Molecular and Genomic Testing   At $1,420, the average payment per appeal for molecular testing is more significant due to the high-dollar value of the testing. Additional information appeals account for 47% of the total appeals filed in 2021 in the molecular segment and have an average success rate of 23%. Another 23% of appeals are for claims denied for medical necessity, followed by prior authorizations at 11.4% of total appeals filed. Prior authorization appeal volumes have remained consistent year-over-year in this segment, averaging 10% in 2020, despite a higher volume of prior authorization requirements than pathology or clinical laboratory.

XiFin’s RCM platform has integrated automation with prior authorization partners, allowing claims meeting prior authorization criteria to be submitted to a prior authorization solution automatically.  Utilizing “real-time data exchange” via application programming interfaces (API) without partners, XiFin can more quickly acquire the necessary prior authorization number and update the patient’s information in XiFin RPM upon those values being returned.

 % of Total  Appeals Filed% of Appeals Paid after 1st Attempt% of Appeals Paid after 2nd Attempt% of Appeals Paid after 3rd AttemptAvg Payment per Appeal
Pathology 22.6%20.6%21.8% $327
Additional Information33.4%28.8%23.4%27.9% $337
Medical Necessity19.0%23.5%23.4%27.6% $398
Out of Network17.9%17.6%12.4%17.7% $318
Prior Authorization12.2%21.5%32.9%36.5% $350
Experimental and Investigational / Non-Covered9.2%17.8%8.9%3.1% $195
COVID Underpayment5.8%9.0%3.4%16.7% $31
Timely Filing2.5%20.5%15.6%13.3% $191
Underpayment0.1%52.2%0.0%  $177

Appeal Trends: Pathology

Approximately 2% of the pathology accessions received into XiFin RPM require an appeal. Those appeals will be responsible for approximately 1-2% of the pathology practice’s revenue. As noted above, the revenue reclaimed is largely attributed to the first attempted appeal. A robust process that includes multiple attempts is critical in revenue recovery in the event the first appeal is not overturned.

If Not Documented, It Did Not Happen

Payer edits and guidelines can be difficult to follow, particularly if physicians, coders, or billing staff are expected to memorize those requirements.

Making the situation even more challenging is the fact that edits vary widely among payers and are constantly changing. RCM platforms should be updated routinely (XiFin RPM is updated monthly) with payer edit updates, while remaining configurable so that custom edits can be easily built to accommodate specific payer requirements.

Whether it is a payer audit or packaging an appeal, documentation in the pathology report and/or clinical notes should clearly outline the services provided and the medical necessity of those services. If it is not documented, it did not happen. Further, understand the various programs that drive payer edits and guidelines. These edits drive an increased need for discipline and documentation. Be conscious of payer-specific requirements. Cigna, Aetna, and UHC require proprietary forms to be completed when appealing claims.

Benchmarking Productivity

Proactively preventing a denial and avoiding the need to submit a corrected claim or file an appeal reduces the time to reimbursement by four to eight weeks, depending on the payer and type of denial. If denials are not addressed properly and manual workflows persist, diagnostic labs will continue to experience a loss of revenue, and staffing will be insufficient to keep up.

Productivity rates for anatomic and molecular billing teams historically average between 12,000-15,000 accessions per person per full-time equivalent (FTE) per year (clinical laboratory is often much higher). However, with the increases in denials, the resulting demands on back-end teams have increased substantially and impacted productivity rates. This holds particularly true for particularly non-covered, medical necessity, and prior authorization denials.

Further, speed to payment is also improved. By automating appeals, the turn-around-time on submitting back to the payer is reduced, on average, from 45 days to 1-3 days, as seen in the blue bar in the chart above.

By installing front-end edits to help maximize clean claims, up to an additional 54 days can be saved, moving from 135 days to just 30 days for full adjudication.

Automating Workflows with AI

Opportunities to automate the process will reduce time and labor and make decisions more consistent. Once there is a deep understanding of coding, billing, denial management, and strategic appeals, there is the ability to automate the important processes across the RCM process. Automation and AI-powered workflows pave the way for consistent, optimized molecular diagnostics and pathology RCM.

Part 3 will demonstrate how AI can be used in RCM to inform, accelerate, automate, validate, and generate. Watch for updates here at DarkDaily.

— Leslie Williams

Pathology Lab Shortages in New Zealand Are One Cause in Long Delays in Melanoma Diagnoses

Similar diagnostic delays due to clinical laboratory staff shortages are reported in other nations as well

Critical pathology shortages are causing lengthy delays for clinical laboratory test results in New Zealand, according to a report that states some patients are waiting over a month for a melanoma diagnosis. This situation puts the lives of cancer patients at risk in the island nation. 

The Melanoma Network of New Zealand (MelNet) is working to reduce the number of people who develop the disease and help melanoma patients receive a fast diagnosis and proper treatment and care.

However, plastic surgeon and MelNet Chair Gary Duncan, MBChB, FRACS, told Radio New Zealand (RNZ) that when patients return to their doctors for test results, those results often have not come back from the medical laboratory. Therefore, the physician cannot discuss any issues with the patient, which causes them to make another appointment for a later date or receive a melanoma diagnosis over the telephone, RNZ reported.

Dermatologist Louise Reiche, MBChB, FRACS, told RNZ that slow pathology services are unfair to patients. Such delays could result in the spreading of the melanoma to other parts of the body and require major surgery under anesthetic.

“Not only will they suffer an extensive surgical procedure, but it could also shorten their life,” she said.

Trishe Leong, MB.BS (hons) Medicine, FRCPA Anatomical Pathology

“We’ve got shortages across the board, and it only seems to be getting worse,” said Trishe Leong, MB.BS (hons) Medicine, FRCPA Anatomical Pathology (above), President of the Royal College of Pathologists of Australasia (RCPA). She added that “there was also a backlog of pathological examinations of placentas, which are used to detect genetic conditions and shed light on complex births,” The Sydney Morning Herald reported. Clinical laboratories in several countries worldwide are experiencing similar delays in reporting critical test results to physicians and their patients. (Photo copyright: RCPA.)

Pathology Labs Cannot Meet Demand for Testing

The Royal College of Pathologists recommends that 80% of specimen results should be returned to clinicians within five days. General practitioner Jeremy Hay, MD, of the Upper Hutt Skin Clinic told RNZ that he has never seen a melanoma report returned from the laboratory he utilizes within the suggested five-day time span. He stated that his local pathology lab simply cannot meet the demand for the vast number of samples waiting to be tested.

“I have visited the lab, and you can see even in the corridors stacks of unreported slides sitting outside the pathologist’s rooms, and there are more inside their rooms,” he said. “They need more staff and that’s quite obvious.”

Hay added that, because of the delays, he typically does not start with a small biopsy of a suspicious-looking piece of skin. Instead, he just cuts the entire area out and sends it to the lab for testing to expedite the diagnosis process.

Lab Loses Accreditation Due to Delays

Long delays caused one lab—Auckland’s Community Anatomic Pathology Service (APS)—to lose its accreditation for the lab’s skin testing department. According to RNZ, some patients had to wait up to eight weeks to learn whether they had melanoma.

An article published by medical/science specialty recruiting firm Odyssey, states that the deficiency at APS was due to several factors, including:

  • Population growth.
  • An increase in private medical practices.
  • The underestimation of the costs required to run the lab.
  • An overestimation of potential savings.
  • A shortage of qualified pathologists, specifically in the fields of anatomical, chemical, and forensic pathology.

The article also states that pathologists are now listed on Immigration New Zealand’s list of shortage skills in the country. That designation means that foreign candidates who have the skills, and who are offered jobs in the country, can immediately apply for permanent residency. 

Three Week Wait for Cancer Diagnoses in Australia

According to the World Cancer Research Fund International (WCRF), New Zealand has the second highest rate of melanoma in the world. The number one spot is held by Australia. 

Other countries are experiencing long wait times for cancer diagnoses as well. According to The Sydney Morning Herald, some individuals are waiting up to three weeks to receive a cancer diagnosis due to a shortage of pathologists.

“It could be the cancer you didn’t suspect,” said Trishe Leong, MB.BS (hons) Medicine, FRCPA Anatomical Pathology, President of the Royal College of Pathologists of Australasia. “There’s always the chance of something unexpected showing up in a biopsy, and if that is not tended to as soon as possible it can have an impact on patient care.”

This is not the first time Dark Daily has reported on clinical laboratory staff shortages around the world causing huge test result delays.

In “Irish Cancer Society Report Shows Patients May Wait Two Years or More for Genetic Cancer Test Results,” we covered research conducted at the University of College Cork (UCC) which revealed that genetic services have been “starved of investment and resources” in the Emerald Isle, leaving healthcare workers involved in cancer genetics and follow-on services “completely overstretched.”   

And in “In Canada, Shortage of Medical Laboratory Technologists and Radiology Technicians Continues to Delay Care,” we reported how varies combinations of facility, physician, and other healthcare professional shortages are generating regular headlines about patient wait times in the UK, Canada, New Zealand, and Australia, particularly for elective procedures that may be six months to a year or more.

In those countries, and around the world, healthcare experts say the solution is expanding training opportunities to solve the shortage of clinical laboratory scientists, medical laboratory and imaging technologists, doctors, nurses, and other medical professionals, and increasing funding for modernizing hospital facilities and clinics.

But in countries with government-run healthcare, that solution is problematic at best.

—JP Schlingman

Related Information:

Eight-week Wait for Skin Cancer Test Results Risking Lives – Doctors

Private Healthcare Pushing Auckland Labs to the Brink

Factors Increasing Demand for Pathology Services in Auckland NZ

‘The Cancer You Didn’t Suspect’: Medical Test Delays Could Be Endangering Patients

Irish Cancer Society Report Shows Patients May Wait Two Years or More for Genetic Cancer Test Results

Experimental Low-Cost Blood Test Can Detect Multiple Cancers, Researchers Say

Test uses a new ultrasensitive immunoassay to detect a known clinical laboratory diagnostic protein biomarker for many common cancers

Researchers from Mass General Brigham, the Dana-Farber Cancer Institute, Harvard University’s Wyss Institute and other institutions around the world have reportedly developed a simple clinical laboratory blood test that can detect a common protein biomarker associated with multiple types of cancer, including colorectal, gastroesophageal, and ovarian cancers.

Best of all, the researchers say the test could provide an inexpensive means of early diagnosis. This assay could also be used to monitor how well patients respond to cancer therapy, according to a news release.

The test, which is still in experimental stages, detects the presence of LINE-1 ORF1p, a protein expressed in many common cancers, as well as high-risk precursors, while having “negligible expression in normal tissues,” the researchers wrote in a paper they published in Cancer Discovery titled, “Ultrasensitive Detection of Circulating LINE-1 ORF1p as a Specific Multicancer Biomarker.”

The protein had previously been identified as a promising biomarker and is readily detectable in tumor tissue, they wrote. However, it is found in extremely low concentrations in blood plasma and is “well below detection limits of conventional clinical laboratory methods,” they noted.

To overcome that obstacle, they employed an ultra-sensitive immunoassay known as a Simoa (Single-Molecule Array), an immunoassay platform for measuring fluid biomarkers.

“We were shocked by how well this test worked in detecting the biomarker’s expression across cancer types,” said lead study author gastroenterologist Martin Taylor, MD, PhD, Instructor in Pathology, Massachusetts General Hospital and Harvard Medical School, in the press release. “It’s created more questions for us to explore and sparked interest among collaborators across many institutions.”

Kathleen Burns, MD, PhD

“We’ve known since the 1980s that transposable elements were active in some cancers, and nearly 10 years ago we reported that ORF1p was a pervasive cancer biomarker, but, until now, we haven’t had the ability to detect it in blood tests,” said pathologist and study co-author Kathleen Burns, MD, PhD (above), Chair of the Department of Pathology at Dana-Farber Cancer Institute and a Professor of Pathology at Harvard Medical School, in a press release. “Having a technology capable of detecting ORF1p in blood opens so many possibilities for clinical applications.” Clinical laboratories may soon have a new blood test to detect multiple types of cancer. (Photo copyright: Dana-Farber Cancer Institute.)

Simoa’s Advantages

In their press release, the researchers described ORF1p as “a hallmark of many cancers, particularly p53-deficient epithelial cancers,” a category that includes lung, breast, prostate, uterine, pancreatic, and head and neck cancers in addition to the cancers noted above.

“Pervasive expression of ORF1p in carcinomas, and the lack of expression in normal tissues, makes ORF1p unlike other protein biomarkers which have normal expression levels,” Taylor said in the press release. “This unique biology makes it highly specific.”

Simoa was developed at the laboratory of study co-author David R. Walt, PhD, the Hansjörg Wyss Professor of Bioinspired Engineering at Harvard Medical School, and Professor of Pathology at Harvard Medical School and Brigham and Women’s Hospital.

The Simoa technology “enables 100- to 1,000-fold improvements in sensitivity over conventional enzyme-linked immunosorbent assay (ELISA) techniques, thus opening the window to measuring proteins at concentrations that have never been detected before in various biological fluids such as plasma or saliva,” according to the Walt Lab website.

Simoa assays take less than two hours to run and require less than $3 in consumables. They are “simple to perform, scalable, and have clinical-grade coefficients of variation,” the researchers wrote.

Study Results

Using the first generation of the ORF1p Simoa assay, the researchers tested blood samples of patients with a variety of cancers along with 406 individuals, regarded as healthy, who served as controls. The test proved to be most effective among patients with colorectal and ovarian cancer, finding detectable levels of ORF1p in 58% of former and 71% of the latter. Detectable levels were found in patients with advanced-stage as well as early-stage disease, the researchers wrote in Cancer Discovery.

Among the 406 healthy controls, the test found detectable levels of ORF1p in only five. However, the control with the highest detectable levels, regarded as healthy when donating blood, “was six months later found to have prostate cancer and 19 months later found to have lymphoma,” the researchers wrote.

They later reengineered the Simoa assay to increase its sensitivity, resulting in improved detection of the protein in blood samples from patients with colorectal, gastroesophageal, ovarian, uterine, and breast cancers.

The researchers also employed the test on samples from 19 patients with gastroesophageal cancer to gauge its utility for monitoring therapeutic response. Although this was a small sample, they found that among 13 patients who had responded to therapy, “circulating ORF1p dropped to undetectable levels at follow-up sampling.”

“More Work to Be Done”

The Simoa assay has limitations, the researchers acknowledged. It doesn’t identify the location of cancers, and it “isn’t successful in identifying all cancers and their subtypes,” the press release stated, adding that the test will likely be used in conjunction with other early-detection approaches. The researchers also said they want to gauge the test’s accuracy in larger cohorts.

“The test is very specific, but it doesn’t tell us enough information to be used in a vacuum,” Walt said in the news release. “It’s exciting to see the early success of this ultrasensitive assessment tool, but there is more work to be done.”

More studies will be needed to valid these findings. That this promising new multi-cancer immunoassay is based on a clinical laboratory blood sample means its less invasive and less painful for patients. It’s a good example of an assay that takes a proteomic approach looking for protein cancer biomarkers rather than the genetic approach looking for molecular DNA/RNA biomarkers of cancer.

—Stephen Beale

Related Information:

Ultrasensitive Blood Test Detects ‘Pan-Cancer’ Biomarker

New Blood Test Could Offer Earlier Detection of Common Deadly Cancers

Ultrasensitive Detection of Circulating LINE-1 ORF1p as a Specific Multicancer Biomarker

Noninvasive and Multicancer Biomarkers: The Promise of LINE-1 Retrotransposons

LINE-1-ORF1p Is a Promising Biomarker for Early Cancer Detection, But More Research Is Needed

‘Pan-Cancer’ Found in Highly Sensitive Blood Test

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