The Office of Management and Budget (OMB) concluded its review of the final rule on April 22. Former FDA commissioner Scott Gottlieb, MD, and other regulatory experts expect the White House to send the final rule to Congress as early as late April and no later than May 22.
On Tuesday morning, Lâle White, executive chair and CEO of San Diego’s XiFin, Inc., will present a keynote on new regulations and diagnostics players that are “poised to reshape lab testing.” Her presentation is followed by a general session on Clinical Laboratory Improvement Amendments (CLIA) regulations featuring Salerno Reynolds, PhD., acting director at the U.S. Centers for Disease Control and Prevention (CDC) Center for Laboratory Systems and Response.
Robert Michel, Editor-in-Chief of The Dark Report will wrap day one with a general session on the regulatory trifecta coming soon to all labs, from LDT to CLIA to private payers’ policies for genetic claims.
Innovation in the spotlight
“It’s a rich mix of expert speakers, lab leaders who are doing innovative things in their own organizations, along with the consultants and the lab vendors who are pushing the front edge of laboratory management, operations, and clinical service delivery,” says Michel, who each year creates the agenda for EWC.
Several sessions, master classes, and speakers will look to the future with discussions about how healthcare data drives innovations in diagnostics and patient care, digital pathology adoption around the world, and hot topics such as artificial intelligence (AI), big data and precision medicine.
Panels offer a variety of viewpoints
“One valuable benefit of participating at the Executive War College is the various panel discussions,” Michel says. “Each panel brings together national experts in a specific area of the laboratory profession. As an example, our lab legal panel this year brings together four prominent and experienced attorneys who share opinions, insights, and commentary about relevant issues in compliance, regulations, and contractual issues with health plans and others.”
This allows attendees to experience a breadth of opinions from multiple respected experts in this area, he adds.
For example, a digital pathology panel will bring together representatives from labs, service providers, and the consultants that are helping labs implement digital pathology. The session will be especially helpful to labs that are deciding when to acquire digital pathology tools and how to deploy them effectively to improve diagnostic accuracy, Michel says.
And a managed care panel will feature executives from some of the nation’s biggest health plans—the ones that sit on the other side of the table from labs—to provide insights and guidance on how labs can work more effectively with them.
Networking opportunities abound
The event is about much more than politics and policy, however. There’s also a distinct social aspect.
“Everyone is welcome, and everyone appreciates the camaraderie, so don’t be shy about going up and introducing yourself to someone. The quality of the crowd is top-notch, yet I’ve always experienced a willingness for those of us who have been to this rodeo to always be welcoming,” she notes.
Michel agrees. “One of the special benefits of participation at the EWC is the superb networking interactions and collaboration that takes place,” he says.
“From the first moments that attendees walk into our opening reception on Monday night until the close of the optional workshops on Thursday, one can see a rich exchange happening amongst circles of attendees. Introductions are being made. Connections are developing into business opportunities. The sum of an attendee’s experience at the Executive War College is to gain as much knowledge from the networking and collaboration as they do from the sessions.”
Speedy DNA sequencing and on-the-spot digital imaging may change the future of anatomic pathology procedures during surgery
Researchers at the Center for Molecular Medicine (CMM) at UMC Utrecht, a leading international university medical center in the Netherlands, have paired artificial intelligence (AI) and machine learning with DNA sequencing to develop a diagnostic tool cancer surgeons can use during surgeries to determine in minutes—while the patient is still on the operating table—whether they have fully removed all the cancerous tissue.
The method, “involves a computer scanning segments of a tumor’s DNA and alighting on certain chemical modifications that can yield a detailed diagnosis of the type and even subtype of the brain tumor,” according to The New York Times, which added, “That diagnosis, generated during the early stages of an hours-long surgery, can help surgeons decide how aggressively to operate, … In the future, the method may also help steer doctors toward treatments tailored for a specific subtype of tumor.”
This technology has the potential to reduce the need for frozen sections, should additional development and studies confirm that it accurately and reliably shows surgeons that all cancerous cells were fully removed. Many anatomic pathologists would welcome such a development because of the time pressure and stress associated with this procedure. Pathologists know that the patient is still in surgery and the surgeons are waiting for the results of the frozen section. Most pathologists would consider fewer frozen sections—with better patient outcomes—to be an improvement in patient care.
“It’s imperative that the tumor subtype is known at the time of surgery,” Jeroen de Ridder, PhD (above), associate professor in the Center for Molecular Medicine at UMC Utrecht and one of the study leaders, told The New York Times. “What we have now uniquely enabled is to allow this very fine-grained, robust, detailed diagnosis to be performed already during the surgery. It can figure out itself what it’s looking at and make a robust classification,” he added. How this discovery affects the role of anatomic pathologists and pathology laboratories during cancer surgeries remains to be seen. (Photo copyright: UMC Utrecht.)
Rapid DNA Sequencing Impacts Brain Tumor Surgeries
The UMC Utrecht scientists employed Oxford Nanopore’s “real-time DNA sequencing technology to address the challenges posed by central nervous system (CNS) tumors, one of the most lethal type of tumor, especially among children,” according to an Oxford Nanopore news release.
The researchers called their new machine learning AI application the “Sturgeon.”
According to The New York Times, “The new method uses a faster genetic sequencing technique and applies it only to a small slice of the cellular genome, allowing it to return results before a surgeon has started operating on the edges of a tumor.”
Jeroen de Ridder, PhD, an associate professor in the Center for Molecular Medicine at UMC Utrecht, told The New York Times that Sturgeon is “powerful enough to deliver a diagnosis with sparse genetic data, akin to someone recognizing an image based on only 1% of its pixels, and from an unknown portion of the image.” Ridder is also a principal investigator at the Oncode Institute, an independent research center in the Netherlands.
The researchers tested Sturgeon during 25 live brain surgeries and compared the results to an anatomic pathologist’s standard method of microscope tissue examination. “The new approach delivered 18 correct diagnoses and failed to reach the needed confidence threshold in the other seven cases. It turned around its diagnoses in less than 90 minutes, the study reported—short enough for it to inform decisions during an operation,” The New York Times reported.
But there were issues. Where the minute samples contain healthy brain tissue, identifying an adequate number of tumor markers could become problematic. Under those conditions, surgeons can ask an anatomic pathologist to “flag the [tissue samples] with the most tumor for sequencing, said PhD candidate Marc Pagès-Gallego, a bioinformatician at UMC Utrecht and a co-author of the study,” The New York Times noted.
“Implementation itself is less straightforward than often suggested,” Sebastian Brandner, MD, a professor of neuropathology at University College London, told The Times. “Sequencing and classifying tumor cells often still required significant expertise in bioinformatics as well as workers who are able to run, troubleshoot, and repair the technology,” he added.
“Brain tumors are also the most well-suited to being classified by the chemical modifications that the new method analyzes; not all cancers can be diagnosed that way,” The Times pointed out.
Thus, the research continues. The new method is being applied to other surgical samples as well. The study authors said other facilities are utilizing the method on their own surgical tissue samples, “suggesting that it can work in other people’s hands.” But more work is needed, The Times reported.
UMC Utrecht Researchers Receive Hanarth Grant
To expand their research into the Sturgeon’s capabilities, the UMC Utrecht research team recently received funds from the Hanarth Fonds, which was founded in 2018 to “promote and enhance the use of artificial intelligence and machine learning to improve the diagnosis, treatment, and outcome of patients with cancer,” according to the organization’s website.
The researchers will investigate ways the Sturgeon AI algorithm can be used to identify tumors of the central nervous system during surgery, a UMC Utrecht news release states. These type of tumors, according to the researchers, are difficult to examine without surgery.
“This poses a challenge for neurosurgeons. They have to operate on a tumor without knowing what type of tumor it is. As a result, there is a chance that the patient will need another operation,” said de Ridder in the news release.
The Sturgeon application solves this problem. It identifies the “exact type of tumor during surgery. This allows the appropriate surgical strategy to be applied immediately,” the news release notes.
The Hanarth funds will enable Jeroen and his team to develop a variant of the Sturgeon that uses “cerebrospinal fluid instead of (part of) the tumor. This will allow the type of tumor to be determined already before surgery. The main challenge is that cerebrospinal fluid contains a mixture of tumor and normal DNA. AI models will be trained to take this into account.”
The UMC Utrecht scientists’ breakthrough is another example of how organizations and research groups are working to shorten time to answer, compared to standard anatomic pathology methods. They are combining developing technologies in ways that achieve these goals.
Forces in play will directly impact the operations and financial stability of many of the nation’s clinical laboratories
With significant regulatory changes expected in the next 18 to 24 months, experts are predicting a “Perfect Storm” for managers of clinical laboratories and pathology practices.
Currently looming are changes to critical regulations in two regulatory areas that will affect hospitals and medical laboratories. One regulatory change is unfolding with the US Food and Drug Administration (FDA) and the other regulatory effort centers around efforts to update the Clinical Laboratory Improvement Amendments of 1988 (CLIA).
The major FDA changes involve the soon-to-be-published Final Rule on Laboratory Developed Tests (LDTs), which is currently causing its own individual storm within healthcare and will likely lead to lawsuits, according to the FDA Law Blog.
In a similar fashion—and being managed under the federal Centers for Medicare and Medicaid Services (CMS)—are the changes to CLIA rules that are expected to be the most significant since 2003.
The final element of the “Perfect Storm” of changes coming to the lab industry is the increased use by private payers of Z-Codes for genetic test claims.
In his general keynote, Robert L. Michel, Dark Daily’s Editor-in-Chief and creator of the 29th Executive War College on Diagnostics, Clinical Laboratory, and Pathology Management, will set the stage by introducing a session titled, “Regulatory Trifecta Coming Soon to All Labs! Anticipating the Federal LDT Rule, Revisions to CLIA Regulations, and Private Payers’ Z-Code Policies for Genetic Claims.”
“There are an unprecedented set of regulatory challenges all smashing into each other and the time is now to start preparing for the coming storm,” says Robert L. Michel (above), Dark Daily’s Editor-in-Chief and creator of the 29th Executive War College on Diagnostics, Clinical Laboratory, and Pathology Management, a national conference on lab management taking place April 30-May 1, 2024, at the Hyatt in New Orleans. (Photo copyright: The Dark Intelligence Group.)
Coming Trifecta of Disruptive Forces to Clinical Laboratory, Anatomic Pathology
The upcoming changes, Michel notes, have the potential to cause major disruptions at hospitals and clinical laboratories nationwide.
“Importantly, this perfect storm—which I like to describe as a Trifecta because these three disruptive forces that will affect how labs will conduct business—is not yet on the radar screen of most lab administrators, executives, and pathologists,” he says.
Because of that, several sessions at this year’s Executive War College conference, now in its 29th year, will offer information designed to give attendees a better understanding of how to manage what’s coming for their labs and anatomic pathology practices.
“This regulatory trifecta consists of three elements,” adds Michel, who is also Editor-in-Chief of Dark Daily’s sister publication The Dark Report, a business intelligence service for senior level executives in the clinical laboratory and pathology industry, as well in companies that offer solutions to labs and pathology groups.
According to Michel, that trifecta includes the following:
Element 1
FDA’s Draft LDT Rule
FDA’s LDT rule is currently the headline story in the lab industry. Speaking about this development and two other FDA initiatives involving diagnostics at the upcoming Executive War College will be pathologist Tim Stenzel, MD, PhD, former director of the FDA’s Office of In Vitro Diagnostics. It’s expected that the final rule on LDTs could be published by the end of April.
Stenzel will also discuss harmonization of ISO 13485 Medical Devices and the FDA’s recent memo on reclassifying most high-risk in vitro diagnostics to moderate-risk to ease the regulatory burden on companies seeking agency review of their diagnostic assays.
Salerno will also cover the CDC’s efforts to foster closer connections with clinical labs and their local public health laboratories, as well as the expanding menu of services for labs that his department now offers.
Element 3
Private Payer Use of Z-Codes for Test Claims
On the third development—increased use by private payers of Z-Codes for genetic test claims—the speaker will be pathologist Gabriel Bien-Willner, MD, PhD. He is the Medical Director of the MolDX program at Palmetto GBA, a Medicare Administrative Contractor (MAC). It is the MolDX program that oversees the issuance of Z-Codes for molecular and diagnostic tests.
UnitedHealthcare (UHC) was first to issue such a Z-Code policy last year, although it has delayed implementation several times. Other major payers are watching to see if UHC succeeds with this requirement, Michel says.
Other Critical Topics to be Covered at EWC
In addition to these need-to-know regulatory topics, Michel says that this year’s Executive War College will present almost 100 sessions and include 148 speakers. Some of the other topics on the agenda in New Orleans include the following and more:
Standardizing automation, analyzers, and tests across 25 lab sites.
Effective ways to attract, hire, and retain top-performing pathologists.
Leveraging your lab’s managed care contracts to increase covered tests.
“Our agenda is filled with the topics that are critically important to senior managers when it comes to managing their labs and anatomic pathology practices,” Michel notes.
“Every laboratory in the United States should recognize these three powerful developments are all in play at the same time and each will have direct impact on the clinical and financial performance of our nation’s labs,” Michel says. “For that reason, every lab should have one or more of their leadership team present at this year’s Executive War College to understand the implications of these developments.”
Visit here to learn more about the 29th Executive War College conference taking place in New Orleans.
This is the third of a three-part series on revenue cycle management for molecular testing laboratories and pathology practices, produced in collaboration with XiFin Inc.
Automation and AI-Powered Workflow Paves the Way for Consistent, Optimized Molecular Diagnostics and Pathology RCM
Third in a three-part series, this article will discuss how sophisticated revenue cycle management technology, including artificial intelligence (AI) capabilities, drives faster, more efficient revenue reimbursement for molecular and pathology testing.
Financial and operational leaders of molecular testing laboratories and pathology groups are under pressure to maximize the revenue collected from their services rendered. This is no easy task. Molecular claims, in particular, can be especially complex. This article outlines the specific areas in which automation and artificial intelligence (AI)-based workflows can improve revenue cycle management (RCM) for molecular diagnostic and pathology organizations so they can better meet their operational and financial goals.
AI can play a number of important roles in business. When it comes to RCM for diagnostic organizations, first and foremost, AI can inform decision-making processes by generating new or derived data, which can be used in reporting and analytics. It can also help understand likely outcomes based on historical data, such as an organization’s current outstanding accounts receivable (AR) and what’s likely to happen with that AR based on historic performance.
AI is also deployed to accelerate the creation of configurations and workflows. For example, generated or derived data can be used to create configurations within a revenue cycle workflow to address changes or shifts in likely outcomes, such as denial rates. Suppose an organization is using AI to analyze historical denial data and predict denial rates. In that case, changes in those predicted denial rates can be used to modify a workflow to prevent those denials upfront or to automate appeals on the backend. This helps organizations adapt to changes more quickly and accelerates the time to reimbursement.
“Furthermore, AI is used to automate workflows by providing or informing decisions directly,“ says Clarisa Blattner, XiFin Senior Director of Revenue and Payor Optimization. “In this case, when the AI sees shifts or changes, it knows what to do to address them. This enables an organization to take a process in the revenue cycle workflow that is very human-oriented and automate it.”
AI is also leveraged to validate data and identify outcomes that are anomalous, or that lie outside of the norm. This helps an organization:
Ensure that the results achieved meet the expected performance
Understand whether the appropriate configurations are in place
Identify if an investigation is required to uncover the reason behind any anomalies so that they can be addressed
Finally, AI can be employed to generate content, such as letters or customer support materials.
Everything AI starts with data
Everything AI-related starts with the data. Without good-quality data, organizations can’t generate AI models that will move a business forward. In order to build effective AI models, an organization must understand the data landscape and be able to monitor and measure performance and progress and adjust the activities being driven, as necessary.
Dirty, unstructured data leads to unintelligent AI. AI embodies the old adage, “garbage in, garbage out.” The quality of the AI decision or prediction is entirely based on the historical data that it’s seen. If that data is faulty, flawed, or incomplete, it can lead to bad decisions or the inability to predict or make a decision at all. Purposeful data modeling is critical to AI success, and having people and processes that can understand the complicated RCM data and structure it so it can be effectively analyzed is vital to success.
The next step is automation. Having effective AI models that generate strong predictions is only as valuable as the ability to get that feedback into the revenue cycle system effectively. If not, that value is minimal, because the organization must expend a lot of human energy to try to reconfigure or act on the AI predictions being generated.
There is a typical transformation path, illustrated below, that organizations go through to get from having data stored in individual silos to fully embedded AI. If an organization is struggling with aggregating data to build AI models, it’s at stage one. The goal is stage five, where an organization uses AI as a key differentiator and AI is a currency, driving activity.
The transformation starts with structuring data with an underlying data approach that keeps it future-ready. It is this foundation that allows organizations to realize the benefits of AI in a cost-effective and efficient way. Getting the automation embedded in the workflow is the key to getting to the full potential of AI in improving the RCM process.
Real-world examples of how AI and automation improve RCM
One example of how AI can improve the RCM process is using AI to discover complex payer information. One significant challenge for diagnostic service providers is ensuring that the right third-party insurance information for patients is captured. This is essential for clean claims submission. Often, the diagnostic provider is not the organization that actually sees the patient, in which case it doesn’t have the ability to collect that information directly. The organization must rely on the referring physician or direct outreach to the patient for this data when it’s incorrect or incomplete.
Diagnostic providers are sensitive to not burdening referring clients or patients with requests for demographic or payer information. It’s important to make this experience as simple and smooth as possible. Also, insurance information is complicated. A lot of data must be collected or corrected if the diagnostic provider doesn’t have the correct information.
Automating this process is difficult. Frequently, understanding who the payer is and how that payer translates into contracts and mapping within the revenue cycle process requires an agent to be on the phone with the patient. It can be very difficult for a patient to get precise payer plan information from their insurance card without the help of a customer service representative.
This is where AI can help. The goal is to require the smallest amount of information from a patient and be able to verify eligibility through electronic means with the payer. Using optical character recognition (OCR), an organization can take an image of the front and back of a patient’s insurance card, isolate the relevant text, and use an AI model to get the information needed in order to generate an eligibility request and confirm eligibility with that payer.
In the event that taking an image of the insurance card is problematic for a patient, the organization can have the patient walk through a simplified online process, for example, through a patient portal, and provide just a few pieces of data to be able to run eligibility verification and get to confirmed eligibility with the payer.
AI can help with this process too. For example, the patient can provide high-level payer information only, such as the name of the commercial payer or whether the coverage is Medicare or Medicaid, the state the patient resides in, and the subscriber ID and AI can use this high-level data to get an eligibility response and confirmed eligibility.
Once the eligibility response is received, the more detailed payer information can be presented back to the patient for confirmation. AI can map the eligibility response to the appropriate contract or payer plan within the RCM system.
Now that the patient’s correct insurance information is captured, the workflow moves on to collecting the patient’s financial responsibility payment. To do that, the organization needs to be able to calculate the patient’s financial responsibility estimate. The RCM system has accurate pricing information and now has detailed payer and plan information, a real-time eligibility response, as well as test or procedure information. This data can be used to estimate patient financial responsibility.
AI can also be used to address and adapt to changes in ordering patterns, payer responses, and payer reimbursement behavior. The RCM process can be designed to incorporate AI to streamline claims, denials, and appeals management, as well as to assign work queues and prioritize exception processing (EP) work based on the likelihood of reimbursement, which improves efficiency.
One other way AI can help is in understanding and or maintaining “expect” prices—what an organization can expect to collect from particular payers for particular procedures. For contracted payers, contracted rates are loaded into the RCM system. It’s important to track whether payers are paying those contracted rates and whether the organization is receiving the level of reimbursement expected. For non-contracted payers, it’s harder to know what the reimbursement rate will be. Historical data and AI can provide a good understanding of what can be expected. AI can also be used to determine if a claim is likely to be rejected because of incorrect or incomplete payer information or patient ineligibility, in which case automation can be applied to resolve most issues.
Another AI benefit relates to quickly determining the probability of reimbursement and assigning how claims are prioritized if a claim requires intervention that cannot be automated. With AI, these claims that require EP are directed to the best available team member, based on that particular team member’s past success with resolving a particular error type.
The goal with EP is to ensure that the claims are prioritized to optimize reimbursement. This starts with understanding the probability of the claim being reimbursed. An AI model can be designed to assess the likelihood of the claim being reimbursed and the likely amount of reimbursement for those expected to be paid. This helps prioritize activities and optimize labor resources. The AI model can also take important factors such as timely filing dates into account. If a claim is less likely to be collected than another procedure but is close to its timely filing deadline, it can be escalated. The algorithms can be run nightly to produce a prioritized list of claims with assignments to the specific team member best suited to address each error.
AI can also be used to create a comprehensive list of activities and the order in which those activities should be performed to optimize reimbursement. The result is a prioritized list for each team member indicating which claims should be worked on first and which specific activities need to be accomplished for each claim.
Summing it all up, organizations need an RCM partner with a solid foundation in data and data modeling. This is essential to being able to effectively harness the power of AI. In addition, the RCM partner must offer the supporting infrastructure to interface with referring clients, patients, and payers. This is necessary to maximize automation and smoothly coordinate RCM activities across the various stakeholders in the process.
Having good AI and insight into data and trends is important, but the ability to add automation to the RCM process based on the AI really solidifies the benefits and delivers a return on investment (ROI). Analytics are also essential for measuring and tracking performance over time and identifying opportunities for further improvement.
Diagnostic executives looking to maximize reimbursement and keep the cost of collection low will want to explore how to better leverage data, AI, automation, and analytics across their RCM process.
This is the third of a three-part series on revenue cycle management for molecular testing laboratories and pathology practices, produced in collaboration with XiFin Inc. Missed the first two articles? www.darkdaily.com
Radiological method using AI algorithms to detect, locate, and identify cancer could negate the need for invasive, painful clinical laboratory testing of tissue biopsies
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 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.
“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.
