Findings suggest new medical guidelines may be needed to determine when to perform clinical laboratory cancer screenings on people under 50
From 1990-2019, new diagnoses of early-onset cancer in individuals under 50 years of age increased by 79%, according to a British Medical Journal (BMJ) news release describing research published last year in BMJ Oncology. The question for anatomic pathology laboratories to consider is, why are more people under 50 being diagnosed with cancer than in earlier years? And do medical guidelines need to be changed to allow more cancer screening for individuals under 50-years old?
This new revelation challenges previously held beliefs about the number of younger adults under 50 experiencing early-onset cancer. Patients can sometimes miss symptoms by attributing them to a more benign condition.
“While cancer tends to be more common in older people, the evidence suggests that cases among the under 50s have been rising in many parts of the world since the 1990s. But most of these studies have focused on regional and national differences; and few have looked at the issue from a global perspective or the risk factors for younger adults, say the researchers. In a bid to plug these knowledge gaps, they drew on data from the Global Burden of Disease 2019 Study for 29 cancers in 204 countries and regions,” the BMJ news release states.
According to the news release, “Breast cancer accounted for the highest number of ‘early-onset’ cases in this age group in 2019. But cancers of the windpipe (nasopharynx) and prostate have risen the fastest since 1990, the analysis reveals. Cancers exacting the heaviest death toll and compromising health the most among younger adults in 2019 were those of the breast, windpipe, lung, bowel, and stomach.”
Although these statistics are being seen worldwide, the highest rates are in North America, Australasia, and Western Europe. However, high death rates due to cancer are also being seen in Eastern Europe, Central Asia, and Oceania. Economic disparities in the latter geographical regions may account for both fewer diagnoses and higher death rates.
“And in low to middle income countries, early onset cancer had a much greater impact on women than on men, in terms of both deaths and subsequent poor health,” the BMJ news release noted.
In an editorial they published in BMJ Oncology on the study findings, Ashleigh Hamilton, PhD (left), Academic Clinical Lecturer, and Helen Coleman, PhD (right), Professor, School of Medicine, Dentistry and Biomedical Sciences, both at the Center for Public Health at Queen’s University Belfast in the UK wrote, “The epidemiological landscape of cancer incidence is changing. … Prevention and early detection measures are urgently required, along with identifying optimal treatment strategies for early-onset cancers, which should include a holistic approach addressing the unique supportive care needs of younger patients.” Anatomic pathology laboratories will play an important role in diagnosing and treating younger cancer patients. (Photo copyrights: Queen’s University Belfast.)
What Caused the Increase?
“It’s such an important question, and it points to the need for more research in all kinds of domains—in population science, behavioral health, public health, and basic science as well,” said medical oncologist Veda Giri, MD, Professor of Internal Medicine, Yale School of Medicine, in a news release. Giri directs the Yale Cancer Center Early-Onset Cancer Program at Smilow Cancer Hospital.
Although experts are still trying to determine exactly where these cases are coming from, signs point to both genetic and lifestyle factors, the BMJ news releases noted. Tobacco and alcohol use, diets high in cholesterol and sodium, and physical inactivity are all lifestyle risk factors. Experts recommend a healthy diet and exercise routine with minimal alcohol consumption.
As for family history? “We’re beginning to recognize that family history is very important,” says Jeremy Kortmansky, MD, also a Yale Medicine medical oncologist.
According to CNN Health, these rates of early-onset cancer are more common in female patients, with rates going up an average of 0.67% each year.
“For young women who have a significant family history of cancer in the family, we are starting to refer them to a high-risk clinic—even if the cancer in their family is not breast cancer,” Kortmansky noted.
Doctors advise patients to implement healthy habits into their lives, not ignore symptoms, advocate for themselves, and be aware of their family history. Cancer patients may be prescribed cancer treatments at a much earlier age. Medical guidelines for patients may continue to shift and change. And oncologists may be incorporating alternative therapies to help younger patients deal with the shock of their diagnosis.
Will Cancer Rates Continue to Rise?
“Based on the observed trends for the past three decades, the researchers estimate that the global number of new early-onset cancer cases and associated deaths will rise by a further 31% and 21% respectively in 2030, with those in their 40s the most at risk,” the BMJ news release noted.
In an editorial they penned for BMJ Oncology on the findings of the cancer study titled, “Shifting Tides: The Rising Tide of Early-Onset Cancers Demands Attention,” Ashleigh Hamilton, PhD, Academic Clinical Lecturer, and Helen Coleman, PhD, Professor, School of Medicine, Dentistry and Biomedical Sciences, both at the Center for Public Health at Queen’s University Belfast in the UK wrote, “Full understanding of the reasons driving the observed trends remains elusive, although lifestyle factors are likely contributing, and novel areas of research such as antibiotic usage, the gut microbiome, outdoor air pollution, and early life exposures are being explored. It is crucial that we better understand the underlying reasons for the increase in early-onset cancers, in order to inform prevention strategies.”
Clinical laboratories should be aware of these findings and the changing landscape of cancer screenings, as they will play a key role in diagnoses. Younger patients may be advocating for cancer screenings and doctors may be ordering them depending on the patient’s symptoms and family history. Anatomic pathology professionals should expect new guidelines when it comes to cancer diagnostics and treatment.
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
Some hospital organizations are pushing back, stating that the new regulations are ‘too rigid’ and interfere with doctors’ treatment of patients
In August, the Biden administration finalized provisions for hospitals to meet specific treatment metrics for all patients with suspected sepsis. Hospitals that fail to meet these requirements risk the potential loss of millions of dollars in Medicare reimbursements annually. This new federal rule did not go over well with some in the hospital industry.
Sepsis kills about 350,000 people every year. One in three people who contract the deadly blood infection in hospitals die, according to the Centers for Disease Control and Prevention (CDC). Thus, the federal government has once again implemented a final rule that requires hospitals, clinical laboratories, and medical providers to take immediate actions to diagnose and treat sepsis patients.
The effort has elicited pushback from several healthcare organizations that say the measure is “too rigid” and “does not allow clinicians flexibility to determine how recommendations should apply to their specific patients,” according to Becker’s Hospital Review.
Perform blood tests within a specific period of time to look for biomarkers in patients that may indicate sepsis, and to
Administer antibiotics within three hours after a possible case is identified.
It also mandates that certain other tests are performed, and intravenous fluids administered, to prevent blood pressure from dipping to dangerously low levels.
“These are core things that everyone should do every time they see a septic patient,” said Steven Simpson, MD, Professor of medicine at the University of Kansas told Fierce Healthcare. Simpson is also the chairman of the Sepsis Alliance, an advocacy group that works to battle sepsis.
Simpson believes there is enough evidence to prove that the SEP-1 guidelines result in improved patient care and outcomes and should be enforced.
“It is quite clear that this works better than what was present before, which was nothing,” he said. “If the current sepsis mortality rate could be cut by even 5%, we could save a lot of lives. Before, even if you were reporting 0% compliance, you didn’t lose your money. Now you actually have to do it,” Simpson noted.
“We are encouraged by the increased attention to sepsis and support CMS’ creation of a sepsis mortality measure that will encourage hospitals to pay more attention to the full breadth of sepsis care,” Chanu Rhee, MD (above), Infectious Disease/Critical Care Physician and Associate Hospital Epidemiologist at Brigham and Women’s Hospital told Healthcare Finance. The new rule, however, requires doctors and medical laboratories to conduct tests and administer antibiotic treatment sooner than many healthcare providers deem wise. (Photo copyright: Brigham and Women’s Hospital.)
Healthcare Organizations Pushback against Final Rule
“By encouraging the use of broad spectrum antibiotics when more targeted ones will suffice, this measure promotes the overuse of the antibiotics that are our last line of defense against drug-resistant bacteria,” the AHA’s letter states.
In its recent coverage of the healthcare organizations’ pushback to CMS’ final rule, Healthcare Finance News explained, “The SEP-1 measure requires clinicians to provide a bundle of care to all patients with possible sepsis within three hours of recognition. … But the SEP-1 measure doesn’t take into account that many serious conditions present in a similar fashion to sepsis … Pushing clinicians to treat all these patients as if they have sepsis … leads to overuse of broad-spectrum antibiotics, which can be harmful to patients who are not infected, those who are infected with viruses rather than bacteria, and those who could safely be treated with narrower-spectrum antibiotics.”
CMS’ latest rule follows the same evolutionary path as previous federal guidelines. In August 2007, CMS announced that Medicare would no longer pay for additional costs associated with preventable errors, including situations known as Never Events. These are “adverse events that are serious, largely preventable, and of concern to both the public and healthcare providers for the purpose of public accountability,” according to the Leapfrog Group.
In 2014, the CDC suggested that all US hospitals have an antibiotic stewardship program (ASP) to measure and improve how antibiotics are prescribed by clinicians and utilized by patients.
Research Does Not Show Federal Sepsis Programs Work
He points to analysis which showed that though use of broad-spectrum antibiotics increased after the original 2015 SEP-1 regulations were introduced, there has been little change to patient outcomes.
“Unfortunately, we do not have good evidence that implementation of the sepsis policy has led to an improvement in sepsis mortality rates,” Rhee told Fierce Healthcare.
Rhee believes that the latest regulations are a step in the right direction, but that more needs to be done for sepsis care. “Retiring past measures and refining future ones will help stimulate new innovations in diagnosis and treatment and ultimately improve outcomes for the many patients affected by sepsis,” he told Healthcare Finance.
Sepsis is very difficult to diagnose quickly and accurately. Delaying treatment could result in serious consequences. But clinical laboratory blood tests for blood infections can take up to three days to produce a result. During that time, a patient could be receiving the wrong antibiotic for the infection, which could lead to worse problems.
The new federal regulation is designed to ensure that patients receive the best care possible when dealing with sepsis and to lower mortality rates in those patients. It remains to be seen if it will have the desired effect.
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.
Researchers intend their new AI image retrieval tool to help pathologists locate similar case images to reference for diagnostics, research, and education
Researchers at Stanford University turned to an unusual source—the X social media platform (formerly known as Twitter)—to train an artificial intelligence (AI) system that can look at clinical laboratory pathology images and then retrieve similar images from a database. This is an indication that pathologists are increasingly collecting and storing images of representative cases in their social media accounts. They then consult those libraries when working on new cases that have unusual or unfamiliar features.
The Stanford Medicine scientists trained their AI system—known as Pathology Language and Image Pretraining (PLIP)—on the OpenPath pathology dataset, which contains more than 200,000 images paired with natural language descriptions. The researchers collected most of the data by retrieving tweets in which pathologists posted images accompanied by comments.
“It might be surprising to some folks that there is actually a lot of high-quality medical knowledge that is shared on Twitter,” said researcher James Zou, PhD, Assistant Professor of Biomedical Data Science and senior author of the study, in a Stanford Medicine SCOPE blog post, which added that “the social media platform has become a popular forum for pathologists to share interesting images—so much so that the community has widely adopted a set of 32 hashtags to identify subspecialties.”
“It’s a very active community, which is why we were able to curate hundreds of thousands of these high-quality pathology discussions from Twitter,” Zou said.
“The main application is to help human pathologists look for similar cases to reference,” James Zou, PhD (above), Assistant Professor of Biomedical Data Science, senior author of the study, and his colleagues wrote in Nature Medicine. “Our approach demonstrates that publicly shared medical information is a tremendous resource that can be harnessed to develop medical artificial intelligence for enhancing diagnosis, knowledge sharing, and education.” Leveraging pathologists’ use of social media to store case images for future reference has worked out well for the Stanford Medicine study. (Photo copyright: Stanford University.)
Retrieving Pathology Images from Tweets
“The lack of annotated publicly-available medical images is a major barrier for innovations,” the researchers wrote in Nature Medicine. “At the same time, many de-identified images and much knowledge are shared by clinicians on public forums such as medical Twitter.”
In this case, the goal “is to train a model that can understand both the visual image and the text description,” Zou said in the SCOPE blog post.
“Pathology is perhaps even more suited to Twitter than many other medical fields because for most pathologists, the bulk of our daily work revolves around the interpretation of images for the diagnosis of human disease,” wrote Jerad M. Gardner, MD, a dermatopathologist and section head of bone/soft tissue pathology at Geisinger Medical Center in Danville, Pa., in a blog post about the Pathology Hashtag Ontology project. “Twitter allows us to easily share images of amazing cases with one another, and we can also discuss new controversies, share links to the most cutting edge literature, and interact with and promote the cause of our pathology professional organizations.”
The researchers used the 32 subspecialty hashtags to retrieve English-language tweets posted from 2006 to 2022. Images in the tweets were “typically high-resolution views of cells or tissues stained with dye,” according to the SCOPE blog post.
The researchers collected a total of 232,067 tweets and 243,375 image-text pairs across the 32 subspecialties, they reported. They augmented this with 88,250 replies that received the highest number of likes and had at least one keyword from the ICD-11 codebook. The SCOPE blog post noted that the rankings by “likes” enabled the researchers to screen for high-quality replies.
They then refined the dataset by removing duplicates, retweets, non-pathology images, and tweets marked by Twitter as being “sensitive.” They also removed tweets containing question marks, as this was an indicator that the practitioner was asking a question about an image rather than providing a description, the researchers wrote in Nature Medicine.
They cleaned the text by removing hashtags, Twitter handles, HTML tags, emojis, and links to websites, the researchers noted.
The final OpenPath dataset included:
116,504 image-text pairs from Twitter posts,
59,869 from replies, and
32,041 image-text pairs scraped from the internet or obtained from the LAION dataset.
The latter is an open-source database from Germany that can be used to train text-to-image AI software such as Stable Diffusion.
Training the PLIP AI Platform
Once they had the dataset, the next step was to train the PLIP AI model. This required a technique known as contrastive learning, the researchers wrote, in which the AI learns to associate features from the images with portions of the text.
As explained in Baeldung, an online technology publication, contrastive learning is based on the idea that “it is easier for someone with no prior knowledge, like a kid, to learn new things by contrasting between similar and dissimilar things instead of learning to recognize them one by one.”
“The power of such a model is that we don’t tell it specifically what features to look for. It’s learning the relevant features by itself,” Zou said in the SCOPE blog post.
The resulting AI PLIP tool will enable “a clinician to input a new image or text description to search for similar annotated images in the database—a sort of Google Image search customized for pathologists,” SCOPE explained.
“Maybe a pathologist is looking at something that’s a bit unusual or ambiguous,” Zou told SCOPE. “They could use PLIP to retrieve similar images, then reference those cases to help them make their diagnoses.”
The Stanford University researchers continue to collect pathology images from X. “The more data you have, the more it will improve,” Zou said.
Pathologists will want to keep an eye on the Stanford Medicine research team’s progress. The PLIP AI tool may be a boon to diagnostics and improve patient outcomes and care.
