Early detection can raise five-year survival rates above 90%, yet most ovarian cancer cases are found late. Emerging biomarker panels and AI-driven tools are empowering labs to make early diagnosis a reality.
For clinical laboratories, the fight against ovarian cancer highlights both the challenges and opportunities in early disease detection. Despite being one of the most difficult cancers to diagnose in its early stages, ovarian cancer outcomes improve dramatically when it’s caught early—underscoring the importance of laboratory innovation, diagnostic vigilance, and collaboration with clinicians. As researchers explore new biomarkers and AI-assisted tools for earlier, less invasive detection, lab professionals are positioned to play a pivotal role in advancing women’s health and improving survival rates.
Detecting ovarian cancer early is challenging but crucial for timely, effective treatment and improved survival. Too often, women are diagnosed after the disease has advanced. However, experts emphasize that the so-called “silent killer” doesn’t have to be silent—greater awareness of its warning signs and risk factors can make a life-saving difference.
“All women are at risk for gynecologic cancers, and risk increases with age,” explained Ruth Stephenson, DO, Gynecologic Oncologist at RWJBarnabas Health (RWJBH) and Rutgers Cancer Institute in a blog post. “If women suspect something isn’t right, for any reason, they shouldn’t hesitate. Early detection is their greatest asset.”
Stephenson encourages women to be proactive by maintaining regular health visits and being cognizant of their risk factors and the possible symptoms of ovarian cancer.
Other symptoms may include fatigue, upset stomach, back pain, pain during intercourse, constipation, menstrual cycle changes, and abdominal swelling.
Declines in Ovarian Cancer Cases Reflect Prevention Gains but Ongoing Risks Persist
Cases of ovarian cancer have been on the decline over the past several decades and ovarian cancer deaths have decreased by 43% since 1976, mostly due to increased use of oral contraceptives and lower use of hormonal therapies. According to the ACS, approximately 20,890 women will receive an ovarian cancer diagnosis in 2025 and about 12,730 women will die from the disease this year. Approximately half the diagnoses of ovarian cancer occur in women over the age of 63 and it is the sixth most common cancer among women in the US. A woman’s risk of getting the disease is about 1 in 91 and the risk of dying from ovarian cancer is approximately 1 in 143.
The cause of most ovarian cancers is unknown, but several aspects have been identified that may affect the risk for obtaining the illness, including:
Older age
Inherited gene mutations, such as BRCA1, BRCA2, or Lynch syndrome
Ruth Stephenson, DO, Gynecologic Oncologist at RWJBH and Rutgers Cancer Institute noted, “Knowing your family history of ovarian and breast cancers, listening to your body, and asking the right questions are among your strongest tools.”
The five-year survival rate for women diagnosed in Stage 1 of ovarian cancer is over 90%, but the survival rates decrease substantially when diagnosed in the later stages. Researchers have been using AI along with blood tests that combine protein and lipid markers to develop methods for earlier and less invasive detection of the disease. Other studies are being conducted to determine whether urine or vaginal samples can detect molecular changes linked to ovarian cancer.
Awareness Campaigns
In September, the ACS and Break Through Cancer announced a collaboration to advance awareness and prevention of ovarian cancer. “This alliance will turn two decades of scientific advances into action by combining research, education, awareness, marketing, and policy strategies to support those at risk of ovarian cancer and their clinicians,” the ACS said in a news release.
“The Outsmart Ovarian Cancer campaign seeks to close the gap between science and practice to ensure that patients and health care providers know the facts, the options, and have the potential to stop ovarian cancer before it starts,” said William Dahut, MD, chief scientific officer of the American Cancer Society. “This awareness campaign aims to give everyone their best chance to outsmart ovarian cancer.”
Detection and treatment options for ovarian cancer continue to improve and providing women with important information about the disease is part of a fundamental strategy for conquering the illness.
“With the American Cancer Society’s national platform and Break Through Cancer’s scientific engine, we are joining forces to bring this knowledge to millions of women,” said Tyler Jacks, PhD, president of Break Through Cancer. “The Outsmart Ovarian Cancer campaign is poised to share emerging research, inform patients, and support health care providers with resources and evolving prevention strategies.”
As awareness campaigns like Outsmart Ovarian Cancer bring renewed focus to prevention and early diagnosis, laboratories have an opportunity to strengthen their role as educators and innovators. Whether through developing and validating biomarker panels, participating in clinical trials, or helping providers interpret evolving screening data, labs can help bridge the gap between research and real-world care. In the ongoing effort to make ovarian cancer less “silent,” the laboratory’s voice—and its science—are essential.
New program draws bipartisan criticism and concern from patients and doctors.
Shrewd labs will keep an eye on the latest Centers for Medicare & Medicaid Services (CMS) prior authorization pilot that leans on artificial intelligence (AI) to determine treatment options for Medicare patients. While the Wasteful and Inappropriate Service Reduction Model pilot (WISeR) doesn’t directly mention lab tests, staying on the pulse of this growing trend will keep labs thinking ahead on how to minimize impact on bottom line, paperwork, and workflows when these pilots infiltrate lab testing.
An article from POLITICO reported that CMS will start a pilot version of the program as early as January 2026 in six states including Ohio, Texas, Oklahoma, Ariz., N.J., and Wash. Private AI companies will assist and focus on “services that have been vulnerable to fraud, waste and abuse in the past,” the article noted. The voluntary model is slated to span six years through December 31, 2031, according to the Centers for Disease Control and Prevention (CDC).
Among the types of procedures encumbered by the pilot program are knee arthroscopy for osteoarthritis, skin and tissue substitutions, and electrical nerve stimulator implants, CMS noted. All outpatient and emergency services would currently be excluded, they added, as well as “services that would pose a substantial risk to patients if substantially delayed.”
“All recommendations for non-payment will be determined by appropriately licensed clinicians who will apply standardized, transparent, and evidence-based procedures to their review,” CMS added.
The premise of the pilot is to eliminate wasteful spending, with CMS citing 25% of US healthcare spending falling in this category. “According to the Medicare Payment Advisory Commission Medicare spent up to $5.8 billion in 2022 on unnecessary or inappropriate services with little to no clinical benefit,” their website noted.
A Sour Reception
The pilot program is receiving a less-than-warm welcome from both parties—doctors, and patients alike, Politico noted. “It’s been referred to as the AI death panel. You get more money if you’re that AI tech company if you deny more claims. That is going to lead to people getting hurt,” Greg Landsman (D-Ohio) said during the committee hearing.
Landsman noted in the article from POLITICO that a bipartisan desire to put a halt to the program exists among growing concerns about patient harm coming from the program. Landsman “called for the program to be shut down until an independent review board could be erected to review the liability questions and ensure the AI prior authorization pilot doesn’t harm patients.”
“I’m concerned that this AI model will result in denials of lifesaving care and incentivize companies to restrict care,” Frank Pallone (D-N.J.) and House Energy and Commerce Committee ranking member said at the subcommittee meeting on the use of AI in health care held on Sept. 3.
“We have pretty good evidence that prior authorization as a process itself is fraught, adding that AI’s ability to improve the process for patients remains unproven,” Michelle Mello, Stanford University health law professor and witness at the hearing, said.
Looking Ahead
The involvement of AI in healthcare will only continue, and learning what aspects positively impact healthcare versus cause damage will continue to evolve.
Worth noting, there are already two unrelated lawsuits, against UnitedHealthcare and Cigna, that challenge the safety of AI use to deny patient care, POLITICO noted in the article.
Laboratory leaders should keep their eyes open and their ears to the ground on not only the pilot but all AI healthcare trends.
The NHS plans to map the DNA of all newborns in England over the next 10 years to evaluate their risk for hundreds of diseases
Clinical laboratories in the UK will soon see a new increase for specialized testing. Under a new 10-year plan by England’s National Health Service (NHS), every newborn baby in the country will have their complete DNA mapped using tiny blood samples taken from their umbilical cords shortly after birth. The blood samples will assess an infant’s risk for hundreds of diseases with the intent of predicting and preventing those illnesses while mitigating demand for services and saving money. The plan itself is set to be revealed in the near future.
In October, the NHS announced it would be analyzing the genetic code of up to 100,000 newborns via a small drop of blood collected from the heel. Currently, testing usually occurs when a baby is five days old and looks for nine rare, but serious gene disorders that develop in early childhood and have effective treatments available.
The new umbilical cord testing utilizes genomics, artificial intelligence (AI), predictive analytics, and other technologies to provide faster diagnoses and treatments for approximately 7,000 single-gene disorders.
“With the power of this new technology, patients will be able to receive personalized healthcare to prevent ill-health before symptoms begin, reducing the pressure on NHS services and helping people live longer, healthier lives,” stated Wes Streeting, NHS Secretary of State for Health and Social Care in a statement.
Wes Streeting, NHS Secretary of State for Health and Social Care said, “”Genomics presents us with the opportunity to leapfrog disease, so we’re in front of it rather than reacting to it.” (Photo credit: GOV.UK)
This new, whole genome sequencing procedure for infants is part of a 10-year plan by the NHS to establish major shifts in how healthcare is delivered in the UK to improve the quality of care and increase transparency. The intent is to move delivery from hospital to community, which includes the implementation of “neighborhood health teams” to aggregate services. The plan also includes transitioning from analog to digital methods and from treating illnesses to preventing them.
“Our 10-year plan will build on the founding promise of the NHS, so that it provides health care free at the point of risk, not just need,” said Streeting.
He also stated technological advances will help alleviate pressures on the NHS and contribute to its future success, thus improving the overall health of the population.
“As we deliver the transformational shifts in our 10-year plan, from hospital to community, analog to digital, and sickness to prevention, it will have radical implications for services,” he said. “Much of what’s done in a hospital today will be done on the high street, over the phone, or through the app in a decade’s time.”
While many scientists, doctors and patient advocacy groups applaud this testing on infants, there are some concerns it may spark an ethical debate. Parents will have the ability to give consent, but the testing may result in information they may not want to know, which could have a negative psychological effect on children and parents who are aware they have a higher risk for certain diseases.
There are also concerns regarding the security of crucial patient data and how such information can be prone to security breaches. The DNA and health records of infants are stored on secure systems and require strict authorization to access, but this type of data is very valuable to cyber hackers. The NHS has stressed it is prioritizing digital security measures, including vigorous cybersecurity, data governance and the implementation of ethical guidelines for AI development.
Robin Lovell-Badge, PhD, principal group leader and head of the Laboratory of Stem Cell Biology and Developmental Genetics at the Francis Crick Institute also noted some reservations he has regarding how the collected data is provided to patients.
“You need people to have conversations with individuals who might be affected by genetic disease,” he said. “One of the things that worries me was an insufficient number of genetic counselors. It’s not just having the information, it’s conveying the information in an appropriate, helpful way.”
As technological advances become increasingly prevalent in medicine, clinical labs will be at the forefront of new initiatives such as the 10-year plan by the NHS.
New AI tool doubled efficiency in busy university radiology department
Creative artificial intelligence (AI) solutions are being developed to address critical staffing shortages in radiology that could help with similar shortages in overworked pathology and clinical laboratories as well.
In a recent clinical study at 11-hospital Northwestern Medicine, researchers developed a new generative AI radiology tool to assist radiologists that demonstrates high accuracy and efficiency rates when working with multiple types of imaging scans.
For the study, approximately 24,000 radiology reports were analyzed and then compared for clinical accuracy with and without the AI tool. The tool evaluates an entire scan and generates a report that is 95% complete and personalized to each patient. A template based on that report is then provided to radiologists for review, according to a Northwestern Medicine Feinberg School of Medicine news release.
The study reported an average 15.5% increase in radiograph efficiency without compromising accuracy. Some radiologists even produced gains as high as 40%. The radiology reports were scrutinized during a five-month period last year and enabled radiologists to improve the time it took to return a diagnosis.
“This is, to my knowledge, the first use of AI that demonstrably improves productivity, especially in healthcare. Even in other fields, I haven’t seen anything close to a 40% boost,” said the study’s senior author Mozziyar Etemadi, MD, PhD, assistant professor of anesthesiology and biomedical engineering at Northwestern University McCormick School of Engineering, in the news release. (Photo copyright: Northwestern University.)
Doubled Efficiency for One Radiology Team
“For me and my colleagues, it’s not an exaggeration to say that it doubled our efficiency. It’s such a tremendous advantage and force multiplier,” said study co-author Samir Abboud, MD, emergency radiology in the department of radiology at Northwestern Medicine, in the news release.
“Having a draft report available, even before it is viewed by the radiologist, offers a simple, actionable datapoint that can be quickly and efficiently acted upon” added study senior author Mozziyar Etemadi, MD, PhD, assistant professor of anesthesiology and biomedical engineering at Northwestern University McCormick School of Engineering, in the news release. “This is completely different than traditional triage systems, which need to meticulously be trained one by one on each and every diagnosis.”
The AI tool can also alert radiologists to life-threatening conditions.
“On any given day in the ER, we might have 100 images to review, and we don’t know which one holds a diagnosis that could save a life,” Abboud said. “This technology helps us triage faster—so we catch the most urgent cases sooner and get patients to treatment quicker.”
Relying on In-house Data
Engineers at Northwestern developed the AI model using clinical data within the university’s own network, emphasizing that such tools can be created without assistance from other organizations.
“Our study shows that building custom AI models is well within reach of a typical health system, without reliance on expensive and opaque third-party tools like ChatGPT,” Etemadi noted.
The Journal of the American College of Radiology states the supply of radiologists is expected to increase by approximately 26% over the next 30 years. However, the need for radiologists is expected to grow between 17% and 27% over the same period. Becker’s Hospital Review reports there will be a shortage of up to 42,000 radiologists in the US by 2033.
Some health organizations are using a mixed model of permanent employees and contracted radiologists to meet the increasing demand for services. Others are also looking at options such as internal training programs, better benefits for workers, teleradiology, and remote radiologists to fulfill radiology needs.
“You still need a radiologist as the gold standard,” Abboud said. “Medicine changes constantly—new drugs, new devices, new diagnoses—and we have to make sure the AI keeps up. Our role becomes ensuring every interpretation is right for the patient.”
Can pathology practices and clinical laboratories learn from radiology’s situation? Development of AI solutions for those fields would likely have similar effects on workloads and overworked personnel.
Exploring the benefits of AI may be one way of helping meet clinical laboratory and pathology practice staff shortages.
Researchers in Sweden develop urine test that more effectively screens for prostate cancer than standard PSA test
Clinical laboratories may soon have a new inexpensive, non-invasive urine test to screen for prostate cancer that produces superior results compared to the standard PSA test.
An international team of scientists led by researchers at the Karolinska Institutet in Sweden found they could use machine learning to not only accurately identify the presence of a new set cancer biomarkers in urine samples but also determine the stage or grade of the cancer.
“There are many advantages to measuring biomarkers in urine,” said Mikael Benson, principal researcher in the Department of Clinical Science, Intervention and Technology at Karolinska Institutet and senior investigator for the study, in a news release. “It’s non-invasive and painless and can potentially be done at home. The sample can then be analyzed using routine methods in clinical labs.”
“New, more precise biomarkers than PSA can lead to earlier diagnosis and better prognoses for men with prostate cancer,” said Mikael Benson, principal researcher at Karolinska Institutet and senior investigator for the study, in a news release. “Moreover, it can reduce the number of unnecessary prostate biopsies in healthy men.” (Photo copyright: Karolinska Institutet.)
New Prostate Cancer Biomarkers
According to the American Cancer Society, there will be approximately 313,780 new cases of prostate cancer diagnosed this year in the US with about 35,770 deaths due to the disease. About one in eight US men will be diagnosed with prostate cancer in their lifetime, and the lifetime risk of dying from prostate cancer is one in 44 men.
“Early cancer diagnosis is crucial but challenging owing to the lack of reliable biomarkers that can be measured using routine clinical methods. The identification of biomarkers for early detection is complicated by each tumor involving changes in the interactions between thousands of genes. In addition to this staggering complexity, these interactions can vary among patients with the same diagnosis as well as within the same tumor,” the researchers wrote in Cancer Research.
The scientists “hypothesized that reliable biomarkers that can be measured with routine methods could be identified by exploiting three facts:
The same tumor can have multiple grades of malignant transformation;
These grades and their molecular changes can be characterized using spatial transcriptomics; and,
These changes can be integrated into models of malignant transformation using pseudotime models to prioritize the genes that were most correlated with malignant transformation.”
To perform their study, the scientists analyzed the mRNA activity of cells in prostate tumors to construct digital models of prostate cancer. These models were then examined using machine learning, a type of artificial intelligence (AI), to locate specific proteins that could be used as biomarkers.
The researchers evaluated these new biomarkers in urine, blood, and tissue samples from more than 2,000 prostate cancer patients along with a control group. The team’s final calculations found the results of the urine test surpassed the current PSA test traditionally used for diagnosing prostate cancer.
“Prostate cancer can be effectively identified by analyzing the expression of candidate biomarkers in urine,” lead study author Martin Smelik, PhD student at Karolinska Institutet, told Fox News. “This approach outperforms the current blood tests based on PSA, but at the same time keeps the advantages of being non-invasive, painless, and relatively cheap.”
Advancements over Traditional PSA Test
Although the prostate-specific antigen (PSA) test typically used by doctors to diagnose prostate cancer can screen for the disease and monitor its progression, it has limitations.
“While PSA is an incredibly sensitive tool for issues related to the prostate, it is not specific to prostate cancer,” Matthew Abramowitz, MD, associate professor in the Department of Radiation Oncology at the Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, told Fox News. “The techniques proposed in the current study suggest the promise of identifying specific cancer markers in the urine, minimizing some of the specificity concerns associated with PSA.”
“This study highlights the power of machine learning applied to patient data in identifying breakthroughs that can help us diagnose cancer earlier, when our treatments are most effective,” Timothy Showalter, MD, a radiation oncologist at UVA Health in Virginia, told Fox News. “Prostate cancer screening has not seen a transformative advance in decades, and current approaches still rely on the PSA blood test, which is known to have low specificity for clinically significant cancers.”
“Overall, this study demonstrates the diagnostic potential of combining spatial transcriptomics, pseudotime, and machine learning for prostate cancer, which should be further tested in prospective studies,” the researchers wrote.
The Karolinska Institutet team is planning large-scale clinical trials as the next phase of their exploration.
