Two studies show the accuracy of perception-based systems in detecting disease biomarkers without needing molecular recognition elements, such as antibodies
Researchers from multiple academic and research institutions have collaborated to develop a non-conventional machine learning-based technology for identifying and measuring biomarkers to detect ovarian cancer without the need for molecular identification elements, such as antibodies.
Traditional clinical laboratory methods for detecting biomarkers of specific diseases require a “molecular recognition molecule,” such as an antibody, to match with each disease’s biomarker. However, according to a Lehigh University news release, for ovarian cancer “there’s not a single biomarker—or analyte—that indicates the presence of cancer.
“When multiple analytes need to be measured in a given sample, which can increase the accuracy of a test, more antibodies are required, which increases the cost of the test and the turnaround time,” the news release noted.
Unveiled in two sequential studies, the new method for detecting ovarian cancer uses machine learning to examine spectral signatures of carbon nanotubes to detect and recognize the disease biomarkers in a very non-conventional fashion.
Perception-based Nanosensor Array for Detecting Disease
In the Science Advances paper, the researchers described their development of “a perception-based platform based on an optical nanosensor array that leverages machine learning algorithms to detect multiple protein biomarkers in biofluids.
“Perception-based machine learning (ML) platforms, modeled after the complex olfactory system, can isolate individual signals through an array of relatively nonspecific receptors. Each receptor captures certain features, and the overall ensemble response is analyzed by the neural network in our brain, resulting in perception,” the researchers wrote.
“This work demonstrates the potential of perception-based systems for the development of multiplexed sensors of disease biomarkers without the need for specific molecular recognition elements,” the researchers concluded.
In the Nature Biomedical Engineering paper, the researchers described a fined-tuned toolset that could accurately differentiate ovarian cancer biomarkers from biomarkers in individuals who are cancer-free.
“Here we show that a ‘disease fingerprint’—acquired via machine learning from the spectra of near-infrared fluorescence emissions of an array of carbon nanotubes functionalized with quantum defects—detects high-grade serous ovarian carcinoma in serum samples from symptomatic individuals with 87% sensitivity at 98% specificity (compared with 84% sensitivity at 98% specificity for the current best [clinical laboratory] screening test, which uses measurements of cancer antigen 125 and transvaginal ultrasonography,” the researchers wrote.
“We demonstrated that a perception-based nanosensor platform could detect ovarian cancer biomarkers using machine learning,” said Yoona Yang, PhD, a postdoctoral research associate in Lehigh’s Department of Chemical and Biomolecular Engineering and co-first author of the Science Advances article, in the news release.
How Perception-based Machine Learning Platforms Work
According to Yang, perception-based sensing functions like the human brain.
“The system consists of a sensing array that captures a certain feature of the analytes in a specific way, and then the ensemble response from the array is analyzed by the computational perceptive model. It can detect various analytes at once, which makes it much more efficient,” Yang said.
“SWCNTs have unique optical properties and sensitivity that make them valuable as sensor materials. SWCNTS emit near-infrared photoluminescence with distinct narrow emission bands that are exquisitely sensitive to the local environment,” the researchers wrote in Science Advances.
“Carbon nanotubes have interesting electronic properties,” said Daniel Heller, PhD, Head of the Cancer Nanotechnology Laboratory at Memorial Sloan Kettering Cancer Center and Associate Professor in the Department of Pharmacology at Weill Cornell Medicine of Cornell University, in the Lehigh University news release.
“If you shoot light at them, they emit a different color of light, and that light’s color and intensity can change based on what’s sticking to the nanotube. We were able to harness the complexity of so many potential binding interactions by using a range of nanotubes with various wrappings. And that gave us a range of different sensors that could all detect slightly different things, and it turned out they responded differently to different proteins,” he added.
The researchers put their technology to practical test in the second study. The wanted to learn if it could differentiate symptomatic patients with high-grade ovarian cancer from cancer-free individuals.
The research team used 269 serum samples. This time, nanotubes were bound with a specific molecule providing “an extra signal in terms of data and richer data from every nanotube-DNA combination,” said Anand Jagota PhD, Professor, Bioengineering and Chemical and Biomolecular Engineering, Lehigh University, in the news release.
This year, 19,880 women will be diagnosed with ovarian cancer and 12,810 will die from the disease, according to American Cancer Society data. While more research and clinical trials are needed, the above studies are compelling and suggest the possibility that one day clinical laboratories may detect ovarian cancer faster and more accurately than with current methods.
This is an important development because the turn-around-time (TAT) for common lab tests is generally much shorter than COVID-19 molecular diagnostics—such as real-time reverse transcription polymerase chain reaction (RT-PCR), currently the most popular coronavirus test—and certainly serological (antibody) diagnostics, which require an infection incubation time of as much as 10-14 days before testing.
Some RT-PCR diagnostic tests for COVID-19, which detect viral RNA on nasopharyngeal swab specimens, can take up to several days to return depending on the test and on the lab’s location. But routine medical laboratory tests generally return much sooner, often within minutes or hours, making this a potential game-changer for triaging infected patients.
Machine Learning Brings AI to COVID-19 Diagnostics
Advances in the use of AI in healthcare have led to the development of machine-learning algorithms that are being utilized as diagnostic tools for anatomic pathology, radiology, and for specific complex diseases, such as cancer. The Weill Cornell scientists wanted to see if alternative lab test results could be used by an algorithmic model to identify people infected with the SARS-CoV-2 coronavirus.
To perform the research, the team incorporated patients’ age, sex, and race, into a machine learning model that was based on results from 27 routine lab tests chosen from a total of 685 different tests ordered for the patients. The study included 3,356 patients who were tested for SARS-CoV-2 at New York-Presbyterian Hospital/Weill Cornell Medical Center between March 11 and April 29 of this year. The patients ranged in ages from 18 to 101 with the mean age being 56.4 years. Of those patients, 1,402 were RT-PCR positive and the remaining 1,954 were RT-PCR negative.
Using a machine-learning technique known as a gradient-boosting decision tree, the algorithm identified SARS-CoV-2 infections with 76% sensitivity and 81% specificity. When looking at only emergency department (ED) patients, the model performed even better with 80% sensitivity and 83% specificity. ED patients comprised just over half (54%) of the patients used for the study.
Weill Cornell Medicine Algorithm Could Lower False Negative Test Results
The algorithm also correctly identified patients who originally tested negative for COVID-19, but who tested positive for the coronavirus upon retesting within two days. According to the researchers, these results indicated their model could potentially decrease the amount of incorrect test results.
“We are thinking that those potentially false negative patients may demonstrate a different routine lab test profile that might be more similar to those that test positive,” Fei Wang, PhD, Assistant Professor of Healthcare Policy and Research at Weill Cornell Medicine and the study’s senior author, told Modern Healthcare. “So, it offers us a chance to capture those patients who are false negatives.”
The researchers validated their model by comparing the results with patients seen at New York Presbyterian Hospital/Lower Manhattan Hospital during the same time period. Among those patients, 496 were RT-PCR positive and 968 were negative and the algorithmic model performed with 74% specificity and 76% sensitivity.
preliminarily identify high-risk SARS-CoV-2 infected patients before RT-PCR results are available,
risk stratify patients in the ED,
select patients who need relatively urgent retesting if initial RT-PCR results are negative,
help isolate infected patients earlier, and
assist in the identification of SARS-CoV-2 infected patients in areas where RT-PCR testing is unavailable due to financial or supply constraints.
Early Results of Study Promising, But More Research is Needed
Wang noted that more research is needed on the algorithm and that he and his colleagues are currently working on ways to improve the model. They are hoping to test it with different conditions and geographies.
“Our model in the paper was built on data from when New York was at its COVID peak,” he told Modern Healthcare. “At that time, we were not doing wide PCR testing, and the patients who were getting tested were pretty sick.”
At the time of the study, the positivity rate for COVID-19 at New York-Presbyterian Hospital was in the 40% to 50% range. That was substantially higher than the current positivity rate, which is in the 2% to 3% range, Modern Healthcare reported.
“This model we built in a population in New York in a certain time period, so we can’t guarantee that it will work well universally,” Wang told Modern Healthcare.
It’s exciting to think that advances in software algorithms may one day make it possible to combine routine clinical laboratory testing and create diagnostics that identify diseases in ways the individual tests were not originally designed to do.
This study is an example that researchers in AI and informatics are working to bring new tools and diagnostic capabilities to clinical laboratories. Also, this is a demonstration of how a patient’s results from multiple other types of lab tests can by analyzed using AI and similar analytical algorithms to diagnose a health condition unrelated to the original reasons for performing those tests.
If this can be demonstrated with other diseases and health conditions, it would open up one more way that pathologists and clinical laboratory scientists can contribute to more accurate diagnoses and improved selection of the most appropriate therapies for individual patients.
Doctors may begin ordering FITs in greater numbers, increasing the demand on clinical laboratories to process these home tests
All clinical laboratory managers and pathologists know that timely screening for colon cancer is an effective way to detect cancer early, when it is easiest to treat. But, invasive diagnostic approaches such as colonoscopies are not popular with consumers. Now comes news of a large-scale study that indicates the non-invasive fecal immunochemical test (FIT) can be as effective as a colonoscopy when screening for colon cancer.
The researchers published their findings in the Annals of Internal Medicine (AIM), a journal published by the American College of Physicians (ACP). The team reviewed data from 31 previous studies. They then analyzed the test results from more than 120,000 average-risk patients who took a FIT and then had a colonoscopy. After comparing the results between the two tests, the researchers concluded that the FIT is a sufficient screening tool for colon cancer.
FIT is Easy, Safe, and Inexpensive
As a medical laboratory test, the FIT is low risk, non-invasive,
and inexpensive. In addition, the FIT can detect most cancers in the first
application, according to the Regenstrief Institute researchers. They recommend
that the FIT be performed on an annual basis for people at average risk for
getting colorectal cancers.
“This non-invasive test for colon cancer screening is available for average risk people,” Imperiale told NBC News. “They should discuss with their providers whether it is appropriate for them.”
FIT is performed in the privacy of the patient’s home. To
use the test, an individual collects a bowel specimen in a receptacle provided
in a FIT kit. They then send the specimen to a clinical laboratory for
evaluation. The FIT requires no special preparations and medicines and food do
not interfere with the test results.
‘A Preventative Health Success Story’
The FIT can be calibrated to different sensitivities at the
lab when determining results. Imperiale and his team found that 95% of cancers
were detected when the FIT was set to a higher sensitivity, however, that
setting resulted in 10% false positives. At lower sensitivity the FIT produced
fewer false positives (5%), but also caught fewer cancers (75%). However, when
the FIT was performed every year, the cancer detection rate was similar at both
sensitivities over a two-year period.
“FIT is an excellent option for colon cancer screening only if it is performed consistently on a yearly basis,” Felice Schnoll-Sussman, MD, told NBC News. Sussman is a gastroenterologist and Professor of Clinical Medicine at Weill Cornell Medicine. “Colon cancer screening and its impact on decreasing rates of colon cancer is a preventative health success story, although we have a way to go to increase rates to our previous desired goal of 80% screened in the US by 2018.”
The FIT looks for hidden blood in the stool by detecting protein hemoglobin found in red blood cells. A normal result indicates that FIT did not detect any blood in the stool and the test should be repeated annually. If the FIT comes back positive for blood in the stool, other tests, such as a sigmoidoscopy or colonoscopy should be performed. Cancers in the colon may not always bleed and the FIT only detects blood from the lower intestines.
Patients are Skipping the Colonoscopy
Approximately 35% of individuals who should be receiving colonoscopies do not undergo the test, NBC News noted. The American Cancer Society (ACS) lists the top five reasons people don’t get screened for colorectal cancer are that they:
fear the test will be difficult or painful;
have no family history of the disease and feel
testing is unnecessary;
have no symptoms and think screening is only for
those with symptoms;
are concerned about the costs associated with
they are concerned about the complexities of
taking the tests, including taking time off from work, transportation after the
procedure, and high out-of-pocket expenses.
“Colorectal cancer screening is one of the best opportunities to prevent cancer or diagnose it early, when it’s most treatable,” Richard Wender, MD, Chief Cancer Control Officer for the ACS stated in a press release. “Despite this compelling reason to be screened, many people either have never had a colorectal cancer screening test or are not up to date with screening.”
Colorectal cancer is the third most common cancer diagnosed in both men and women in the United States. The ACS estimates there will be 101,420 new cases of colon cancer and 44,180 new cases of rectal cancer diagnosed this year. The disease is expected to be responsible for approximately 51,020 deaths in 2019.
New cases of the disease have been steadily decreasing over
the past few decades in most age populations, primarily due to early screening.
However, the overall death rate among people younger than age 55 has increased
1% per year between 2007 and 2016. The ACS estimates there are now more than
one million colorectal cancer survivors living in the US.
The ACS recommends that average-risk individuals start
regular colorectal cancer screenings at age 45. The five-year survival rate for
colon cancer patients is 90% when there is no sign that the cancer has spread
outside the colon.
Clinical laboratory professionals may find it unpleasant to
test FIT specimens. Opening the specimen containers and extracting the samples
can be messy and malodorous. However, FITs are essential, critical tests that
can save many lives.