An artificial intelligence tool has successfully identified people at the highest risk for
pancreatic cancer up to three years before diagnosis using solely the patients’ medical
records, according to new research led by investigators at Harvard Medical School.
The findings, published in the journal Nature Medicine, suggest that AI-based population
screening could be valuable in finding those at elevated risk for the disease and could
expedite the diagnosis of a condition found all too often at advanced stages when treatment
is less effective and outcomes are dismal.
Pancreatic cancer is one of the deadliest cancers in the world, and its toll projected
to increase.
Currently, there are no population-based tools to screen broadly for pancreatic cancer.
Those with a family history and certain genetic mutations that predispose them to pancreatic
cancer are screened in a targeted fashion.
But such targeted screenings can miss other cases that fall outside of those categories.
One of the most important decisions clinicians face day to day is who is at high risk for
a disease, and who would benefit from further testing, which can also mean more invasive
and more expensive procedures that carry their own risks.
An AI tool that can zero in on those at highest risk for pancreatic cancer who stand to benefit
most from further tests could go a long way toward improving clinical decision-making.
Applied at scale, such an approach could expedite detection of pancreatic cancer, lead to earlier
treatment, and improve outcomes and prolong patients’ life spans.
Many types of cancer, especially those hard to identify and treat early, exert a disproportionate
toll on patients, families and the healthcare system as a whole.
AI-based screening is an opportunity to alter the trajectory of pancreatic cancer, an aggressive
disease that is notoriously hard to diagnose early and treat promptly when the chances
for success are highest.
In the new study, the AI algorithm was trained on two separate data sets totaling 9 million
patient records from Denmark and the United States.
The researchers “asked” the AI model to look for telltale signs based on the data
contained in the records.
Based on combinations of disease codes and the timing of their occurrence, the model
was able to predict which patients are likely to develop pancreatic cancer in the future.
Notably, many of the symptoms and disease codes were not directly related to or stemming
from the pancreas.
The researchers tested different versions of the AI models for their ability to detect
people at elevated risk for disease development within different time scales — 6 months,
one year, two years, and three years.
Overall, each version of the AI algorithm was substantially more accurate at predicting
who would develop pancreatic cancer than current population-wide estimates of disease incidence
— defined as how often a condition develops in a population over a specific period of
time.
The researchers said they believe the model is at least as accurate in predicting disease
occurrence as are current genetic sequencing tests that are usually available only for
a small subset of patients in data sets.
Screening for certain common cancers such as those of the breast, cervix, and prostate
gland relies on relatively simple and highly effective techniques — a mammogram, a Pap
smear, and a blood test, respectively.
These screening methods have transformed outcomes for these diseases by ensuring early detection
and intervention during the most treatable stages.
By comparison, pancreatic cancer is harder and more expensive to screen and test for.
Physicians look mainly at family history and the presence of genetic mutations, which,
while important indicators of future risk, often miss many patients.
One particular advantage of the AI tool is that it could be used on any and all patients
for whom health records and medical history are available, not just in those with known
family history or genetic predisposition for the disease.
This is especially important, the researchers add, because many patients at high risk may
not even be aware of their genetic predisposition or family history.
In the absence of symptoms and without a clear indication that someone is at high risk for
pancreatic cancer, clinicians may be understandably cautious to recommend more sophisticated and
more expensive testing, such as CT scans, MRI or endoscopic ultrasound.
When these tests are used and suspicious lesions discovered, the patient must undergo a procedure
to obtain a biopsy.
Positioned deep inside the abdomen, the organ is hard to access and easy to provoke and
inflame.
Its irritability has earned it the moniker “the angry organ.”
An AI tool that identifies those at the highest risk for pancreatic cancer would ensure that
clinicians test the right population, while sparing others unnecessary testing and additional
procedures.
About 44 percent of people diagnosed in the early stages of pancreatic cancer survive
five years after diagnosis, but only 12 percent of cases are diagnosed that early.
The survival rate drops to 2 to 9 percent in those whose tumors have grown beyond their
site of origin, researchers estimate.
For the current study, the researchers designed several versions of the AI model and trained
them on the health records of 6.2 million patients from Denmark’s national health
system spanning 41 years.
Of those patients, 23,985 developed pancreatic cancer over time.
During the training, the algorithm discerned patterns indicative of future pancreatic cancer
risk based on disease trajectories, that is, whether the patient had certain conditions
that occurred in a certain sequence over time.
For example, diagnoses such as gallstones, anemia, type 2 diabetes, and other GI-related
problems portended greater risk for pancreatic cancer within 3 years of evaluation.
Less surprisingly, inflammation of the pancreas was strongly predictive of future pancreatic
cancer within an even shorter time span of two years.
The researchers caution that none of these diagnoses by themselves should be deemed indicative
or causative of future pancreatic cancer.
However, the pattern and sequence in which they occur over time offer clues for an AI-based
surveillance model and could prompt physicians to monitor those at elevated risk more closely
or test accordingly.
Next, the researchers tested the best performing algorithm on an entirely new set of patient
records it had not previously encountered — a U.S. Veterans Health Administration
data set of nearly 3 million records spanning 21 years and containing 3,864 individuals
diagnosed with pancreatic cancer.
The tool’s predictive accuracy was somewhat lower on the US data set.
This was most likely because the US dataset was collected over a shorter time and contained
a somewhat different patient population profiles — the entire population of Denmark in the
Danish data set versus current and former military personnel in the Veterans’ Affairs
data set.
When the algorithm was retrained from scratch on the US dataset, its predictive accuracy
improved.
This underscores two important points: First, ensuring that AI models are trained on high
quality and rich data.
Second, the need for access to large representative datasets of clinical records aggregated nationally
and internationally.
In the absence of such globally valid models, AI models should be trained on local health
data to ensure their training reflects the idiosyncrasies of local populations.