The three risk assessment tools now in use fall far
short. Using the latest deep learning techniques, investigators are developing
more personalized ways to locate women at high risk.
John Halamka, M.D., president, Mayo Clinic Platform, and Paul
Cerrato, senior research analyst and communications specialist, Mayo Clinic
Platform, wrote this article.
The promise of personalized medicine will eventually allow
clinicians to offer individual patients more precise advice on prevention,
early detection and treatment. Of course, the operative word is eventually. A
closer examination of the screening tools available to detect breast cancer
demonstrates that we still have a way to go before we can fulfill that promise.
But with the help of better technology, we are getting closer to that
realization.
Disease screening is about risk assessment. Researchers collect
data on thousands of patients who develop breast cancer, for instance, and
discover that the age range, family history and menstruation history of those
who develop the disease differs significantly from those who remain free of it.
That in turn allows policy makers to create a screening protocol that suggests
women of a certain age who have experienced early menarche or late menopause
are more likely to develop the malignancy. That risk assessment is consistent
with the fact that more reproductive years means more exposure to the hormones
that contribute to breast cancer. Similarly, there’s evidence to show that
women with first degree relatives with the cancer and those with a history of
ovarian cancer or HRT use are at greater risk.
Statistics like this are the basis for several breast cancer
risk scoring systems, including the Gail score, the IBIS score, and BCSC
tool. The National
Cancer Institute, which
uses the Gail model, explains: “The Breast Cancer Risk Assessment Tool allows
health professionals to estimate a woman’s risk of developing invasive breast
cancer over the next 5 years and up to age 90 (lifetime risk). The tool uses a
woman’s personal medical and reproductive history and the history of breast
cancer among her first-degree relatives (mother, sisters, daughters) to
estimate absolute breast cancer risk—her chance or probability of developing
invasive breast cancer in a defined age interval.” While the screening tool
saves lives, it can also be misleading. If, for example, it finds that a woman
has a 1% likelihood of developing breast cancer, what that really means is a
large population of women with those specific risk factors has a one in 100
risk of developing the disease. There is no way of knowing what the threat is
for any one patient in that group. Similar problems exist for the International
Breast Cancer Intervention Study (IBIS) score, based on the
Tyrer-Cuzick Model, and the Breast Cancer Surveillance Consortium (BCSC) Risk Calculator. These
3 assessment tools can give patients a false sense of security if they don’t
dive into the details. BCSC, for instance, cannot be applied to women younger
that 35 or older than 74, nor does it accurately measure risk for anyone who
has previously had ductal carcinoma in situ (DCIS), or had breast augmentation.
Similarly, the NCI tool doesn’t accurately estimate risk in women with BRCA1 or
BRCA1 mutation, as well as certain other subgroups.
During a conversation with Tufia Haddad, M.D,, a Mayo Clinic
medical oncologist with specialty interest in precision medicine in breast
cancer and artificial intelligence, she discussed the research she and her
colleagues are doing to improve the risk assessment process and identify more
high-risk women. Dr. Haddad pointed out that there are numerous obstacles that
prevent women from obtaining the best possible risk assessment. Too many women
do not have a primary care practitioner who might use a risk tool. And those
that do have a PCP are more likely to have an evaluation based on the Breast
Cancer Risk Assessment tool (the Gail model). “We prefer the Tyrer-Cuzick model
in part because it incorporates more personal information for each individual
patient including a detailed family history, a woman’s breast density from her
mammogram, as well as her history of atypia or other high risk benign breast
disease,” says Dr. Haddad. Unfortunately, the Tyrer-Cuzick method requires many
more data elements to assess breast cancer risk, which discourages busy
clinicians from using it.
Another obstacle to using any of these risk assessment tools is
the fact that they don’t readily fit into the average physician’s clinical
workflow. Ideally these tools should seamlessly integrate into the EHR system.
Even better would be the incorporation of AI-enhanced algorithms that automate
the abstraction of the required data elements from the patient’s record into
the assessment tool. For example, the algorithm would flag a family history of
breast cancer, increased breast density as determined during a mammogram, as
well as hormone replacement therapy and insert those risk factors into the Tyrer-Cuzick
tool.
Even with this AI-enhanced approach, all of the available risk
models fall short because they take a population-based approach, as we
mentioned above. Dr. Haddad and her colleagues are looking to make the
assessment process more individualized, as are others work in this specialty.
That model could incorporate each patient’s previous mammography results, their
genetics and benign breast biopsy findings, and much more. Adam Yala, and his colleagues at
MIT recently developed a mammography-based deep learning model designed to take
this more sophisticated approach. Called Mirai, it was trained on a large data
set from Massachusetts General Hospital and from facilities in Sweden and
Taiwan. The new model generated
significantly better results for breast cancer risk prediction than the TC
model.
Breast cancer risk assessment continues to evolve. And with
better utilization of existing assessment tools and the assistance of deep
learning, we can look forward to better patient outcomes.
