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.
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.
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