Despite major advances in genetics and modern imaging, the diagnosis catches most breast cancer patients by surprise. For some, it comes too late. Later diagnosis means aggressive treatments, uncertain outcomes, and more medical expenses. As a result, identifying patients has been a central pillar of breast cancer research and effective early detection.
The Solution –
MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) and Massachusetts General Hospital (MGH) has developed a new deep learning-based AI prediction model; that can anticipate the development of breast cancer up to five years in advance. Researchers working on the product also recognized that other similar projects have often had inherent bias; because they were based overwhelmingly on white patient populations, and specifically designed their own model; so that it is informed by “more equitable” data that ensures it’s “equally accurate for white and black women.”
That’s key, MIT notes in a blog post; because black women are more than 42 percent more likely than white women to die from breast cancer; and one contributing factor could be that they aren’t as well-served by current early detection techniques. MIT says that its work in developing this technique was aimed specifically at making the assessment of health risks of this nature more accurate for minorities; who are often not well represented in development of deep learning models. The issue of algorithmic bias is a focus of a lot of industry research; and even newer products forthcoming from technology companies working on deploying AI in the field.
How it works?
Since the first breast-cancer risk model from 1989; development has largely been driven by human knowledge and intuition of what major risk factors might be; such as age, family history of breast and ovarian cancer, hormonal and reproductive factors, and breast density.
However, most of these markers are only weakly correlated with breast cancer. As a result, such models still aren’t very accurate at the individual level; and many organizations continue to feel risk-based screening programs are not possible, given those limitations.
Rather than manually identifying the patterns in a mammogram that drive future cancer; the MIT/MGH team trained a deep-learning model to deduce the patterns directly from the data. Using information from more than 90,000 mammograms, the model detected patterns too subtle for the human eye to detect.
“Since the 1960s radiologists have noticed that women have unique and widely variable patterns of breast tissue; visible on the mammogram,” says Lehman. “These patterns can represent the influence of genetics, hormones, pregnancy, lactation, diet, weight loss, and weight gain. We can now leverage this detailed information to be more precise in our risk assessment at the individual level.”
Equality in Outcome –
This MIT tool, which is trained on mammograms and patient outcomes; (eventual development of cancer being the key one) from over 60,000 patients (with over 90,000 mammograms total) from the Massachusetts General Hospital; starts from the data and uses deep learning; to identify patters that would not be apparent or even observable by human clinicians. Because it’s not based on existing assumptions or received knowledge about risk factors; which are at best a suggestive framework, the results have so far shown to be far more accurate; especially at predictive, pre-diagnosis discovery.
Overall, the project is intended to help healthcare professionals put together the right screening program for individuals in their care; and eliminate the heartbreaking and all-too common outcome of late diagnosis. MIT hopes the technique can also be used to improve detection of other diseases; that have similar problems with existing risk models with far too many gaps and lower degrees of accuracy.