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Despite major advances in genetics and modern imaging, the diagnosis surprises most patients with bad cancer.
For some, it's too late. Further diagnosis involves aggressive treatments, uncertain results and more medical costs. As a result, patient identification has been a central pillar of bad cancer research and effective early detection.
With this in mind, a team from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and Mbadachusetts General Hospital (MGH) has created a new in-depth learning model to predict from a mammogram, if a patient is as likely to develop bad cancer. like five years in the future. Trained in mammograms and known outcomes in more than 60,000 patients with MGH, the model has learned subtle patterns of bad tissue that are precursors to malignant tumors.
Professor Regina Barzilay of MIT, herself a bad cancer survivor, said it was hoped that such systems would allow physicians to personalize screening and prevention programs at the individual level, making late diagnosis a relic of the past .
Although it has been shown that mammography reduces bad cancer mortality, the frequency of screening and the timing of the beginning of the debate are the subject of debate. While the American Cancer Society recommends annual screening beginning at age 45, the US Preventative Working Group recommends screening every two years starting at age 50.
"Instead of taking a unique approach, we can customize cancer risk screening in women," said Barzilay, lead author of a new article on the project released recently at Radiology. "For example, a doctor might recommend to a group of women to undergo a mammogram every two years, while another high-risk group might undergo additional MRI screening." Barzilay is Professor Delta Electronics at CSAIL and the Department of Electrical and Computer Engineering at MIT and a member of the Koch Institute for Integrative Cancer Research at MIT.
The team model was significantly better at predicting risk than existing approaches: it accurately placed 31% of all cancer patients in its highest risk category, compared to only 18% for all models. traditional.
Harvard professor Constance Lehman said there was little support in the medical community for risk-based rather than age-based screening strategies.
"That's because before, we did not have an accurate risk badessment tool that was tailored to every woman," says Lehman, professor of radiology at Harvard Medical School and head of the imaging division. at the MGH Hospital. "Our work is the first to show that it is possible."
Barzilay and Lehman co-authored the article with lead author Adam Yala, a PhD student at CSAIL. Tal Schuster, doctoral student, and Tally Portnoi, former master's student.
How it works
Since the first bad cancer risk model of 1989, development relies heavily on the knowledge and intuition of what could be the main risk factors, such as age, family history bad and ovarian cancer, hormonal and reproductive factors and bad density.
However, most of these markers are only weakly correlated with bad cancer. As a result, such models are still not very specific at the individual level and many organizations continue to believe that risk-based screening programs are not possible because of these limitations.
Rather than manually identify patterns of a mammogram at the origin of future cancer, the MIT / MGH team formed an in-depth learning model to infer patterns directly from the data. Using information from more than 90,000 mammograms, the model detected profiles too subtle to be detected by the human eye.
"Since the 1960s, radiologists have noticed that bad tissue seen on mammograms was very varied among women," said Lehman. "These patterns can represent the influence of genetics, hormones, pregnancy, badfeeding, diet, weight loss, and weight gain." We can now exploit this information to be more precise in our individual risk badessment. "
Make cancer detection fairer
The project also aims to make risk badessment more accurate for racial minorities in particular. Many early models were developed on white populations and were much less accurate for other breeds. The MIT / MGH model, on the other hand, is just as accurate for white women as it is for black women. This is especially important since black women are 42% more likely to die of bad cancer because of a wide range of factors that may include differences in screening and access to health care.
"It is particularly striking that the model works equally well for whites and blacks, which has not been the case with the previous tools," says Allison Kurian, badociate professor of medicine and research on health at Stanford University. "If validated and made available for widespread use, it could actually improve our current risk badessment strategies."
Barzilay says their system could also someday allow doctors to use mammograms to see if patients are at greater risk of contracting other health problems, such as cardiovascular disease or other medical conditions. other cancers. The researchers want to apply the models to other diseases and conditions, particularly those with less effective risk models, such as pancreatic cancer.
"Our goal is to integrate these advances into the quality of care," Yala said. "By predicting who will develop cancer in the future, we can hope to save lives and stop cancer before the symptoms begin to manifest themselves."
Reprinted with the permission of MIT News. Read the original article.
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