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Artificial Intelligence Is Helping Doctors Find Breast Cancer Risk 30 Times Faster

Published 6 Sep 2016

Artificial Intelligence Is Helping Doctors Find Breast Cancer Risk 30 Times Faster
The mammogram is one of medical science’s best tools for detecting breast cancer, but when the typically painful test reveals a potential problem, women frequently undergo breast biopsies for a closer look–a practice that’s all too often unnecessary, according to a group of artificial intelligence (AI) researchers, and which doctors may be able to significantly reduce thanks to a little insight from computers.
 
Announced today, researchers from Houston Methodist have developed AI software that can interpret mammogram results a full 30 times quicker than doctors and with 99 percent accuracy, according to the team’s recent study. Published in the journal Cancer, the study found that the software was able to intuitively translate patient charts into diagnostic information for human review at top speeds, which offers doctors reliable and seriously time-saving support when it comes to assessing patient cancer risk and the need for further tests.
 
To determine the software’s effectiveness for assessing breast cancer risk, the team provided its AI with mammogram and pathology reports of 500 breast cancer patients, as well as information on diagnostic features and correlated mammogram findings for breast cancer subtypes. In just a few hours, the researchers’ AI had successfully assessed patient charts for cancer risk for the entire group; on the other hand, two clinicians needed between 50 and 70 hours to review just 50 patient charts manually, suggesting that such software stands to help save hundreds or thousands of physician hours.
 
To explain the need for tech that can rapidly assess breast cancer risk, the researchers point to recent statistics from the Centers for Disease Control and Prevention (CDC) and American Cancer society, which suggest that up to half of the 12.1 million mammograms performed annually in the United States result in false positives. According to the researchers, patients with ‘suspicious’ mammograms bearing a 3 to 95 percent cancer risk are frequently referred for biopsies, accounting for over 1.6 million U.S. breast biopsies per year–20 percent of which, the team says, are the unnecessary result of false positives on mammograms. 
 
In addition to minimizing patient suffering and maximizing physicians’ time, the software also has the potential to help rein in at least one aspect of the rising cost of cancer care, which the National Cancer Institute projects will top $158 billion per year by 2020. According to Grand View Research, the breast biopsy device market is on track to hit $911.2 Million by 2024, despite the fact that the “stressful” procedure returns clear results between 60 and 80 percent of the time. Researchers from Johns Hopkins, meanwhile, have also suggested that unnecessary hormone-assessing breast biopsies for patients diagnosed with ductal carcinoma in situ (DCIS), the most common non-invasive breast cancer, could be wasting as much as $35 million per year.
 
The researchers hope that their software will be able to help physicians better assess breast cancer risk, and refine their procedures for recommending biopsies, as well as reduce unnecessary ones. Stephen T. Wong, chair of the Department of Systems Medicine and Bioengineering at Houston Methodist Research Institute and the study’s co-leader, commented in a press release,This software intelligently reviews millions of records in a short amount of time, enabling us to determine breast cancer risk more efficiently using a patient’s mammogram, [and] has the potential to decrease unnecessary biopsies … Accurate review of this many charts would be practically impossible without AI.

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