You are free to share this article under the Attribution 4.0 International license. A new artificial intelligence system could help pathologists read biopsies more accurately, and lead to better detection and diagnosis of breast cancer, researchers say. Doctors examine images of breast tissue biopsies to diagnose breast cancer. But the differences between cancerous and benign images can be difficult for the human eye to classify. The new algorithm helps interpret them, and does so nearly as accurately or better than an experienced pathologist, depending on the task.
"It is critical to get a correct diagnosis from the beginning so that we can guide patients to the most effective treatments," said Dr. Joann Elmore, the study's senior author and a professor of medicine at the David Geffen School of Medicine at UCLA. Why would there be a need for such a study? Well, because, according to a 2015 study led by Elmore, pathologists often disagree on the outcome of breast biopsies. Furthermore, research has also found that diagnostic errors occurred in about one out of every six women who had ductal carcinoma in situ (DCIS) and incorrect diagnoses were given in about half of the biopsy cases of breast atypia. These are quite some significant errors.
WASHINGTON DC: Researchers discovered an artificial intelligence system that could help pathologists read biopsies more accurately and to better detect and diagnose breast cancer. The new system, described in a study published in the journal'JAMA Network Open,' helped interpret medical images used to diagnose breast cancer that can be difficult for the human eye to classify, and it does so nearly as accurate or better as experienced pathologists. "It is critical to get a correct diagnosis from the beginning so that we can guide patients to the most effective treatments," said Dr. Joann Elmore, the study's senior author and a professor of medicine at the David Geffen School of Medicine at UCLA. A 2015 study led by Elmore found that pathologists often disagree on the interpretation of breast biopsies, which are performed on millions of women each year. That earlier research revealed that diagnostic errors occurred in about one out of every six women who had ductal carcinoma in situ (a noninvasive type of breast cancer), and that incorrect diagnoses were given in about half of the biopsy cases of breast atypia (abnormal cells that are associated with a higher risk for breast cancer).
A computer could be better than a doctor at diagnosing certain types of cancerous and precancerous breast lesions, new research suggests. Researchers at the University of California, Los Angeles, trained an artificial intelligence system using 240 biopsy images, and tested it against 87 pathologists. The machine performed more or less as well as doctors at detecting and classifying all of the breast biopsies. However, it was better at making one crucial distinction: telling the difference between DCIS (ductal carcinoma in situ), a type of cancer, and atypical hyperplasia, a high-risk lesion that has very similar hallmarks but does is not cancerous and does not require the same level of treatment. 'Medical images of breast biopsies contain a great deal of complex data and interpreting them can be very subjective,' said Dr Joann Elmore, lead author of the study published in the JAMA Network Open journal.
Human doctors once again fell short of artificial intelligence in a test to accurately diagnose breast cancer, adding yet more evidence that AI-aided diagnostics may soon be commonplace. Researchers at the University of Washington and UCLA created a system that was able to distinguish between a pair of conditions that human doctors often struggle to identify correctly. The results are reminiscent of an effort by Google to detect metastatic breast cancer using AI, which the company says is 99 percent accurate. The company claims that another project, aimed at spotting early-stage lung cancer, can also outperform doctors. Rival tech giant Microsoft has a number of projects that use algorithms to fight cancer and is working with Seattle-based Adaptive Biotechnologies on a system that uses AI to diagnose multiple diseases from a single blood test.