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Using Artificial Intelligence to Improve Early Breast Cancer Detection

posted by Fnord666 on Friday October 20 2017, @07:54AM   
from the I'm-sensing-a-theme dept.

MrPlow writes:

Submitted via IRC for TheMightyBuzzard

Model developed at MIT's Computer Science and Artificial Intelligence Laboratory could reduce false positives and unnecessary surgeries.

Every year 40,000 women die from breast cancer in the U.S. alone. When cancers are found early, they can often be cured. Mammograms are the best test available, but they're still imperfect and often result in false positive results that can lead to unnecessary biopsies and surgeries.

One common cause of false positives are so-called "high-risk" lesions that appear suspicious on mammograms and have abnormal cells when tested by needle biopsy. In this case, the patient typically undergoes surgery to have the lesion removed; however, the lesions turn out to be benign at surgery 90 percent of the time. This means that every year thousands of women go through painful, expensive, scar-inducing surgeries that weren't even necessary.

How, then, can unnecessary surgeries be eliminated while still maintaining the important role of mammography in cancer detection? Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts General Hospital, and Harvard Medical School believe that the answer is to turn to artificial intelligence (AI).

As a first project to apply AI to improving detection and diagnosis, the teams collaborated to develop an AI system that uses machine learning to predict if a high-risk lesion identified on needle biopsy after a mammogram will upgrade to cancer at surgery.

Source: http://news.mit.edu/2017/artificial-intelligence-early-breast-cancer-detection-1017

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Original Submission

• Reduce overdiagnosis (Score: 3, Interesting) by stormwyrm on Friday October 20 2017, @03:36PM

  by stormwyrm (717) on Friday October 20 2017, @03:36PM (#585263) Journal

  http://scienceblogs.com/insolence/2016/10/13/a-study-of-overdiagnosis-due-to-mammography-reported-during-breast-cancer-awareness-month/ [scienceblogs.com]

  Basically, overdiagnosis is a phenomenon that can confound any screening program in which large populations of asymptomatic patients are subjected to a diagnostic test to screen for a disease. The basic concept is that there can be preclinical disease that either does not progress or progresses so slowly that it would never threaten the life of the patient within that patient’s lifetime. yet the test picks it up. Because we don’t have tests that can predict which lesions picked up by such a screening test will or will not progress to endanger the patient, physicians are left with little choice but to treat each screen-detected lesion as though it will progress, resulting in overtreatment. This situation is very much the case for mammography and breast cancer, for example, for which there is evidence that as many as one in five to one in three screen-detected (as opposed to cancers detected by symptoms or a mass) breast cancers are overdiagnosed. As a result, physicians are much less confident in traditional recommendations for screening mammography than we once were. Add to that the phenomenon of lead time bias, in which earlier detection doesn’t actually impact survival but only gives the appearance of prolonged survival, as I described recently in more detail. Similarly, due to the phenomenon of length bias (also described by yours truly recently), mammography also tends to preferentially detect slower growing tumors.

  Hopefully the AI is smart enough to ameliorate this.

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  Numquam ponenda est pluralitas sine necessitate.

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