An artificial intelligence tool that could help detect breast lesions likely to become cancer has been developed by researchers at MIT.
When breast cancer is detected early, it can often be treated successfully. The best available test currently is the mammogram, but they are still imperfect and often result in false positive results that can lead to unnecessary biopsies and surgeries. The new MIT technology has the potential to lower the number of unnecessary surgeries done.
Diagnosed by biopsy, high-risk breast lesions carry an increased risk of developing into cancer.
Due to that risk, surgical removal is often the preferred treatment option. But not all high-risk lesions pose an immediate threat to the patient’s life. Some can be safely monitored with follow-up imaging, sparing patients the costs and complications associated with surgery.
High-risk Lesion Types
Study author and radiologist Manisha Bahl, M.D., M.P.H., from Massachusetts General Hospital (MGH) and Harvard Medical School, said:
“There are different types of high-risk lesions. Most institutions recommend surgical excision for high-risk lesions such as atypical ductal hyperplasia, for which the risk of upgrade to cancer is about 20 percent. For other types of high-risk lesions, the risk of upgrade varies quite a bit in the literature, and patient management, including the decision about whether to remove or survey the lesion, varies across practices.“
Dr. Bahl and colleagues at MGH investigated the use of a machine learning tool to pinpoint high-risk lesions that are at low risk for upgrade to cancer. The study resulted from a close collaboration between researchers at the Massachusetts Institute of Technology’s (MIT) Computer Science and Artificial Intelligence Laboratory in Cambridge, Mass., and breast imaging experts at MGH.
[caption id="attachment_92625” align="aligncenter” width="680”] Flow diagram showing patient selection.
Credit: Radiological Society of North America[/caption]
When tested on 335 high-risk lesions, the model correctly diagnosed 97 percent of the breast cancers as malignant and reduced the number of benign surgeries by more than 30 percent compared to existing approaches.
“Because diagnostic tools are so inexact, there is an understandable tendency for doctors to over-screen for breast cancer. When there’s this much uncertainty in data, machine learning is exactly the tool that we need to improve detection and prevent over-treatment,”
says Regina Barzilay, MIT’s Delta Electronics Professor of Electrical Engineering and Computer Science and a breast cancer survivor herself.
Machine Learning Concept
Machine learning is a form of artificial intelligence in which a model automatically
[caption id="attachment_92628” align="aligncenter” width="680”] Accuracy achieved with machine learning model for independent test set as a function of model output score.
Indicates for both patients with malignancy (red line) and for patients without malignancy (blue line), in independent test set.
Vertical dotted line indicates 5 percent threshold.
Credit: Radiologial Society of North America[/caption]
learns and improves based on previous experiences.
The model developed by researchers analyzed traditional risk factors such as patient age and lesion histology, along with several unique features, including words that appear in the text from the biopsy pathology report.
The researchers trained the model on a group of patients with biopsy-proven high-risk lesions who had surgery or at least two-year imaging follow-up. Of the 1,006 high-risk lesions identified, 115, or 11 percent, were upgraded to cancer.
The machine-learning model identified the terms “severely” and “severely atypical” in the text of the pathology reports as associated with a greater risk of upgrade to cancer.
“Our study provides ‘proof of concept’ that machine learning can not only decrease unnecessary surgery by nearly one-third in this specific patient population, but also can support more targeted, personalized approaches to patient care,“
said the paper’s senior author, Constance Lehman, M.D., Ph.D., professor at Harvard Medical School and Director of Breast Imaging at MGH.
Manisha Bahl, Adam Yedidia, Lili Yu, Regina Barzilay and Constance Lehman A Machine Learning Model to Predict Pathologic Upgrade and Reduce Unnecessary Surgical Excision of High-Risk Breast Lesions Radiology, 2017. https://doi.org/10.1148/radiol.2017170549
Top Image: Jason Dorfman/CSAIL