Scientists, including one of Indian origin, have developed an artificial intellince (AI) system that can identify which breast lesions are likely to become cancerous - potentially reducing unnecessary surgeries.
High-risk breast lesions are biopsy-diagnosed lesions that carry an increased risk of developing into cancer.
Due to that risk, surgical removal is often the preferred treatment option. However, many high-risk lesions do not pose an immediate threat to the patient's life and can be safely monitored with follow-up imaging, sparing patients the costs and complications associated with surgery.
"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 per cent," said Manisha Bahl, from Massachusetts General Hospital (MGH) and Harvard Medical School in the US.
"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," Bahl said.
Researchers studied the use of a machine learning tool to identify high-risk lesions that are at low risk for upgrade to cancer.
"Because diagnostic tools are inexact, there is an understandable tendency for doctors to over-screen for breast cancer," said Regina Barzilay, professor at MIT.
"When there's this much uncertainty in data, machine learning is exactly the tool that we need to improve detection and prevent overtreatment," said Barzilay.
Machine learning is a type of artificial intelligence in which a model automatically learns and improves based on previous experiences.
The model developed by researchers analysed 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 per cent, were upgraded to cancer.
After training the machine learning model on two-thirds of the high-risk lesions, the researchers tested it on the remaining 335 lesions.
The model correctly predicted 37 of the 38 lesions, or 97 per cent, that were upgraded to cancer. The researchers also found that use of the model would have helped avoid almost one-third of benign surgeries.
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