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Thứ Năm, 23 tháng 1, 2020

2 US features predict cancer recurrence risk.

By Theresa Pablos, AuntMinnie staff writer
January 23, 2020 -- Machine learning might one day be able to tell which breast cancer patients will benefit from additional genetic testing. In a recent study, researchers used natural language processing to identify key features from ultrasound reports associated with cancer recurrence risk.
The study included hundreds of women with breast cancer who had previously undergone genetic tests, also known as transcriptomic tests, to determine their risk of cancer recurrence. After using a script to parse the women's BI-RADS ultrasound findings, the researchers found two key features that may identify when a patient could benefit from additional testing.
"Ultrasound findings, notably the 'retrotumoral' and 'margins' features, if abnormal, may help provide justification to obtain one of the transcriptomic tests," wrote the authors, led by Dr. Neema Jamshidi, PhD, a diagnostic radiologist from the University of California, Los Angeles (UCLA) David Geffen School of Medicine (Plos One, January 10, 2020). "Future multi-institutional prospective studies will be important in determining if these observations persist in larger cohorts."
The researchers acquired data from the electronic health records of 219 patients with breast cancer at the Harbor-UCLA Medical Center between April 2008 and January 2013. All patients had an ultrasound scan performed when they were first diagnosed with breast cancer. They also all had either an Oncotype DX or MammaPrint test to identify their risk of cancer recurrence.
The researchers coded a custom script to analyze the BI-RADS findings from the descriptive terminology from the women's initial ultrasound scans. Their program searched the terminology and attempted to find words or phrases associated with cancer reoccurrence risk.
In particular, three sonographic features -- "margins," "retrotumoral," and "internal echoes" -- were correlated with the genetic test results. The features "margins" and "retrotumoral" appeared in both the MammaPrint and Oncotype DX classification trees, while "internal echoes" only appeared in the MammaPrint classification tree.
The researchers hope future studies use more patients to determine whether atypical findings related to "margins" and "retrotumoral" features can truly help determine which patients would benefit from genetic testing. They also hope studies will evaluate other types of genetic tests.

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