Artificial Intelligence May Expedite Breast Cancer Risk Prediction

Potential new technology here that may limit number of breast biopsies previously deemed “unnecessary” using artificial intelligence to aid screening with mammography with tumor protein expression. Perhaps as many as 20% of breast biopsies would be eliminated based on current figures.

Key Points

• The software scanned patient charts, collected diagnostic features, and correlated mammogram findings with breast cancer subtype.
• Clinicians used results, like the expression of tumor proteins, to accurately predict each patient’s probability of breast cancer diagnosis.
• Manual review of 50 charts took two clinicians 50 to 70 hours. AI reviewed 500 charts in a few hours, saving over 500 physician hours.

Researchers at Houston Methodist have developed artificial intelligence (AI) software that reliably interprets mammograms, assisting doctors with a quick and accurate prediction of breast cancer risk. According to a new study published by Patel et al in Cancer, the computer software intuitively translates patient charts into diagnostic information at 30 times human speed and with 99% accuracy.

“This software intelligently reviews millions of records in a short amount of time, enabling us to determine breast cancer risk more efficiently using a patient’s mammogram. This has the potential to decrease unnecessary biopsies,” said Stephen T. Wong, PhD, PE, Chair of the Department of Systems Medicine and Bioengineering at Houston Methodist Research Institute.

Currently, when mammograms fall into the suspicious category—a broad range of 3% to 95% cancer risk—patients are recommended for biopsies. Over 1.6 million breast biopsies are performed annually nationwide, and about 20% are unnecessarily performed due to false-positive mammogram results of cancer-free breasts, estimates the ACS.

Study Findings

The team led by Dr. Wong and Jenny C. Chang, MD, Director of the Houston Methodist Cancer Center, used the AI software to evaluate mammograms and pathology reports of 500 breast cancer patients. The software scanned patient charts, collected diagnostic features, and correlated mammogram findings with breast cancer subtype. Clinicians used results, like the expression of tumor proteins, to accurately predict each patient’s probability of breast cancer diagnosis.

The Houston Methodist team hopes this artificial intelligence software will help physicians better define the percent risk requiring a biopsy, equipping doctors with a tool to decrease unnecessary breast biopsies.

Manual review of 50 charts took two clinicians 50 to 70 hours. AI reviewed 500 charts in a few hours, saving over 500 physician hours.

“Accurate review of this many charts would be practically impossible without AI,” concluded Dr. Wong.

Source: The ASCO Post – Posted 8/30/2016

The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.