AI helps radiologists assess axillary lymph nodes
Artificial intelligence (AI) can be trained to predict a patient’s likelihood of axillary lymph node metastasis using a breast MRI dataset, according to a study published in the Journal of Digital Imaging.
The authors used data from 133 metastatic axillary lymph nodes and 142 negative control lymph nodes for their research, using cropped images from each breast MRI to design a convolutional neural network (CNN). Overall, the CNN achieved a five-fold cross-validation accuracy of 84.3 percent. Each false positive prediction and false negative prediction was examined, but no “discernibly consistent features that consistently lead to false negative or false positive classifications” were observed.
“Applying deep machine learning using CNN-based algorithm in our study, we were able to generate reasonable diagnostic performance in predicting axillary lymph node metastasis even with a small dataset,” Richard Ha, MD, department of radiology at Columbia University Irving Medical Center in New York City, and colleagues. “Larger dataset will likely improve our prediction model.”
The authors also noted that more research is needed to “further validate” their findings.
Michael has more than 19 years of experience as a professional writer and editor. He has written at length about cardiology, radiology, artificial intelligence and other key healthcare topics.