In addition to providing an estimated anomaly score, the detection model produces a spatially resolved heatmap for an MR image. This heatmap highlights in color the regions in the image that the model believes to be abnormal. The abnormal regions identified by the model matched areas of biopsy-proven malignancy annotated by a radiologist, largely surpassing the performance of benchmark models.
The model was tested on internal and external datasets. The internal dataset consisted of MRI exams performed on 171 women (mean age 48.8) for screening (71.9%; 31 cancers confirmed on subsequent biopsy) or pre-operative evaluation for a known cancer (28.1%; 50 cancers confirmed by biopsy). The external, publicly available, multicenter dataset included pre-treatment breast MRI exams of 221 women with invasive breast cancer.
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The anomaly detection model accurately depicted tumor location and outperformed benchmark models in grouped cross-validation, internal and external test datasets, and in both balanced (high prevalence of cancer) and imbalanced (low cancer prevalence) detection tasks.
If integrated into radiology workflows, Dr. Oviedo said the anomaly detection model could potentially exclude normal scans for triage purposes and improve reading efficiency.
“Our model provides an understandable, pixel-level explanation of what’s abnormal in a breast,” he said. “These anomaly heatmaps could highlight areas of potential concern, allowing radiologists to focus on those exams that are more likely to be cancer.”
Before clinical application, he said the model needs to be evaluated in larger datasets and prospective studies to assess its potential for enhancing radiologists’ workflow.
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