A deep learning model performs at the level of an abdominal radiologist in the detection of clinically significant prostate cancer on MRI, according to a study published today in Radiology , a journal of the Radiological Society of North America (RSNA). The researchers hope the model can be used as an adjunct to radiologists to improve prostate cancer detection. Prostate cancer is the second most common cancer in men worldwide.
Radiologists typically use a technique that combines different MRI sequences (called multiparametric MRI) to diagnose clinically significant prostate cancer. Results are expressed through the Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS), a standardized interpretation and reporting approach.
However, lesion classification using PI-RADS has limitations. The interpretation of prostate MRI is difficult. More experienced radiologists tend to have higher diagnostic performance.
" Naoki Takahashi, M.D., study senior author, Department of Radiology, Mayo Clinic in Rochester, Minnesota Applying artificial intelligence (AI) algorithms to prostate MRI has shown promise for improving cancer detection and reducing observer variability, which is the inconsistency in how people measure or interpret things that can lead to errors.
However, a major drawback of existing AI approaches is that the lesion needs to be annotated (adding a note or explanation) by a radiologist or pathologist at the time of initial model development and again during model re.