Deep learning may help track bone lesions in NaF-PET/CT

University of Wisconsin-Madison researchers found that a deep learning algorithm yielded similar sensitivity and specificity in differentiating benign and malignant bone lesions on F-18 NaF-PET/CT scans of patients with metastatic prostate cancer, but enabled significantly faster interpretation times compared with a traditional radiomics-based machine learning algorithm. The findings, presented at the SNMMI Annual Meeting, suggest that the deep learning algorithm may be "a great alternative to a radiomics model," said researcher Tyler Bradshaw.