Automated detection of aggressive and indolent prostate cancer on magnetic resonance imaging
Medical Physics2021Vol. 48(6), pp. 2960–2972
Citations Over TimeTop 10% of 2021 papers
Arun Seetharaman, Indrani Bhattacharya, Leo C. Chen, Christian A. Kunder, Wei Shao, Simon John Christoph Soerensen, Jeffrey B. Wang, Nikola C. Teslovich, Richard E. Fan, Pejman Ghanouni, James D. Brooks, Katherine To’o, Geoffrey A. Sonn, Mirabela Rusu
Abstract
Our SPCNet model accurately detected aggressive prostate cancer. Its performance approached that of radiologists, and it helped identify lesions otherwise missed by radiologists. Our model has the potential to assist physicians in specifically targeting the aggressive component of prostate cancers during biopsy or focal treatment.
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