Evaluation of a Cascaded Deep Learning–based Algorithm for Prostate Lesion Detection at Biparametric MRI

作者：Yue Lin, Enis C. Yılmaz, Mason J. Belue, Stephanie A. Harmon, Jesse Tetreault, Tim E. Phelps, Katie Merriman, Lindsey Hazen, Charisse Garcia, Dong Yang, Ziyue Xu, Nathan Lay, Antoun Toubaji, Maria J. Merino, Daguang Xu, Yan Mee Law, Sandeep Gurram, Bradford J. Wood, Peter L. Choyke, Peter A. Pinto, Barış Türkbey · 发表于：Radiology · 年份：2024 · DOI：10.1148/radiol.230750 · 被引用次数：31 · 研究领域：Prostate Cancer Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、AI in cancer detection

= .23). The algorithm identified 84% (103 of 122), 96% (152 of 159), 96% (47 of 49), 95% (38 of 40), and 98% (45 of 46) of participants with ISUP GG 1, 2, 3, 4, and 5 lesions, respectively. In the lesion-level analysis using radiologist ground truth, the detection sensitivity was 55% (569 of 1029; 95% CI: 52%, 58%), and the PPV was 57% (535 of 934; 95% CI: 54%, 61%). The mean number of false-positive lesions per participant was 0.61 (range, 0-3). The lesion segmentation DSC was 0.29. Conclusion The AI algorithm detected cancer-suspicious lesions on biparametric MRI scans with a performance comparable to that of an experienced radiologist. Moreover, the algorithm reliably predicted clinically significant lesions at histopathologic examination. ClinicalTrials.gov Identifier: NCT03354416 © RSNA, 2024