Purpose: The Prostate Imaging Reporting and Data System (PI-RADS) was introduced to standardize prostate cancer diagnosis by MRI. However, the inter-reader agreement by PI-RADS scoring is not always high. The purpose ...Purpose: The Prostate Imaging Reporting and Data System (PI-RADS) was introduced to standardize prostate cancer diagnosis by MRI. However, the inter-reader agreement by PI-RADS scoring is not always high. The purpose of this study was to validate a deep-learning-based diagnostic algorithm of PI-RADS. Methods: We applied a Siemens Healthineers Prostate Artificial Intelligence (AI) prototype (work in progress) for fully automated prostate lesion detection, classification and reporting. More than 2000 bi-parametric MRI studies along with the PI-RADS reports were included as training, validation, and test data. This prospective validation study includes 101 consecutive patients suspected of prostate cancer, and 100 patients were included in the analysis. All subjects underwent a noncontrast-enhanced bi-parametric MRI including T2-weighted and diffusion-weighted imaging. Two board-certified radiologists independently scored the PI-RADS, and if there were disagreements;another radiologist confirmed the diagnosis. We compared the AI results with the interpretation results by the radiologists. Results: The sensitivity of our AI model for PI-RADS ≥ 4 was 0.76, and the specificity was 0.76. For the cases with PI-RADS ≥ 3, the sensitivity was 0.69, and the specificity was 0.76. In the lesion-based analysis, AI detection rates of PI-RADS 3, 4, 5 lesions in the peripheral zone were 43%, 63%, and 100%, respectively. In the transition zone, AI detection rates of PI-RADS 3, 4, 5 were 30%, 54%, and 100%, respectively. Conclusion: Our deep-learning-based algorithm has been validated and shown to help score PI-RADS.展开更多
文摘Purpose: The Prostate Imaging Reporting and Data System (PI-RADS) was introduced to standardize prostate cancer diagnosis by MRI. However, the inter-reader agreement by PI-RADS scoring is not always high. The purpose of this study was to validate a deep-learning-based diagnostic algorithm of PI-RADS. Methods: We applied a Siemens Healthineers Prostate Artificial Intelligence (AI) prototype (work in progress) for fully automated prostate lesion detection, classification and reporting. More than 2000 bi-parametric MRI studies along with the PI-RADS reports were included as training, validation, and test data. This prospective validation study includes 101 consecutive patients suspected of prostate cancer, and 100 patients were included in the analysis. All subjects underwent a noncontrast-enhanced bi-parametric MRI including T2-weighted and diffusion-weighted imaging. Two board-certified radiologists independently scored the PI-RADS, and if there were disagreements;another radiologist confirmed the diagnosis. We compared the AI results with the interpretation results by the radiologists. Results: The sensitivity of our AI model for PI-RADS ≥ 4 was 0.76, and the specificity was 0.76. For the cases with PI-RADS ≥ 3, the sensitivity was 0.69, and the specificity was 0.76. In the lesion-based analysis, AI detection rates of PI-RADS 3, 4, 5 lesions in the peripheral zone were 43%, 63%, and 100%, respectively. In the transition zone, AI detection rates of PI-RADS 3, 4, 5 were 30%, 54%, and 100%, respectively. Conclusion: Our deep-learning-based algorithm has been validated and shown to help score PI-RADS.
