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A Validation Study of the Deep-Learning-Based Prostate Imaging Reporting and Data System Scoring Algorithm

Open Journal of Radiology 2022年第3期12卷 59-67页

作者： Ryusuke Irie maki amano Kazusa Sugeno Shingo Okada Ali Kamen Bin Lou Heinrich von Busch Robert Grimm Dorin Comaniciu Toshiaki Akashi Ryohei Kuwatsuru Shigeo Horie Kanako K. Kumamaru Shigeki Aoki Department of Radiology Juntendo University School of Medicine Tokyo Japan Department of Radiology Juntendo University Shizuoka Hospital Shizuoka Japan Digital Technology and Innovation Division Siemens Healthineers Princeton USA Digital Health Siemens Healthcare GmbH Erlangen Germany MR Application Predevelopment Siemens Healthcare GmbH Erlangen Germany Department of Urology Juntendo University School of Medicine Tokyo Japan

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.

关键词： Deep Learning Artificial Intelligence Prostate Cancer PI-RADS

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A Validation Study of the Deep-Learning-Based Prostate Imaging Reporting and Data System Scoring Algorithm

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Open Journal of Radiology（放射学期刊） 2022年第3期

作者： ryusuke irie maki amano kazusa sugeno shingo okada ali kamen bin lou heinrich von busch robert grimm dorin comaniciu toshiaki akashi ryohei kuwatsuru shigeo horie kanako k. kumamaru shigeki aoki

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