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Editorial
. 2022 Apr;32(4):2221-2223.
doi: 10.1007/s00330-022-08609-6. Epub 2022 Feb 23.

Assessing the clinical performance of artificial intelligence software for prostate cancer detection on MRI

Tobias Penzkofer  1 , Anwar R Padhani  2 , Baris Turkbey  3 , Hashim U Ahmed  4
Affiliations
Editorial

Assessing the clinical performance of artificial intelligence software for prostate cancer detection on MRI

Tobias Penzkofer et al. Eur Radiol. 2022 Apr.
No abstract available

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References

    1. Twilt JJ, van Leeuwen KG, Huisman HJ et al (2021) Artificial intelligence based algorithms for prostate cancer classification and detection on magnetic resonance imaging: a narrative review. Diagnostics (Basel) 11:959. https://doi.org/10.3390/diagnostics11060959
    1. Turkbey B, Haider MA (2021) Artificial intelligence (AI) for automated cancer detection on prostate MRI: opportunities and ongoing challenges, from the AJR Special Series on AI Applications. AJR Am J Roentgenol. https://doi.org/10.2214/AJR.21.26917
    1. Syer T, Mehta P, Antonelli M et al (2021) Artificial intelligence compared to radiologists for the initial diagnosis of prostate cancer on magnetic resonance imaging: a systematic review and recommendations for future studies. Cancers (Basel) 13:3318. https://doi.org/10.3390/cancers13133318 - DOI
    1. Mottet N, van den Bergh RCN, Briers E et al (2021) EAU-EANM-ESTRO-ESUR-SIOG guidelines on prostate cancer-2020 update. Part 1: Screening, diagnosis, and local treatment with curative intent. Eur Urol 79:243–262. https://doi.org/10.1016/j.eururo.2020.09.042 - DOI - PubMed
    1. Drost F-JH, Osses D, Nieboer D et al (2020) Prostate magnetic resonance imaging, with or without magnetic resonance imaging-targeted biopsy, and systematic biopsy for detecting prostate cancer: a Cochrane systematic review and meta-analysis. Eur Urol 77:78–94. https://doi.org/10.1016/j.eururo.2019.06.023 - DOI - PubMed

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