Prostate MRI: what to consider when shopping for AI tools
Author:
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00330-024-10867-5.pdf
Reference9 articles.
1. Basset O, Sun Z, Mestas JL, Gimenez G (1993) Texture analysis of ultrasonic images of the prostate by means of co-occurrence matrices. Ultrason Imaging 15:218–237. https://doi.org/10.1177/016173469301500303
2. Hamm CA, Baumgartner GL, Padhani AR et al (2024) Reduction of false positives using zone-specific prostate-specific antigen density for prostate MRI-based biopsy decision strategies. Eur Radiol. https://doi.org/10.1007/s00330-024-10700-z10.1007/s00330-024-10700-z
3. Guenzel K, Lukas Baumgaertner G, Padhani AR et al (2024) Diagnostic utility of artificial intelligence-assisted transperineal biopsy planning in prostate cancer suspected men: a prospective cohort study. Eur Urol Focus S2405-4569:00059–2. https://doi.org/10.1016/j.euf.2024.04.00710.1016/j.euf.2024.04.007
4. European Society of R (2023) ESR paper on structured reporting in radiology-update 2023. Insights Imaging 14:199. https://doi.org/10.1186/s13244-023-01560-0
5. Drost FH, Osses DF, Nieboer D et al (2019) Prostate MRI, with or without MRI-targeted biopsy, and systematic biopsy for detecting prostate cancer. Cochrane Database Syst Rev 4:CD012663. https://doi.org/10.1002/14651858.CD012663.pub2
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