From data to decisions: deep learning is shaping prostate cancer diagnostics
Author:
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00330-024-10997-w.pdf
Reference10 articles.
1. James ND, Tannock I, N’Dow J et al (2024) The Lancet Commission on prostate cancer: planning for the surge in cases. Lancet 403:1683–1722
2. Drost FH, 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
3. Cornford P, van den Bergh RCN, Briers E et al (2024) EAU-EANM-ESTRO-ESUR-ISUP-SIOG guidelines on prostate cancer—2024 Update. Part I: screening, diagnosis, and local treatment with curative intent. Eur Urol. https://doi.org/10.1016/j.eururo.2024.03.027
4. Westphalen AC, McCulloch CE, Anaokar JM et al (2020) Variability of the positive predictive value of PI-RADS for prostate MRI across 26 centers: experience of the Society of Abdominal Radiology Prostate Cancer Disease-focused Panel. Radiology 296:76–84
5. Padhani AR, Haider MA, Rouviere O (2022) Balancing the benefits and harms of MRI-directed biopsy pathways. Eur Radiol 32:2326–2329
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