Prostate cancer risk assessment and avoidance of prostate biopsies using fully automatic deep learning in prostate MRI: comparison to PI-RADS and integration with clinical data in nomograms.
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| Title: | Prostate cancer risk assessment and avoidance of prostate biopsies using fully automatic deep learning in prostate MRI: comparison to PI-RADS and integration with clinical data in nomograms. |
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| Authors: | Schrader A; Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.; Heidelberg University Medical School, Heidelberg, Germany., Netzer N; Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.; Heidelberg University Medical School, Heidelberg, Germany., Hielscher T; Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany., Görtz M; Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany.; Junior Clinical Cooperation Unit 'Multiparametric Methods for Early Detection of Prostate Cancer', German Cancer Research Center (DKFZ), Heidelberg, Germany., Zhang KS; Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany., Schütz V; Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany., Stenzinger A; Institute of Pathology, University of Heidelberg Medical Center, Heidelberg, Germany., Hohenfellner M; Department of Urology, University of Heidelberg Medical Center, Heidelberg, Germany., Schlemmer HP; Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.; National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany., Bonekamp D; Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany. d.bonekamp@dkfz-heidelberg.de.; Heidelberg University Medical School, Heidelberg, Germany. d.bonekamp@dkfz-heidelberg.de.; National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany. d.bonekamp@dkfz-heidelberg.de. |
| Source: | European radiology [Eur Radiol] 2024 Dec; Vol. 34 (12), pp. 7909-7920. Date of Electronic Publication: 2024 Jul 02. |
| Publication Type: | Journal Article; Comparative Study |
| Journal Info: | Publisher: Springer International Country of Publication: Germany NLM ID: 9114774 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1084 (Electronic) Linking ISSN: 09387994 NLM ISO Abbreviation: Eur Radiol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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