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| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Schrader, Adrian |0 P:(DE-He78)b524425cb0469141913534458d441006 |b 0 |e First author |u dkfz |
| 245 | _ | _ | |a 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. |
| 260 | _ | _ | |a Heidelberg |c 2024 |b Springer |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1732099161_11919 |2 PUB:(DE-HGF) |
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| 500 | _ | _ | |a #EA:E010#LA:E010# / Volume 34, pages 7909–7920, (2024) |
| 520 | _ | _ | |a Risk calculators (RCs) improve patient selection for prostate biopsy with clinical/demographic information, recently with prostate MRI using the prostate imaging reporting and data system (PI-RADS). Fully-automated deep learning (DL) analyzes MRI data independently, and has been shown to be on par with clinical radiologists, but has yet to be incorporated into RCs. The goal of this study is to re-assess the diagnostic quality of RCs, the impact of replacing PI-RADS with DL predictions, and potential performance gains by adding DL besides PI-RADS.One thousand six hundred twenty-seven consecutive examinations from 2014 to 2021 were included in this retrospective single-center study, including 517 exams withheld for RC testing. Board-certified radiologists assessed PI-RADS during clinical routine, then systematic and MRI/Ultrasound-fusion biopsies provided histopathological ground truth for significant prostate cancer (sPC). nnUNet-based DL ensembles were trained on biparametric MRI predicting the presence of sPC lesions (UNet-probability) and a PI-RADS-analogous five-point scale (UNet-Likert). Previously published RCs were validated as is; with PI-RADS substituted by UNet-Likert (UNet-Likert-substituted RC); and with both UNet-probability and PI-RADS (UNet-probability-extended RC). Together with a newly fitted RC using clinical data, PI-RADS and UNet-probability, existing RCs were compared by receiver-operating characteristics, calibration, and decision-curve analysis.Diagnostic performance remained stable for UNet-Likert-substituted RCs. DL contained complementary diagnostic information to PI-RADS. The newly-fitted RC spared 49% [252/517] of biopsies while maintaining the negative predictive value (94%), compared to PI-RADS ≥ 4 cut-off which spared 37% [190/517] (p < 0.001).Incorporating DL as an independent diagnostic marker for RCs can improve patient stratification before biopsy, as there is complementary information in DL features and clinical PI-RADS assessment.For patients with positive prostate screening results, a comprehensive diagnostic workup, including prostate MRI, DL analysis, and individual classification using nomograms can identify patients with minimal prostate cancer risk, as they benefit less from the more invasive biopsy procedure.The current MRI-based nomograms result in many negative prostate biopsies. The addition of DL to nomograms with clinical data and PI-RADS improves patient stratification before biopsy. Fully automatic DL can be substituted for PI-RADS without sacrificing the quality of nomogram predictions. Prostate nomograms show cancer detection ability comparable to previous validation studies while being suitable for the addition of DL analysis. |
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| 650 | _ | 7 | |a Deep learning |2 Other |
| 650 | _ | 7 | |a Nomograms |2 Other |
| 650 | _ | 7 | |a Prostatic neoplasms, Multiparametric magnetic resonance imaging |2 Other |
| 650 | _ | 7 | |a Risk assessment |2 Other |
| 700 | 1 | _ | |a Netzer, Nils |0 P:(DE-He78)32c69a3bed6c75b378ef19ad39a74572 |b 1 |u dkfz |
| 700 | 1 | _ | |a Hielscher, Thomas |0 P:(DE-He78)743a4a82daab55306a2c88b9f6bf8c2f |b 2 |u dkfz |
| 700 | 1 | _ | |a Görtz, Magdalena |0 P:(DE-He78)0f26d76d27427945f14f0e874d824aa6 |b 3 |u dkfz |
| 700 | 1 | _ | |a Zhang, Kevin Sun |0 P:(DE-He78)b542df279437ced507cda1a8c93a2d4d |b 4 |u dkfz |
| 700 | 1 | _ | |a Schütz, Viktoria |b 5 |
| 700 | 1 | _ | |a Stenzinger, Albrecht |b 6 |
| 700 | 1 | _ | |a Hohenfellner, Markus |b 7 |
| 700 | 1 | _ | |a Schlemmer, Heinz-Peter |0 P:(DE-He78)3d04c8fee58c9ab71f62ff80d06b6fec |b 8 |u dkfz |
| 700 | 1 | _ | |a Bonekamp, David |0 P:(DE-He78)ea098e4d78abeb63afaf8c25ec6d6d93 |b 9 |e Last author |u dkfz |
| 773 | _ | _ | |a 10.1007/s00330-024-10818-0 |0 PERI:(DE-600)1472718-3 |p 7909–7920 |t European radiology |v 34 |y 2024 |x 0938-7994 |
| 856 | 4 | _ | |u https://inrepo02.dkfz.de/record/291445/files/s00330-024-10818-0.pdf |
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