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| Home > Publications database > Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment. > print |
| 001 | 157380 | ||
| 005 | 20240229133507.0 | ||
| 024 | 7 | _ | |a 10.1007/s00330-020-07086-z |2 doi |
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| 100 | 1 | _ | |a Schelb, Patrick |0 P:(DE-He78)4b5e5faa688c6b833c70b6777f91f662 |b 0 |e First author |
| 245 | _ | _ | |a Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment. |
| 260 | _ | _ | |a Heidelberg |c 2021 |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 1609765244_8234 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a 2021 Jan;31(1):302-313#EA:E010#LA:E010# |
| 520 | _ | _ | |a To simulate clinical deployment, evaluate performance, and establish quality assurance of a deep learning algorithm (U-Net) for detection, localization, and segmentation of clinically significant prostate cancer (sPC), ISUP grade group ≥ 2, using bi-parametric MRI.In 2017, 284 consecutive men in active surveillance, biopsy-naïve or pre-biopsied, received targeted and extended systematic MRI/transrectal US-fusion biopsy, after examination on a single MRI scanner (3 T). A prospective adjustment scheme was evaluated comparing the performance of the Prostate Imaging Reporting and Data System (PI-RADS) and U-Net using sensitivity, specificity, predictive values, and the Dice coefficient.In the 259 eligible men (median 64 [IQR 61-72] years), PI-RADS had a sensitivity of 98% [106/108]/84% [91/108] with a specificity of 17% [25/151]/58% [88/151], for thresholds at ≥ 3/≥ 4 respectively. U-Net using dynamic threshold adjustment had a sensitivity of 99% [107/108]/83% [90/108] (p > 0.99/> 0.99) with a specificity of 24% [36/151]/55% [83/151] (p > 0.99/> 0.99) for probability thresholds d3 and d4 emulating PI-RADS ≥ 3 and ≥ 4 decisions respectively, not statistically different from PI-RADS. Co-occurrence of a radiological PI-RADS ≥ 4 examination and U-Net ≥ d3 assessment significantly improved the positive predictive value from 59 to 63% (p = 0.03), on a per-patient basis.U-Net has similar performance to PI-RADS in simulated continued clinical use. Regular quality assurance should be implemented to ensure desired performance.• U-Net maintained similar diagnostic performance compared to radiological assessment of PI-RADS ≥ 4 when applied in a simulated clinical deployment. • Application of our proposed prospective dynamic calibration method successfully adjusted U-Net performance within acceptable limits of the PI-RADS reference over time, while not being limited to PI-RADS as a reference. • Simultaneous detection by U-Net and radiological assessment significantly improved the positive predictive value on a per-patient and per-lesion basis, while the negative predictive value remained unchanged. |
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| 700 | 1 | _ | |a Wang, Xianfeng |0 P:(DE-He78)aec14a1077ed145f9ebe9de1d50905b0 |b 1 |
| 700 | 1 | _ | |a Radtke, Jan Philipp |0 P:(DE-He78)79897f8897ff77676549d9895258a0f2 |b 2 |u dkfz |
| 700 | 1 | _ | |a Wiesenfarth, Manuel |0 P:(DE-He78)1042737c83ba70ec508bdd99f0096864 |b 3 |
| 700 | 1 | _ | |a Kickingereder, Philipp |b 4 |
| 700 | 1 | _ | |a Stenzinger, Albrecht |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Hohenfellner, Markus |b 6 |
| 700 | 1 | _ | |a Schlemmer, Heinz-Peter |0 P:(DE-He78)3d04c8fee58c9ab71f62ff80d06b6fec |b 7 |
| 700 | 1 | _ | |a Maier-Hein, Klaus H |0 P:(DE-He78)33c74005e1ce56f7025c4f6be15321b3 |b 8 |
| 700 | 1 | _ | |a Bonekamp, David |0 P:(DE-He78)ea098e4d78abeb63afaf8c25ec6d6d93 |b 9 |e Last author |u dkfz |
| 773 | _ | _ | |a 10.1007/s00330-020-07086-z |0 PERI:(DE-600)1472718-3 |n 1 |p 302-313 |t European radiology |v 31 |y 2021 |x 1432-1084 |
| 856 | 4 | _ | |u https://pubmed.ncbi.nlm.nih.gov/32767102/ |
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