Dataset Information

Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment.

ABSTRACT:

Objectives

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.

Methods

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.

Results

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.

Conclusions

U-Net has similar performance to PI-RADS in simulated continued clinical use. Regular quality assurance should be implemented to ensure desired performance.

Key points

• 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.

SUBMITTER: Schelb P

PROVIDER: S-EPMC7755653 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment.

Schelb Patrick P Wang Xianfeng X Radtke Jan Philipp JP Wiesenfarth Manuel M Kickingereder Philipp P Stenzinger Albrecht A Hohenfellner Markus M Schlemmer Heinz-Peter HP Maier-Hein Klaus H KH Bonekamp David D

European radiology 20200807 1

<h4>Objectives</h4>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.<h4>Methods</h4>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 ...[more]

PMID: 32767102

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Dataset Information

Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment.

Objectives

Methods

Results

Conclusions

Key points

Publications

Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment.

Similar Datasets

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