Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) maps.

Autor: Hiremath A; Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA. axh672@case.edu., Shiradkar R; Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA., Merisaari H; Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.; Department of Diagnostic Radiology, University of Turku, Turku, Finland., Prasanna P; Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.; Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA., Ettala O; Department of Urology, University of Turku and Turku University Hospital, Turku, Finland., Taimen P; Institute of Biomedicine, Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland., Aronen HJ; Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland., Boström PJ; Department of Urology, University of Turku and Turku University Hospital, Turku, Finland., Jambor I; Department of Diagnostic Radiology, University of Turku, Turku, Finland.; Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Madabhushi A; Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.; Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio, USA.
Jazyk: angličtina
Zdroj: European radiology [Eur Radiol] 2021 Jan; Vol. 31 (1), pp. 379-391. Date of Electronic Publication: 2020 Jul 23.
DOI: 10.1007/s00330-020-07065-4
Abstrakt: Objectives: To evaluate short-term test-retest repeatability of a deep learning architecture (U-Net) in slice- and lesion-level detection and segmentation of clinically significant prostate cancer (csPCa: Gleason grade group > 1) using diffusion-weighted imaging fitted with monoexponential function, ADC m .
Methods: One hundred twelve patients with prostate cancer (PCa) underwent 2 prostate MRI examinations on the same day. PCa areas were annotated using whole mount prostatectomy sections. Two U-Net-based convolutional neural networks were trained on three different ADC m b value settings for (a) slice- and (b) lesion-level detection and (c) segmentation of csPCa. Short-term test-retest repeatability was estimated using intra-class correlation coefficient (ICC(3,1)), proportionate agreement, and dice similarity coefficient (DSC). A 3-fold cross-validation was performed on training set (N = 78 patients) and evaluated for performance and repeatability on testing data (N = 34 patients).
Results: For the three ADC m b value settings, repeatability of mean ADC m of csPCa lesions was ICC(3,1) = 0.86-0.98. Two CNNs with U-Net-based architecture demonstrated ICC(3,1) in the range of 0.80-0.83, agreement of 66-72%, and DSC of 0.68-0.72 for slice- and lesion-level detection and segmentation of csPCa. Bland-Altman plots suggest that there is no systematic bias in agreement between inter-scan ground truth segmentation repeatability and segmentation repeatability of the networks.
Conclusions: For the three ADC m b value settings, two CNNs with U-Net-based architecture were repeatable for the problem of detection of csPCa at the slice-level. The network repeatability in segmenting csPCa lesions is affected by inter-scan variability and ground truth segmentation repeatability and may thus improve with better inter-scan reproducibility.
Key Points: • For the three ADC m b value settings, two CNNs with U-Net-based architecture were repeatable for the problem of detection of csPCa at the slice-level. • The network repeatability in segmenting csPCa lesions is affected by inter-scan variability and ground truth segmentation repeatability and may thus improve with better inter-scan reproducibility.
Databáze: MEDLINE
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