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- Title
Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) maps.
- Authors
Hiremath, Amogh; Shiradkar, Rakesh; Merisaari, Harri; Prasanna, Prateek; Ettala, Otto; Taimen, Pekka; Aronen, Hannu J.; Boström, Peter J.; Jambor, Ivan; Madabhushi, Anant
- Abstract
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, ADCm. 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 ADCmb 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 ADCmb value settings, repeatability of mean ADCm 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 ADCmb 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 ADCmb 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.
- Subjects
DEEP learning; DIFFUSION coefficients; SIGNAL convolution; PROSTATE cancer; STATISTICAL reliability; CONVOLUTIONAL neural networks; BLAND-Altman plot
- Publication
European Radiology, 2021, Vol 31, Issue 1, p379
- ISSN
0938-7994
- Publication type
Article
- DOI
10.1007/s00330-020-07065-4