Amogh Hiremath1, Rakesh Shiradkar2, Harri Merisaari2,3, Prateek Prasanna2,4, Otto Ettala5, Pekka Taimen6, Hannu J Aronen7, Peter J Boström5, Ivan Jambor3,8, Anant Madabhushi2,9. 1. Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA. axh672@case.edu. 2. Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA. 3. Department of Diagnostic Radiology, University of Turku, Turku, Finland. 4. Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA. 5. Department of Urology, University of Turku and Turku University Hospital, Turku, Finland. 6. Institute of Biomedicine, Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland. 7. Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland. 8. Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. 9. Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio, USA.
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 ADCm 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 ADCm b 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 ADCm 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 ADCm 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.
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 ADCm 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 ADCm b 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 ADCm 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 ADCm 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.
Authors: L Schimmöller; M Quentin; C Arsov; R S Lanzman; A Hiester; R Rabenalt; G Antoch; P Albers; D Blondin Journal: Eur Radiol Date: 2013-06-12 Impact factor: 5.315
Authors: Ivan Jambor; Harri Merisaari; Pekka Taimen; Peter Boström; Heikki Minn; Marko Pesola; Hannu J Aronen Journal: Magn Reson Med Date: 2014-07-12 Impact factor: 4.668
Authors: Geoffrey A Sonn; Richard E Fan; Pejman Ghanouni; Nancy N Wang; James D Brooks; Andreas M Loening; Bruce L Daniel; Katherine J To'o; Alan E Thong; John T Leppert Journal: Eur Urol Focus Date: 2017-12-07
Authors: Kathryn E Keenan; Jana G Delfino; Kalina V Jordanova; Megan E Poorman; Prathyush Chirra; Akshay S Chaudhari; Bettina Baessler; Jessica Winfield; Satish E Viswanath; Nandita M deSouza Journal: Med Phys Date: 2021-09-29 Impact factor: 4.506
Authors: Rakesh Shiradkar; Soumya Ghose; Amr Mahran; Lin Li; Isaac Hubbard; Pingfu Fu; Sree Harsha Tirumani; Lee Ponsky; Andrei Purysko; Anant Madabhushi Journal: Front Oncol Date: 2022-05-20 Impact factor: 5.738