Automated pelvic MRI measurements associated with urinary incontinence for prostate cancer patients undergoing radical prostatectomy.

Autor: van den Berg I; Department of Radiation Oncology, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands. I.vandenBerg-8@umcutrecht.nl.; Department of Urology, St. Antonius Hospital, Nieuwegein, Utrecht, The Netherlands. I.vandenBerg-8@umcutrecht.nl., Spaans RN; Department of Urology, St. Antonius Hospital, Nieuwegein, Utrecht, The Netherlands.; Technical Medicine, University of Twente, Enschede, The Netherlands., Wessels FJ; Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands., van der Hoeven EJRJ; Department of Radiology, St. Antonius Hospital, Nieuwegein, Utrecht, The Netherlands., Nolthenius CJT; Department of Radiology, St. Antonius Hospital, Nieuwegein, Utrecht, The Netherlands., van den Bergh RCN; Department of Urology, St. Antonius Hospital, Nieuwegein, Utrecht, The Netherlands., van der Voort van Zyp JRN; Department of Radiation Oncology, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands., van den Berg CAT; Department of Radiation Oncology, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands., van Melick HHE; Department of Urology, St. Antonius Hospital, Nieuwegein, Utrecht, The Netherlands.
Jazyk: angličtina
Zdroj: European radiology experimental [Eur Radiol Exp] 2024 Jan 02; Vol. 8 (1), pp. 1. Date of Electronic Publication: 2024 Jan 02.
DOI: 10.1186/s41747-023-00402-4
Abstrakt: Background: Pelvic morphological parameters on magnetic resonance imaging (MRI), such as the membranous urethral length (MUL), can predict urinary incontinence after radical prostatectomy but are prone to interobserver disagreement. Our objective was to improve interobserver agreement among radiologists in measuring pelvic parameters using deep learning (DL)-based segmentation of pelvic structures on MRI scans.
Methods: Preoperative MRI was collected from 167 prostate cancer patients undergoing radical prostatectomy within our regional multicentric cohort. Two DL networks (nnU-Net) were trained on coronal and sagittal scans and evaluated on a test cohort using an 80/20% train-test split. Pelvic parameters were manually measured by three abdominal radiologists on raw MRI images and with the use of DL-generated segmentations. Automated measurements were also performed for the pelvic parameters. Interobserver agreement was evaluated using the intraclass correlation coefficient (ICC) and the Bland-Altman plot.
Results: The DL models achieved median Dice similarity coefficient (DSC) values of 0.85-0.97 for coronal structures and 0.87-0.98 for sagittal structures. When radiologists used DL-generated segmentations of pelvic structures, the interobserver agreement for sagittal MUL improved from 0.64 (95% confidence interval 0.28-0.83) to 0.91 (95% CI 0.84-0.95). Furthermore, there was an increase in ICC values for the obturator internus muscle from 0.74 (95% CI 0.42-0.87) to 0.86 (95% CI 0.75-0.92) and for the levator ani muscle from 0.40 (95% CI 0.05-0.66) to 0.61 (95% CI 0.31-0.78).
Conclusions: DL-based automated segmentation of pelvic structures improved interobserver agreement in measuring pelvic parameters on preoperative MRI scans.
Relevance Statement: The implementation of deep learning segmentations allows for more consistent measurements of pelvic parameters by radiologists. Standardized measurements are crucial for incorporating these parameters into urinary continence prediction models.
Key Points: • DL-generated segmentations improve interobserver agreement for pelvic measurements among radiologists. • Membranous urethral length measurement improved from substantial to almost perfect agreement. • Artificial intelligence enhances objective pelvic parameter assessment for continence prediction models.
(© 2023. The Author(s).)
Databáze: MEDLINE
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