دورية أكاديمية
أعرض في EDS

Deep learning for automated contouring of neurovascular structures on magnetic resonance imaging for prostate cancer patients.

التفاصيل البيبلوغرافية
العنوان: Deep learning for automated contouring of neurovascular structures on magnetic resonance imaging for prostate cancer patients.
المؤلفون: van den Berg I; Department of Radiation Oncology, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.; Department of Urology, St. Antonius Hospital, Nieuwegein, Utrecht, The Netherlands.; Magnetic Detection and Imaging Group, Technical Medical Centre, University of Twente, Enschede, the Netherlands., Savenije MHF; Department of Radiation Oncology, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands., Teunissen FR; Department of Radiation Oncology, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands., van de Pol SMG; Department of Radiation Oncology, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands., Rasing MJA; 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., Brink WM; Magnetic Detection and Imaging Group, Technical Medical Centre, University of Twente, Enschede, the Netherlands., de Boer JCJ; 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 der Voort van Zyp JRN; Department of Radiation Oncology, Division of Imaging & Oncology, University Medical Center Utrecht, Utrecht, The Netherlands.
المصدر: Physics and imaging in radiation oncology [Phys Imaging Radiat Oncol] 2023 Jun 01; Vol. 26, pp. 100453. Date of Electronic Publication: 2023 Jun 01 (Print Publication: 2023).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Elsevier B.V Country of Publication: Netherlands NLM ID: 101704276 Publication Model: eCollection Cited Medium: Internet ISSN: 2405-6316 (Electronic) Linking ISSN: 24056316 NLM ISO Abbreviation: Phys Imaging Radiat Oncol Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: [Amsterdam] : Elsevier B.V., [2017]-
مستخلص: Background and Purpose: Manual contouring of neurovascular structures on prostate magnetic resonance imaging (MRI) is labor-intensive and prone to considerable interrater disagreement. Our aim is to contour neurovascular structures automatically on prostate MRI by deep learning (DL) to improve workflow and interrater agreement.
Materials and Methods: Segmentation of neurovascular structures was performed on pre-treatment 3.0 T MRI data of 131 prostate cancer patients (training [n = 105] and testing [n = 26]). The neurovascular structures include the penile bulb (PB), corpora cavernosa (CCs), internal pudendal arteries (IPAs), and neurovascular bundles (NVBs). Two DL networks, nnU-Net and DeepMedic, were trained for auto-contouring on prostate MRI and evaluated using volumetric Dice similarity coefficient (DSC), mean surface distances (MSD), Hausdorff distances, and surface DSC. Three radiation oncologists evaluated the DL-generated contours and performed corrections when necessary. Interrater agreement was assessed and the time required for manual correction was recorded.
Results: nnU-Net achieved a median DSC of 0.92 (IQR: 0.90-0.93) for the PB, 0.90 (IQR: 0.86-0.92) for the CCs, 0.79 (IQR: 0.77-0.83) for the IPAs, and 0.77 (IQR: 0.72-0.81) for the NVBs, which outperformed DeepMedic for each structure (p < 0.03). nnU-Net showed a median MSD of 0.24 mm for the IPAs and 0.71 mm for the NVBs. The median interrater DSC ranged from 0.93 to 1.00, with the majority of cases (68.9%) requiring manual correction times under two minutes.
Conclusions: DL enables reliable auto-contouring of neurovascular structures on pre-treatment MRI data, easing the clinical workflow in neurovascular-sparing MR-guided radiotherapy.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2023 The Author(s).)
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فهرسة مساهمة: Keywords: Artificial intelligence (AI); Contouring; Deep learning (DL); Magnetic resonance-guided radiotherapy (MRgRT); Neurovascular-sparing; Prostate cancer (PCa)
تواريخ الأحداث: Date Created: 20230614 Latest Revision: 20230615
رمز التحديث: 20230615
مُعرف محوري في PubMed: PMC10258498
DOI: 10.1016/j.phro.2023.100453
PMID: 37312973
قاعدة البيانات: MEDLINE
الوصف
تدمد:2405-6316
DOI:10.1016/j.phro.2023.100453