Record link: 
http://hdl.handle.net/20.500.12708/218537
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Title: 
Deep learning based automated delineation of the intraprostatic gross tumour volume in PSMA-PET for patients with primary prostate cancer
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Citation: 
Holzschuh, J. C., Mix, M., Ruf, J., Hölscher, T., Kotzerke, J., Vrachimis, A., Doolan, P., Ilhan, H., Marinescu, I. M., Spohn, S. K. B., Fechter, T., Kuhn, D., Bronsert, P., Gratzke, C., Grosu, R., Kamran, S. C., Heidari, P., Ng, T. S. C., Könik, A., … Zamboglou, C. (2023). Deep learning based automated delineation of the intraprostatic gross tumour volume in PSMA-PET for patients with primary prostate cancer. RADIOTHERAPY AND ONCOLOGY, 188, Article 109774. https://doi.org/10.1016/j.radonc.2023.109774
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Publisher DOI: 
10.1016/j.radonc.2023.109774
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Publication Type: 
Article - Original Research Article
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Language: 
English
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Authors: 
Holzschuh, Julius C 
Mix, Michael 
Ruf, Juri 
Hölscher, Tobias 
Kotzerke, Jörg 
Vrachimis, Alexis 
Doolan, Paul 
Ilhan, Harun 
Marinescu, Ioana M 
Spohn, Simon Konrad Benedict 
Fechter, Tobias 
Kuhn, Dejan 
Bronsert, Peter 
Gratzke, Christian 
Grosu, Radu 
Kamran, Sophia C 
Heidari, Pedram 
Ng, Thomas S C 
Könik, Arda 
Grosu, Anca-Ligia 
Zamboglou, Constantinos 
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Organisational Unit: 
E191-01 - Forschungsbereich Cyber-Physical Systems 
E056-17 - Fachbereich Trustworthy Autonomous Cyber-Physical Systems 
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Journal: 
RADIOTHERAPY AND ONCOLOGY 
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ISSN: 
0167-8140
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Date (published): 
1-Nov-2023
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Number of Pages: 
8
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Publisher: 
ELSEVIER IRELAND LTD
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Peer reviewed: 
Yes
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Keywords: 
Male; Humans; Tumor Burden; Positron Emission Tomography Computed Tomography; Radiotherapy Planning, Computer-Assisted; CNN; Machine Learning; PSMA-PET; Prostate; Segmentation; Deep Learning; Prostatic Neoplasms
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Abstract: 
With the increased use of focal radiation dose escalation for primary prostate cancer (PCa), accurate delineation of gross tumor volume (GTV) in prostate-specific membrane antigen PET (PSMA-PET) becomes crucial. Manual approaches are time-consuming and observer dependent. The purpose of this study was to create a deep learning model for the accurate delineation of the intraprostatic GTV in PSMA-PET.
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Research Areas: 
Computer Engineering and Software-Intensive Systems: 100%
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Science Branch: 
2020 - Elektrotechnik, Elektronik, Informationstechnik: 100%
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