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Deep learning based automated delineation of the intraprostatic gross tumour volume in PSMA-PET for patients with primary prostate cancer

2023; Elsevier BV; Volume: 188; Linguagem: Inglês

10.1016/j.radonc.2023.109774

1879-0887

Julius C. Holzschuh, Michael Mix, Juri Ruf, Tobias Hölscher, Jörg Kotzerke, Alexis Vrachimis, Paul Doolan, Harun Ilhan, Ioana M. Marinescu, Simon K. B. Spohn, Tobias Fechter, Dejan Kuhn, Peter Bronsert, Christian Gratzke, Radu Grosu, Sophia C. Kamran, Pedram Heidari, Thomas S.C. Ng, Arda Könik, Anca‐Ligia Grosu, Constantinos Zamboglou,

Radiomics and Machine Learning in Medical Imaging

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.