Deep Neural Network for Automatic Characterization of Lesions on 68Ga-PSMA PET/CT Images.

Zhao, Yu; Gafita, Andrei; Tetteh, Giles; Haupt, Fabian; Afshar Oromieh, Ali; Menze, Bjoern; Eiber, Matthias; Rominger, Axel; Shi, Kuangyu (2019). Deep Neural Network for Automatic Characterization of Lesions on 68Ga-PSMA PET/CT Images. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019, pp. 951-954. IEEE 10.1109/EMBC.2019.8857955

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The emerging PSMA-targeted radionuclide therapy provides an effective method for the treatment of advanced metastatic prostate cancer. To optimize the therapeutic effect and maximize the theranostic benefit, there is a need to identify and quantify target lesions prior to treatment. However, this is extremely challenging considering that a high number of lesions of heterogeneous size and uptake may distribute in a variety of anatomical context with different backgrounds. This study proposes an end-to-end deep neural network to characterize the prostate cancer lesions on PSMA imaging automatically. A 68Ga-PSMA-11 PET/CT image dataset including 71 patients with metastatic prostate cancer was collected from three medical centres for training and evaluating the proposed network. For proof-of-concept, we focus on the detection of bone and lymph node lesions in the pelvic area suggestive for metastases of prostate cancer. The preliminary test on pelvic area confirms the potential of deep learning methods. Increasing the amount of training data may further enhance the performance of the proposed deep learning method.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Clinic of Nuclear Medicine

UniBE Contributor:

Haupt, Fabian; Afshar Oromieh, Ali; Rominger, Axel Oliver and Shi, Kuangyu

Subjects:

600 Technology > 610 Medicine & health

ISSN:

2694-0604

Publisher:

IEEE

Language:

English

Submitter:

Sabine Lanz

Date Deposited:

04 Jan 2021 08:38

Last Modified:

04 Jan 2021 08:38

Publisher DOI:

10.1109/EMBC.2019.8857955

PubMed ID:

31946051

BORIS DOI:

10.48350/149135

URI:

https://boris.unibe.ch/id/eprint/149135

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