Artificial intelligence for the detection, quantification and characterization of metastatic prostate cancer in PSMA PET/CT - where are we now?

Afshar-Oromieh, Ali; Rominger, Axel; Shi, Kuangyu (2019). Artificial intelligence for the detection, quantification and characterization of metastatic prostate cancer in PSMA PET/CT - where are we now? Der Nuklearmediziner, 42(02), pp. 144-147. Thieme 10.1055/a-0916-6143

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Prostate cancer (PCa) is the most frequent tumor entity in
men worldwide. Since their clinical introduction in 2011,
PSMA-PET/CT and radionuclide therapy with PSMA-ligands
have rapidly spread worldwide and are regarded as significant
step forwards in the diagnosis and therapy of PCa.
However, it is still an unmet challenge to evaluate and control
all tumor lesions including their volume and characteristics
in the complex context of advanced multimetastatic
disease in PSMA-PET/CT. Such a control plays an important
role, e.g. for the optimization of PSMA-ligandtherapy. In
this context, artificial intelligence (AI) could play an important
role in the near future. The rapid development of AI in
the past few years has demonstrated its superiority in extending
the human power of data processing and provides
great potential to improve the detection, quantification
and characterization of metastatic prostate cancer lesions
in PSMA-PET/CT. This paper reviews the current progress
of the development of artificial intelligence methods for
PSMA-PET/CT and discusses the potential of clinical application.

Item Type:

Journal Article (Review Article)

Division/Institute:

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

UniBE Contributor:

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

Subjects:

600 Technology > 610 Medicine & health

ISSN:

0723-7065

Publisher:

Thieme

Language:

German

Submitter:

Sabine Lanz

Date Deposited:

23 Jan 2020 15:10

Last Modified:

23 Jan 2020 15:10

Publisher DOI:

10.1055/a-0916-6143

BORIS DOI:

10.7892/boris.137423

URI:

https://boris.unibe.ch/id/eprint/137423

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