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Open AccessReview
Prostate-Specific Membrane Antigen-Positron Emission Tomography-Guided Radiomics and Machine Learning in Prostate Carcinoma
by
Justine Maes
Justine Maes 1,
Simon Gesquière
Simon Gesquière 2,
Alex Maes
Alex Maes 1,3,
Mike Sathekge
Mike Sathekge 4 and
Christophe Van de Wiele
Christophe Van de Wiele 1,5,*
1
Department of Nuclear Medicine, AZ Groeninge, 8500 Kortrijk, Belgium
2
Department of Nuclear Medicine, University Hospital Ghent, 9000 Ghent, Belgium
3
Department of Morphology and Functional Imaging, University Hospital Leuven, 3000 Leuven, Belgium
4
Department of Nuclear Medicine, University of Pretoria, Pretoria 0002, South Africa
5
Department of Diagnostic Sciences, University Ghent, 9000 Ghent, Belgium
*
Author to whom correspondence should be addressed.
Submission received: 9 September 2024
/
Revised: 16 September 2024
/
Accepted: 20 September 2024
/
Published: 1 October 2024
Simple Summary
Available studies suggest that radiomics and machine learning applied to PSMA-radioligand avid primary prostate carcinoma have potential to serve as an alternative for non-invasive Gleason score characterization, for the prediction of biochemical recurrence and to differentiate benign from malignant increased tracer uptake. However, prior to their implementation in clinical practice, additional, clinically relevant studies performed according to recently published guidelines and checklists, offering full transparency, including large enough datasets as well as external validation, are mandatory.
Abstract
Positron emission tomography (PET) using radiolabeled prostate-specific membrane antigen targeting PET-imaging agents has been increasingly used over the past decade for imaging and directing prostate carcinoma treatment. Here, we summarize the available literature data on radiomics and machine learning using these imaging agents in prostate carcinoma. Gleason scores derived from biopsy and after resection are discordant in a large number of prostate carcinoma patients. Available studies suggest that radiomics and machine learning applied to PSMA-radioligand avid primary prostate carcinoma might be better performing than biopsy-based Gleason-scoring and could serve as an alternative for non-invasive GS characterization. Furthermore, it may allow for the prediction of biochemical recurrence with a net benefit for clinical utilization. Machine learning based on PET/CT radiomics features was also shown to be able to differentiate benign from malignant increased tracer uptake on PSMA-targeting radioligand PET/CT examinations, thus paving the way for a fully automated image reading in nuclear medicine. As for prediction to treatment outcome following 177Lu-PSMA therapy and overall survival, a limited number of studies have reported promising results on radiomics and machine learning applied to PSMA-targeting radioligand PET/CT images for this purpose. Its added value to clinical parameters warrants further exploration in larger datasets of patients.
Share and Cite
MDPI and ACS Style
Maes, J.; Gesquière, S.; Maes, A.; Sathekge, M.; Van de Wiele, C.
Prostate-Specific Membrane Antigen-Positron Emission Tomography-Guided Radiomics and Machine Learning in Prostate Carcinoma. Cancers 2024, 16, 3369.
https://doi.org/10.3390/cancers16193369
AMA Style
Maes J, Gesquière S, Maes A, Sathekge M, Van de Wiele C.
Prostate-Specific Membrane Antigen-Positron Emission Tomography-Guided Radiomics and Machine Learning in Prostate Carcinoma. Cancers. 2024; 16(19):3369.
https://doi.org/10.3390/cancers16193369
Chicago/Turabian Style
Maes, Justine, Simon Gesquière, Alex Maes, Mike Sathekge, and Christophe Van de Wiele.
2024. "Prostate-Specific Membrane Antigen-Positron Emission Tomography-Guided Radiomics and Machine Learning in Prostate Carcinoma" Cancers 16, no. 19: 3369.
https://doi.org/10.3390/cancers16193369
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