A Systematic Review on Artificial Intelligence Evaluating Metastatic Prostatic Cancer and Lymph Nodes on PSMA PET Scans

Jianliang Liu; Thomas P Cundy; Dixon T S Woon; Nathan Lawrentschuk

doi:10.3390/cancers16030486

A Systematic Review on Artificial Intelligence Evaluating Metastatic Prostatic Cancer and Lymph Nodes on PSMA PET Scans

Cancers (Basel). 2024 Jan 23;16(3):486. doi: 10.3390/cancers16030486.

Authors

Jianliang Liu^{1

2

3}, Thomas P Cundy⁴, Dixon T S Woon^{1

3}, Nathan Lawrentschuk^{1

2

3}

Affiliations

¹ E.J. Whitten Prostate Cancer Research Centre, Epworth Healthcare, Melbourne, VIC 3005, Australia.
² Department of Urology, The Royal Melbourne Hospital, University of Melbourne, Melbourne, VIC 3052, Australia.
³ Department of Surgery, University of Melbourne, Melbourne, VIC 3052, Australia.
⁴ Discipline of Surgery, University of Adelaide, Adelaide, SA 5005, Australia.

Abstract

Early detection of metastatic prostate cancer (mPCa) is crucial. Whilst the prostate-specific membrane antigen (PSMA) PET scan has high diagnostic accuracy, it suffers from inter-reader variability, and the time-consuming reporting process. This systematic review was registered on PROSPERO (ID CRD42023456044) and aims to evaluate AI's ability to enhance reporting, diagnostics, and predictive capabilities for mPCa on PSMA PET scans. Inclusion criteria covered studies using AI to evaluate mPCa on PSMA PET, excluding non-PSMA tracers. A search was conducted on Medline, Embase, and Scopus from inception to July 2023. After screening 249 studies, 11 remained eligible for inclusion. Due to the heterogeneity of studies, meta-analysis was precluded. The prediction model risk of bias assessment tool (PROBAST) indicated a low overall risk of bias in ten studies, though only one incorporated clinical parameters (such as age, and Gleason score). AI demonstrated a high accuracy (98%) in identifying lymph node involvement and metastatic disease, albeit with sensitivity variation (62-97%). Advantages included distinguishing bone lesions, estimating tumour burden, predicting treatment response, and automating tasks accurately. In conclusion, AI showcases promising capabilities in enhancing the diagnostic potential of PSMA PET scans for mPCa, addressing current limitations in efficiency and variability.

Keywords: PSMA PET; artificial intelligence; convolutional neural network; deep learning; machine learning; prostate cancer.

Publication types

Review

Grants and funding

The first author is a PhD Candidate at the University of Melbourne and acknowledges the support of the Australian Commonwealth Government Research Training Program. The corresponding author is a recipient of the Epworth Prostate Cancer Research Grant which was made possible thanks to the generosity of the donors of the Epworth Medical Foundation.