A systematic review on artificial intelligence evaluating PSMA PET scan for intraprostatic cancer
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View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence shows promise in evaluating prostate cancer (PCa) on PSMA PET scans for treatment planning. While AI can differentiate high-grade PCa and identify lesions, it requires further validation before clinical use.
Area Of Science
- Oncology
- Radiology
- Artificial Intelligence
Background
- Prostate cancer (PCa) evaluation before treatment relies on accurate imaging.
- Prostate-specific membrane antigen positron emission tomography (PSMA PET) is increasingly used for PCa staging.
- Assessing intraprostatic PCa on PSMA PET scans is crucial for treatment decisions.
Approach
- A systematic review was conducted searching major databases for studies on AI evaluating intraprostatic PCa on PSMA PET scans.
- Included studies focused on AI's ability to assess lesions prior to active treatment (radiotherapy or prostatectomy).
- The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used for quality assessment.
Key Points
- AI demonstrated high accuracy in differentiating high-grade PCa (ISUP Grade Group ≥3 and ≥4).
- AI showed capability in identifying non-PSMA-avid lesions and detecting extraprostatic extensions.
- AI can automate lesion segmentation and gross tumor volume measurement on PSMA PET scans.
Conclusions
- Current AI applications for evaluating intraprostatic PCa on PSMA PET scans are promising but experimental.
- AI offers potential benefits in local staging, identifying occult lesions, and standardizing reporting.
- Further large-scale, prospective, multicenter studies are necessary to establish AI for routine clinical use.
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