AI based automatic measurement of split renal function in [18F]PSMA-1007 PET/CT
Kristian Valind1,2,3, Johannes Ulén4, Anni Gålne5,6
1Department of Translational Medicine, Wallenberg Centre for Molecular Medicine, Lund University, Malmö, Sweden. kristian.valind@med.lu.se.
EJNMMI Reports
|June 15, 2025
Summary
An AI model can automatically segment kidneys in PSMA PET scans to measure split renal function (SRF). This AI tool provides accurate measurements comparable to manual methods, improving efficiency in renal imaging.
Area of Science:
- Nuclear medicine
- Radiopharmaceutical imaging
- Artificial intelligence in medical imaging
Background:
- Prostate-specific membrane antigen (PSMA) is overexpressed in prostate cancer and also present in kidney proximal tubules.
- PSMA PET imaging is being explored for renal functional assessment.
- Manual kidney segmentation for measuring split renal function (SRF) from PSMA PET is time-consuming and impractical.
Purpose of the Study:
- To design, train, and validate an artificial intelligence (AI) model for automatic renal segmentation.
- To measure SRF using an AI model in [18F]PSMA-1007 PET/CT images.
- To assess the feasibility and accuracy of AI-driven SRF measurement.
Main Methods:
- An AI model was developed for automatic segmentation of kidneys in [18F]PSMA-1007 PET/CT images.
- The AI model was trained on 135 PET/CT studies and validated on 40 independent studies.
- Measurements of left renal function percentage (LRF%) by the AI model were compared to those of three human readers.
Main Results:
- AI-driven segmentation and LRF% measurements were performed on 40 test studies.
- LRF% measurements showed strong correlations (Spearman coefficients 0.98-0.99) with human readers.
- The maximum difference in LRF% between AI and human measurements was 3 percentage points, indicating high agreement.
Conclusions:
- Automatic measurement of SRF using PSMA PET is feasible with AI.
- This AI tool can efficiently provide data for assessing renal functional impairment, particularly in patients treated for prostate cancer.
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