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AI based automatic measurement of split renal function in [18F]PSMA-1007 PET/CT

Valind, Kristian LU orcid ; Ulén, Johannes LU ; Gålne, Anni LU orcid ; Jögi, Jonas LU orcid ; Minarik, David LU and Trägårdh, Elin LU orcid (2025) In EJNMMI reports 9.
Abstract

Background: Prostate-specific membrane antigen (PSMA) is an important target for positron emission tomography (PET) with computed tomography (CT) in prostate cancer. In addition to overexpression in prostate cancer cells, PSMA is expressed in healthy cells in the proximal tubules of the kidneys. Consequently, PSMA PET is being explored for renal functional imaging. Left and right renal uptake of PSMA targeted radiopharmaceuticals have shown strong correlations to split renal function (SRF) as determined by other methods. Manual segmentation of kidneys in PET images is, however, time consuming, making this method of measuring SRF impractical. In this study, we designed, trained and validated an artificial intelligence (AI) model for... (More)

Background: Prostate-specific membrane antigen (PSMA) is an important target for positron emission tomography (PET) with computed tomography (CT) in prostate cancer. In addition to overexpression in prostate cancer cells, PSMA is expressed in healthy cells in the proximal tubules of the kidneys. Consequently, PSMA PET is being explored for renal functional imaging. Left and right renal uptake of PSMA targeted radiopharmaceuticals have shown strong correlations to split renal function (SRF) as determined by other methods. Manual segmentation of kidneys in PET images is, however, time consuming, making this method of measuring SRF impractical. In this study, we designed, trained and validated an artificial intelligence (AI) model for automatic renal segmentation and measurement of SRF in [18F]PSMA-1007 PET images. Results: Kidneys were segmented in 135 [18F]PSMA-1007 PET/CT studies used to train the AI model. The model was evaluated in 40 test studies. Left renal function percentage (LRF%) measurements ranged from 40 to 67%. Spearman correlation coefficients for LRF% measurements ranged between 0.98 and 0.99 when comparing segmentations made by 3 human readers and the AI model. The largest LRF% difference between any measurements in a single case was 3 percentage points. The AI model produced measurements similar to those of human readers. Conclusions: Automatic measurement of SRF in PSMA PET is feasible. A potential use could be to provide additional data in investigation of renal functional impairment in patients treated for prostate cancer.

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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
AI, CNN, PET, PSMA, Renal function, Segmentation, SRF
in
EJNMMI reports
volume
9
article number
20
publisher
Springer Nature
external identifiers
  • pmid:40518464
  • scopus:105007992322
ISSN
3005-074X
DOI
10.1186/s41824-025-00254-8
language
English
LU publication?
yes
additional info
Publisher Copyright: © The Author(s) 2025.
id
6669292c-657a-47cf-89dc-f04afa46e1c0
date added to LUP
2025-06-23 09:01:57
date last changed
2025-07-07 09:12:12
@article{6669292c-657a-47cf-89dc-f04afa46e1c0,
  abstract     = {{<p>Background: Prostate-specific membrane antigen (PSMA) is an important target for positron emission tomography (PET) with computed tomography (CT) in prostate cancer. In addition to overexpression in prostate cancer cells, PSMA is expressed in healthy cells in the proximal tubules of the kidneys. Consequently, PSMA PET is being explored for renal functional imaging. Left and right renal uptake of PSMA targeted radiopharmaceuticals have shown strong correlations to split renal function (SRF) as determined by other methods. Manual segmentation of kidneys in PET images is, however, time consuming, making this method of measuring SRF impractical. In this study, we designed, trained and validated an artificial intelligence (AI) model for automatic renal segmentation and measurement of SRF in [<sup>18</sup>F]PSMA-1007 PET images. Results: Kidneys were segmented in 135 [<sup>18</sup>F]PSMA-1007 PET/CT studies used to train the AI model. The model was evaluated in 40 test studies. Left renal function percentage (LRF%) measurements ranged from 40 to 67%. Spearman correlation coefficients for LRF% measurements ranged between 0.98 and 0.99 when comparing segmentations made by 3 human readers and the AI model. The largest LRF% difference between any measurements in a single case was 3 percentage points. The AI model produced measurements similar to those of human readers. Conclusions: Automatic measurement of SRF in PSMA PET is feasible. A potential use could be to provide additional data in investigation of renal functional impairment in patients treated for prostate cancer.</p>}},
  author       = {{Valind, Kristian and Ulén, Johannes and Gålne, Anni and Jögi, Jonas and Minarik, David and Trägårdh, Elin}},
  issn         = {{3005-074X}},
  keywords     = {{AI; CNN; PET; PSMA; Renal function; Segmentation; SRF}},
  language     = {{eng}},
  publisher    = {{Springer Nature}},
  series       = {{EJNMMI reports}},
  title        = {{AI based automatic measurement of split renal function in [<sup>18</sup>F]PSMA-1007 PET/CT}},
  url          = {{http://dx.doi.org/10.1186/s41824-025-00254-8}},
  doi          = {{10.1186/s41824-025-00254-8}},
  volume       = {{9}},
  year         = {{2025}},
}