Effect of data-driven gating on the quantification of prostate cancer metastases in 68Ga-PSMA- and ¹⁸F-DCFPyL-PSMA PET/CT
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Abstract
Background PSMA PET/CT using 18F‑DCFPyL and 68Ga‑labelled tracers is central to prostate cancer staging, but 68Ga‑PSMA may show reduced sensitivity and quantitative accuracy, partly due to respiratory motion and lower spatial resolution. Device-based motion‑correction methods have limitations. Data‑driven gating (DDG) offers a hardware‑free alternative, though its impact on PSMA PET, especially 68Ga‑PSMA has not been systematically evaluated. To evaluate the quantitative and qualitative performance of the DDG motion-correction method in 68Ga-PSMA and 18F-DCFPyL-PSMA PET/CT, focusing on metastatic lymph node, lung, and bone lesions, and to assess its robustness across tracers. Methods This retrospective study included patients with biopsy‑proven prostate cancer who underwent 68Ga‑PSMA or 18F‑DCFPyL PSMA PET/CT for primary staging or biochemical recurrence and had metastatic disease. Motion correction was applied using the OncoFreeze AI™ data‑driven gating (OFAI‑DDG) method. Metastatic lesions were segmented semi‑automatically, and SUV metrics, image noise, and contrast‑to‑noise ratio were quantified by anatomical site in both OFAI-DDG and static images. Qualitative image quality was independently scored by two experienced readers. Statistical analyses included non‑parametric tests, Bland-Altman agreement, regression analyses, tracer comparison, and inter‑reader reliability assessment. Results After exclusions, 23 68Ga‑PSMA and 40 18F‑PSMA patients were analyzed, yielding 133 and 192 metastatic lesions, respectively. OFAI‑DDG reconstruction significantly increased SUVmean and SUVmax compared with static imaging for both tracers across bone, lymph node, and lung lesions (p < 0.0001), with the largest gains in lung lesions. CNR improved for most lesion types, and image noise was significantly reduced. Qualitative analysis showed better image quality, sharpness, and noise with OFAI‑DDG, with high inter‑reader agreement. OFAI‑DDG performance was robust across tracers and largely independent of lesion location. Conclusions This study demonstrates that OFAI‑DDG significantly improves PSMA PET/CT image quality and quantitative accuracy compared with standard static imaging. The improvements were observed for both 68Ga‑PSMA and 18F‑PSMA tracers, indicating tracer‑independent robustness. The OFAI‑DDG simplified the workflow and reduced time used for patient set-up, while enhancing lesion visualization and quantitative reliability without altering lesion detection, supporting its clinical value for metastatic prostate cancer imaging.
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BibTeX
@article{farag2026effect,
title = {{Effect of data-driven gating on the quantification of prostate cancer metastases in 68Ga-PSMA- and ¹⁸F-DCFPyL-PSMA PET/CT}},
author = {Adam Farag and Cornel Felder and Douglas Hussey and Seyed Ali Mirshahvalad and Sara Harsini and Ur Metser and Patrick Veit-Haibach},
journal = {EJNMMI reports},
year = {2026},
month = oct,
doi = {10.1186/s41824-026-00328-1},
url = {https://europepmc.org/article/PMC/PMC13691034},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Effect of data-driven gating on the quantification of prostate cancer metastases in 68Ga-PSMA- and ¹⁸F-DCFPyL-PSMA PET/CT
AU - Adam Farag
AU - Cornel Felder
AU - Douglas Hussey
AU - Seyed Ali Mirshahvalad
AU - Sara Harsini
AU - Ur Metser
AU - Patrick Veit-Haibach
PY - 2026
DA - 2026/10/23
JO - EJNMMI reports
DO - 10.1186/s41824-026-00328-1
UR - https://europepmc.org/article/PMC/PMC13691034
AB - Background PSMA PET/CT using 18F‑DCFPyL and 68Ga‑labelled tracers is central to prostate cancer staging, but 68Ga‑PSMA may show reduced sensitivity and quantitative accuracy, partly due to respiratory motion and lower spatial resolution. Device-based motion‑correction methods have limitations. Data‑driven gating (DDG) offers a hardware‑free alternative, though its impact on PSMA PET, especially 68Ga‑PSMA has not been systematically evaluated. To evaluate the quantitative and qualitative performance of the DDG motion-correction method in 68Ga-PSMA and 18F-DCFPyL-PSMA PET/CT, focusing on metastatic lymph node, lung, and bone lesions, and to assess its robustness across tracers. Methods This retrospective study included patients with biopsy‑proven prostate cancer who underwent 68Ga‑PSMA or 18F‑DCFPyL PSMA PET/CT for primary staging or biochemical recurrence and had metastatic disease. Motion correction was applied using the OncoFreeze AI™ data‑driven gating (OFAI‑DDG) method. Metastatic lesions were segmented semi‑automatically, and SUV metrics, image noise, and contrast‑to‑noise ratio were quantified by anatomical site in both OFAI-DDG and static images. Qualitative image quality was independently scored by two experienced readers. Statistical analyses included non‑parametric tests, Bland-Altman agreement, regression analyses, tracer comparison, and inter‑reader reliability assessment. Results After exclusions, 23 68Ga‑PSMA and 40 18F‑PSMA patients were analyzed, yielding 133 and 192 metastatic lesions, respectively. OFAI‑DDG reconstruction significantly increased SUVmean and SUVmax compared with static imaging for both tracers across bone, lymph node, and lung lesions (p < 0.0001), with the largest gains in lung lesions. CNR improved for most lesion types, and image noise was significantly reduced. Qualitative analysis showed better image quality, sharpness, and noise with OFAI‑DDG, with high inter‑reader agreement. OFAI‑DDG performance was robust across tracers and largely independent of lesion location. Conclusions This study demonstrates that OFAI‑DDG significantly improves PSMA PET/CT image quality and quantitative accuracy compared with standard static imaging. The improvements were observed for both 68Ga‑PSMA and 18F‑PSMA tracers, indicating tracer‑independent robustness. The OFAI‑DDG simplified the workflow and reduced time used for patient set-up, while enhancing lesion visualization and quantitative reliability without altering lesion detection, supporting its clinical value for metastatic prostate cancer imaging.
SN - 3005-074X
ER -