Authors
Solène Perret, Léa Albe, Agathe Edet-Sanson, David Tonnelet, Thomas Carlier, Camilla Kohi, Matthieu Barbaud, Romain Modzelewski, Pierre Vera, Laetitia Augusto, Frédéric Di Fiore, Sébastien Hapdey, Clément Bailly, Arnaud Dieudonné, Pierre Decazes
Published in
EJNMMI research. Jul 01, 2026. Epub Jul 01, 2026.
Abstract
Manual segmentation of prostate cancer metastases on PSMA PET/CT and SPECT/CT is time-consuming and poorly scalable, particularly in highly metastatic patients. This study evaluated nnU-Net-based automatic segmentation models trained on PET and SPECT either separately or jointly and assessed whether PET-derived information can improve SPECT lesion segmentation. Seventy-three patients with metastatic castration-resistant prostate cancer treated with ¹⁷⁷Lu-PSMA were retrospectively included: 48 from the Henri Becquerel Cancer Center (HBCC) and 25 from Nantes University Hospital (NUH). For each patient, ⁶⁸Ga-PSMA PET/CT and ¹⁷⁷Lu-PSMA SPECT/CT were acquired before and during the first treatment cycle respectively. All images were manually segmented by four nuclear medicine physicians in consensus. Four nnU-Net models were trained: M1 (PET/CT only), M2 (SPECT/CT only), M3 (joint PET/CT+SPECT/CT, unimodal input at inference), and M4 (SPECT/CT with PET/CT segmentation as a priori input). Models were first trained and internally validated on HBCC data, then retrained on the full HBCC cohort and externally validated on NUH data.
For PET/CT segmentation, M1 and M3 achieved comparable performance. M1 reached DSCs of 0.83 ± 0.19 (internal) and 0.76 ± 0.22 (external), while M3 achieved 0.83 ± 0.16 (internal) and 0.77 ± 0.21 (external). For SPECT/CT, the PET-guided model M4 (DSC: 0.63 ± 0.24 internal; 0.78 ± 0.14 external; PPV: 0.65 ± 0.26 internal; 0.75 ± 0.23 external) provided the best results. Compared with the SPECT-only model M2 (DSC: 0.61 ± 0.26 internal; 0.70 ± 0.25 external; PPV: 0.63 ± 0.25 internal; 0.71 ± 0.24 external), M4 showed no statistically significant difference in internal validation (DSC p = 0.35), while being statistically significant in external validation (DSC p = 0.014).
The nnU-Net framework enables accurate lesion segmentation on both ⁶⁸Ga-PSMA PET/CT and ¹⁷⁷Lu-PSMA SPECT/CT. While PET-only and joint PET+SPECT models perform similarly on PET images, incorporating PET-derived segmentations as prior information tends to improve SPECT/CT lesion segmentation. This PET-guided SPECT segmentation strategy leverages the higher spatial resolution of PET and represents a key step towards fully automated extraction of volumetric and dosimetric biomarkers for personalized prostate cancer treatment.
PMID:
42387196
Bibliographic data and abstract were imported from PubMed on 02 Jul 2026.
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