Automatic Lesion Segmentation in ⁶⁸Ga-PSMA PET/CT and ¹⁷⁷Lu-PSMA SPECT/CT: Added Value of PET-Guided SPECT in a Bicentric Study

Background 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. Performance was primarily assessed using the Dice Similarity Coefficient (DSC). Results For PET/CT segmentation, M1 and M3 achieved comparable performance. M1 reached DSCs of 0.82 ± 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 provided the best results, with DSCs of 0.63 ± 0.29 (internal) and 0.78 ± 0.14 (external). Compared with the SPECT-only model M2 (0.61 ± 0.25 internal; 0.70 ± 0.25 external), M4 also showed a tighter dispersion of DSC values, indicating greater robustness and consistency (p = 0.014 in external validation). Conclusion nnU-Net 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 substantially improves 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 management.