Automatic Bone Marrow Segmentation for Precise [Lu]Lu-PSMA-617 Dosimetry

Background: Bone marrow (BM) is the dominant dose-limiting organ in [Lu]Lu-PSMA-617 therapy for patients with metastasized castration resistant prostate cancer, where BM dosimetry is challenging due to segmentation.

Purpose: We aim to develop an automatic image-based segmentation method on peri-therapeutic sequential [Lu]Lu-PSMA-617 images for personalized BM dosimetry.

Methods: Quantitative SPECT/CT imaging at 2, 20, 40 and 60 (n = 14)/200 (n = 16) h post [Lu]Lu-PSMA-617 injection were analyzed for 10 patients with 30 treatment cycles. X-means clustering was applied on the deep learning-based segmented lumbar spines CT images to classify the BM region. A single threshold method, two empirical segmentation methods (one sphere and five spheres), and gold standard manual segmentation were also implemented. The Dice similarity coefficient between BM masks of the X-means clustering and single threshold method was calculated as compared to the gold standard. BM mean absorbed dose (D) was obtained for different segmentation methods. Absolute errors and Bland-Altman analysis were also evaluated for BM D derived from evaluated segmentation methods compared with the gold standard. Wilcoxon signed-rank test was performed for statistical evaluation. BM D was correlated with the change of platelets and white blood cells (WBC) pre- and post-treatment using Pearson correlation analysis.

Results: In 30 cycles of 10 patients, the average Dice is 0.76 ± 0.18 for the X-means clustering method, as compared to 0.61 ± 0.19 for the single threshold method. The gold standard yields mean BM D of 0.46 ± 0.69 Gy. The X-means clustering method exhibits significantly (p < 0.01) lower mean absolute BM D (25.34 ± 64.48%) errors, followed by the single threshold (32.46 ± 69.49%), one sphere (50.53 ± 35.40%), and five spheres (72.73 ± 115.97%) methods. Bland-Altman analysis reveals that the X-means clustering method has a smaller D difference (0.0330 Gy) compared to the single threshold (0.0512 Gy), one sphere (-0.1903 Gy), and five spheres (0.2108 Gy) methods. Stronger correlations (r ≤ -0.65) are found between platelets/WBC changes and BM D from both the gold standard and X-means clustering methods than other methods.

Conclusions: X-means clustering is feasible to segment the BM based on the CT images of peri-therapy SPECT/CT and shows advantages compared with the single threshold and empirical sphere segmentation methods.