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, 60 (9), 1277-1283

qPSMA: Semiautomatic Software for Whole-Body Tumor Burden Assessment in Prostate Cancer Using 68 Ga-PSMA11 PET/CT

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qPSMA: Semiautomatic Software for Whole-Body Tumor Burden Assessment in Prostate Cancer Using 68 Ga-PSMA11 PET/CT

Andrei Gafita et al. J Nucl Med.

Abstract

Our aim was to introduce and validate qPSMA, a semiautomatic software package for whole-body tumor burden assessment in prostate cancer patients using 68Ga-prostate-specific membrane antigen (PSMA) 11 PET/CT. Methods: qPSMA reads hybrid PET/CT images in DICOM format. Its pipeline was written using Python and C++ languages. A bone mask based on CT and a normal-uptake mask including organs with physiologic 68Ga-PSMA11 uptake are automatically computed. An SUV threshold of 3 and a liver-based threshold are used to segment bone and soft-tissue lesions, respectively. Manual corrections can be applied using different tools. Multiple output parameters are computed, that is, PSMA ligand-positive tumor volume (PSMA-TV), PSMA ligand-positive total lesion (PSMA-TL), PSMA SUVmean, and PSMA SUVmax Twenty 68Ga-PSMA11 PET/CT data sets were used to validate and evaluate the performance characteristics of qPSMA. Four analyses were performed: validation of the semiautomatic algorithm for liver background activity determination, assessment of intra- and interobserver variability, validation of data from qPSMA by comparison with Syngo.via, and assessment of computational time and comparison of PSMA PET-derived parameters with serum prostate-specific antigen. Results: Automatic liver background calculation resulted in a mean relative difference of 0.74% (intraclass correlation coefficient [ICC], 0.996; 95%CI, 0.989;0.998) compared with METAVOL. Intra- and interobserver variability analyses showed high agreement (all ICCs > 0.990). Quantitative output parameters were compared for 68 lesions. Paired t testing showed no significant differences between the values obtained with the 2 software packages. The ICC estimates obtained for PSMA-TV, PSMA-TL, SUVmean, and SUVmax were 1.000 (95%CI, 1.000;1.000), 1.000 (95%CI, 1.000;1.000), 0.995 (95%CI, 0.992;0.997), and 0.999 (95%CI, 0.999;1.000), respectively. The first and second reads for intraobserver variability resulted in mean computational times of 13.63 min (range, 8.22-25.45 min) and 9.27 min (range, 8.10-12.15 min), respectively (P = 0.001). Highly significant correlations were found between serum prostate-specific antigen value and both PSMA-TV (r = 0.72, P < 0.001) and PSMA-TL (r = 0.66, P = 0.002). Conclusion: Semiautomatic analyses of whole-body tumor burden in 68Ga-PSMA11 PET/CT is feasible. qPSMA is a robust software package that can help physicians quantify tumor load in heavily metastasized prostate cancer patients.

Keywords: PET/CT; PSMA; qPSMA; tumor segmentation.

Figures

FIGURE 1.
FIGURE 1.
The 6-step workflow of qPSMA. First, bone mask (A) and normal-uptake mask (B) are automatically computed. Then, SUVthr_st is semiautomatically computed from liver background activity (C). Bone lesions are segmented using SUVthr_bone (D), whereas soft-tissue lesions are segmented using SUVthr_st, previously calculated at third step (E). Finally, output parameters are obtained by performing general statistics (F).
FIGURE 2.
FIGURE 2.
Examples of manual corrections in 2 metastatic castration-resistant prostate cancer patients. (A) Because of their large connections with intestine, retroperitoneal lymph nodes were wrongly classified as having normal uptake and not considered when SUVthr_st was applied. After correction of normal-uptake label, lymph nodes were segmented as soft-tissue lesions. (B) Ureter segmented as soft-tissue lesions and manually changed to normal-uptake label.
FIGURE 3.
FIGURE 3.
Bland–Altman plot of qPSMA and METAVOL agreement on semiautomatic computation of SUVthr_st. Solid line indicates average mean difference, and dotted lines delineate 95% limits of agreement (mean ± 1.96 × SD). No systematic difference between the 2 software programs was found.
FIGURE 4.
FIGURE 4.
Bland–Altman plots for tumor volume (A), total lesion (B), SUVmean (C), and SUVmax (D) from 68 lesions segmented with qPSMA and Syngo.via software. Solid lines indicate average mean difference, and dotted lines delineate 95% limits of agreement (mean ± 1.96 × SD).

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