Semiautomatically Quantified Tumor Volume Using <sup>68</sup>Ga-PSMA-11 PET as a Biomarker for Survival in Patients with Advanced Prostate Cancer
Robert Seifert, Ken Herrmann, Jens Kleesiek, Michael Schäfers, Vijay Shah, Zhoubing Xu, Guillaume Chabin, Saša Grbić, Bruce Spottiswoode, Kambiz Rahbar
Abstract
Prostate-specific membrane antigen (PSMA)–targeting PET imaging is becoming the reference standard for prostate cancer staging, especially in advanced disease. Yet, the implications of PSMA PET–derived whole-body tumor volume for overall survival are poorly elucidated to date. This might be because semiautomated quantification of whole-body tumor volume as a PSMA PET biomarker is an unmet clinical challenge. Therefore, in the present study we propose and evaluate a software that enables the semiautomated quantification of PSMA PET biomarkers such as whole-body tumor volume. <b>Methods:</b> The proposed quantification is implemented as a research prototype. PSMA-accumulating foci were automatically segmented by a percental threshold (50% of local SUV<sub>max</sub>). Neural networks were trained to segment organs in PET/CT acquisitions (training CTs: 8,632, validation CTs: 53). Thereby, PSMA foci within organs of physiologic PSMA uptake were semiautomatically excluded from the analysis. Pretherapeutic PSMA PET/CTs of 40 consecutive patients treated with <sup>177</sup>Lu-PSMA-617 were evaluated in this analysis. The whole-body tumor volume (PSMA<sub>TV50</sub>), SUV<sub>max</sub>, SUV<sub>mean</sub>, and other whole-body imaging biomarkers were calculated for each patient. Semiautomatically derived results were compared with manual readings in a subcohort (by 1 nuclear medicine physician). Additionally, an interobserver evaluation of the semiautomated approach was performed in a subcohort (by 2 nuclear medicine physicians). <b>Results:</b> Manually and semiautomatically derived PSMA metrics were highly correlated (PSMA<sub>TV50</sub>: <i>R</i><sup>2</sup> = 1.000, <i>P</i> < 0.001; SUV<sub>max</sub>: <i>R</i><sup>2</sup> = 0.988, <i>P</i> < 0.001). The interobserver agreement of the semiautomated workflow was also high (PSMA<sub>TV50</sub>: <i>R</i><sup>2</sup> = 1.000, <i>P</i> < 0.001, interclass correlation coefficient = 1.000; SUV<sub>max</sub>: <i>R</i><sup>2</sup> = 0.988, <i>P</i> < 0.001, interclass correlation coefficient = 0.997). PSMA<sub>TV50</sub> (ml) was a significant predictor of overall survival (hazard ratio: 1.004; 95% confidence interval: 1.001–1.006, <i>P</i> = 0.002) and remained so in a multivariate regression including other biomarkers (hazard ratio: 1.004; 95% confidence interval: 1.001–1.006 <i>P</i> = 0.004). <b>Conclusion:</b> PSMA<sub>TV50</sub> is a promising PSMA PET biomarker that is reproducible and easily quantified by the proposed semiautomated software. Moreover, PSMA<sub>TV50</sub> is a significant predictor of overall survival in patients with advanced prostate cancer who receive <sup>177</sup>Lu-PSMA-617 therapy.