PT - JOURNAL ARTICLE AU - Robert Seifert AU - Ken Herrmann AU - Jens Kleesiek AU - Michael Schäfers AU - Vijay Shah AU - Zhoubing Xu AU - Guillaume Chabin AU - Sasa Grbic AU - Bruce Spottiswoode AU - Kambiz Rahbar TI - Semiautomatically Quantified Tumor Volume Using <sup>68</sup>Ga-PSMA-11 PET as a Biomarker for Survival in Patients with Advanced Prostate Cancer AID - 10.2967/jnumed.120.242057 DP - 2020 Dec 01 TA - Journal of Nuclear Medicine PG - 1786--1792 VI - 61 IP - 12 4099 - http://jnm.snmjournals.org/content/61/12/1786.short 4100 - http://jnm.snmjournals.org/content/61/12/1786.full SO - J Nucl Med2020 Dec 01; 61 AB - 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. Methods: The proposed quantification is implemented as a research prototype. PSMA-accumulating foci were automatically segmented by a percental threshold (50% of local SUVmax). 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 177Lu-PSMA-617 were evaluated in this analysis. The whole-body tumor volume (PSMATV50), SUVmax, SUVmean, 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). Results: Manually and semiautomatically derived PSMA metrics were highly correlated (PSMATV50: R2 = 1.000, P &lt; 0.001; SUVmax: R2 = 0.988, P &lt; 0.001). The interobserver agreement of the semiautomated workflow was also high (PSMATV50: R2 = 1.000, P &lt; 0.001, interclass correlation coefficient = 1.000; SUVmax: R2 = 0.988, P &lt; 0.001, interclass correlation coefficient = 0.997). PSMATV50 (ml) was a significant predictor of overall survival (hazard ratio: 1.004; 95% confidence interval: 1.001–1.006, P = 0.002) and remained so in a multivariate regression including other biomarkers (hazard ratio: 1.004; 95% confidence interval: 1.001–1.006 P = 0.004). Conclusion: PSMATV50 is a promising PSMA PET biomarker that is reproducible and easily quantified by the proposed semiautomated software. Moreover, PSMATV50 is a significant predictor of overall survival in patients with advanced prostate cancer who receive 177Lu-PSMA-617 therapy.