RT Journal Article
SR Electronic
T1 Semi-automatically quantified tumor volume using Ga-68-PSMA-11-PET as biomarker for survival in patients with advanced prostate cancer
JF Journal of Nuclear Medicine
JO J Nucl Med
FD Society of Nuclear Medicine
SP jnumed.120.242057
DO 10.2967/jnumed.120.242057
A1 Seifert, Robert
A1 Herrmann, Ken
A1 Kleesiek, Jens
A1 Schafers, Michael A.
A1 Shah, Vijay
A1 Xu, Zhoubing
A1 Chabin, Guillaume
A1 Garbic, Sasa
A1 Spottiswoode, Bruce
A1 Rahbar, Kambiz
YR 2020
UL http://jnm.snmjournals.org/content/early/2020/04/23/jnumed.120.242057.abstract
AB Prostate specific membrane antigen (PSMA) targeting Positron Emission Tomography (PET) imaging is becoming the reference standard for prostate cancer (PC) 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 due to the fact that (semi-) automated quantification of whole-body tumor volume as PSMA-PET biomarker is an unmet clinical challenge. Therefore, a novel semi-automated software is proposed and evaluated by the present study, which enables the semi-automated quantification of PSMA-PET biomarkers such as whole-body tumor volume. Methods: The proposed quantification is implemented as a research prototype (MI Whole Body Analysis Suite, v1.0, Siemens Medical Solutions USA, Inc., Knoxville, TN). 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 semi-automatically excluded from the analysis. Pretherapeutic PSMA-PET-CTs of 40 consecutive patients treated with 177Lu-PSMA-617 therapy were evaluated in this analysis. The volumetric whole-body tumor volume (PSMATV50), SUVmax, SUVmean and other whole-body imaging biomarkers were calculated for each patient. Semi-automatically derived results were compared with manual readings in a sub-cohort (by one nuclear medicine physician using syngo.MM Oncology software, Siemens Healthineers, Knoxville, TN). Additionally, an inter-observer evaluation of the semi-automated approach was performed in a sub-cohort (by two nuclear medicine physicians). Results: Manually and semi automatically derived PSMA metrics were highly correlated (PSMATV50: R2=1.000; p<0.001; SUVmax: R2=0.988; p<0.001). The inter-observer agreement of the semi-automated workflow was also high (PSMATV50: R2=1.000; p<0.001; ICC=1.000; SUVmax: R2=0.988; p<0.001; ICC=0.997). PSMATV50 [ml] was a significant predictor of overall survival (HR: 1.004; 95%CI: 1.001-1.006, P = 0.002) and remained so in a multivariate regression including other biomarkers (HR: 1.004; 95%CI: 1.001-1.006 P = 0.004). Conclusion: PSMATV50 is a promising PSMA-PET biomarker that is reproducible and easily quantified by the proposed semi-automated software. Moreover, PSMATV50 is a significant predictor of overall survival in patients with advanced prostate cancer that receive 177Lu-PSMA-617 therapy.