‘One button push’ fully automated PSMA PET quantification: Correlation with progression free and overall survival in patients undergoing [ <sup>177</sup> Lu] Lu PSMA therapy for metastatic castrate resistant prostate cancer.
Abstract
5054 Background: [ 177 Lu]Lu-PSMA is an effective treatment in metastatic castrate-resistant prostate cancer (mCRPC). Whole body standardized uptake value (SUV)mean and total tumor volume (PSMA-TTV) are valuable screening biomarkers for 177 Lu-PSMA therapy but require labour intensive semi-quantitative software. This study aims to compare PSMA SUVmean, and PSMA-TTV from fully automated and semi-automated methods of PSMA-PET quantification for predictive and prognostic capability. Methods: Datasets of participants (pts) from ethics approved trials with mCRPC post androgen receptor signaling inhibition and post taxane (or unfit for taxane), treated with [ 177 Lu]Lu-PSMA with a prior screening 68 Ga-PSMA-11 PET/CT, and outcome data including PSA progression-free (PSA-PFS) and overall survival (OS) were included. Screening 68 Ga-PSMA-11 PET/CT of participants were quantified using MIM LesionID Pro to derive SUVmean and PSMA-TTV with a fully automated quantification process (Method A) and semi-automated quantification adjusted manually for error (Method B). Both methods utilised software that segmented all lesions above SUVmax 3 and a CT-based deep learning method to identify normal organs for automatic physiological uptake removal. SUVmean and PSMA-TTV were evaluated in quartiles. Associations between SUVmean and PSMA-TTV above and below the 75 th percentile (Q4 vs Q1-3) were examined with Kaplan Meier estimates and log-rank tests. Results: Data from 139 pts were analysed, median age 72 years (IQR: 67–77) and median PSA 94 ng/ml (IQR: 34–325). The median time to PSA-PFS (120 events) 5.5 months (95%CI:4–6.0) and OS (82 events) 13.5 months (95%CI:11– 18). With method A (fully automated), SUVmean Q4 was 9.7 and PSMA -TTV Q4 was 1156ml. The corresponding results with method B (manually adjusted) were SUVmean Q4 9.9 and PSMA-TTV Q4 1203ml. Withmethod A, median PSA-PFS for SUVmean Q1-3 was 4.5 (95%CI:3–6) vs 7 months (mo) (95%CI:5–11) for SUVmean Q4 (p=0.003). Median OS for SUVmean Q1-3 was 12.0 (95%CI:10–6) vs 20 mo (95%CI:12.0–NE) for SUVmean Q4 (p=0.011). For PSMA-TTV Q4 vs Q1-3, median OS was 8.5 (95%CI:7 –12.0) vs 18 mo (95%CI: 13–20) (p<0.001). With method B, median PSA-PFS for SUVmean Q1-3 was 4.5 (95%CI:3– 6) vs 7.5 mo (95%CI:5–11) for SUVmean Q4 (p=0.002). Median OS for SUVmean Q1-3 was 13 (95%CI:10–17) vs 20 mo (95%CI:11 – NE) for SUVmean Q4 (p=0.03). For PSMA-TTV Q4 vs Q1-3, median OS was 8.5 (95%CI:7–12) vs 18 mo (95%CI:13 – 20) (p<0.001). Conclusions: PSMA SUVmean and PSMA-TTV with a fully automated quantification method predicted both PSA-PFS and OS in patients undergoing [ 177 Lu]Lu-PSMA therapy. Fully automated vs manually adjusted predictive capability was not different. This is an important step in moving PSMA-PET quantitative biomarkers from research tool to routine clinical care.
Article Details
Journal Info
Journal of Clinical Oncology
Lippincott Williams & Wilkins
Authors (15)
Louise Emmett
Alex Zheng
Charlie Jin
University of New South Wales, Sydney, Australia
Nathan Papa
Garvin Institute of Medical Research, Sydney, Australia
Sobia Khan
St Vincent's Hospital Sydney, Sydney, Australia
Narjess Ayati
St Vincent's Hospital Sydney, Sydney, Australia
Timothy Susman
Mim Software Inc., Beachwood, OH
Ken Ngai
MIM Software, Beachwood, OH
Aaron Nelson
MIM Software Inc., Cleveland, OH
Shikha Sharma
Rahul Anand
Nikeith John
St Vincent's Hospital Sydney, Sydney, Australia
Megan Crumbaker
Kinghorn Cancer Centre, Sydney, NSW, Australia
Andrew Nguyen
Ken Herrmann