AI-driven body composition monitoring and its prognostic role in mCRPC undergoing lutetium-177 PSMA radioligand therapy: insights from a retrospective single-center analysis.
Authors
Affiliations (6)
Affiliations (6)
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany. [email protected].
- Department of Nuclear Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany.
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany.
- Department of Urology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, 10117, Berlin, Germany.
- Department of Urology, Medical University of Vienna, Vienna, Austria.
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany.
Abstract
Body composition (BC) analysis is performed to quantify the relative amounts of different body tissues as a measure of physical fitness and tumor cachexia. We hypothesized that relative changes in body composition (BC) parameters, assessed by an artificial intelligence-based, PACS-integrated software, between baseline imaging before the start of radioligand therapy (RLT) and interim staging after two RLT cycles could predict overall survival (OS) in patients with metastatic castration-resistant prostate cancer. We conducted a single-center, retrospective analysis of 92 patients with mCRPC undergoing [<sup>177</sup>Lu]Lu-PSMA RLT between September 2015 and December 2023. All patients had [<sup>68</sup> Ga]Ga-PSMA-11 PET/CT at baseline (≤ 6 weeks before the first RLT cycle) and at interim staging (6-8 weeks after the second RLT cycle) allowing for longitudinal BC assessment. During follow-up, 78 patients (85%) died. Median OS was 16.3 months. Median follow-up time in survivors was 25.6 months. The 1 year mortality rate was 32.6% (95%CI 23.0-42.2%) and the 5 year mortality rate was 92.9% (95%CI 85.8-100.0%). In multivariable regression, relative change in visceral adipose tissue (VAT) (HR: 0.26; p = 0.006), previous chemotherapy of any type (HR: 2.4; p = 0.003), the presence of liver metastases (HR: 2.4; p = 0.018) and a higher baseline De Ritis ratio (HR: 1.4; p < 0.001) remained independent predictors of OS. Patients with a higher decrease in VAT (< -20%) had a median OS of 10.2 months versus 18.5 months in patients with a lower VAT decrease or VAT increase (≥ -20%) (log-rank test: p = 0.008). In a separate Cox model, the change in VAT predicted OS (p = 0.005) independent of the best PSA response after 1-2 RLT cycles (p = 0.09), and there was no interaction between the two (p = 0.09). PACS-Integrated, AI-based BC monitoring detects relative changes in the VAT, Which was an independent predictor of shorter OS in our population of patients undergoing RLT.