AI-driven body composition monitoring and its prognostic role in mCRPC undergoing lutetium-177 PSMA radioligand therapy: insights from a retrospective single-center analysis.

IF 3.1 3区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Tristan Ruhwedel, Julian Rogasch, Markus Galler, Imke Schatka, Christoph Wetz, Christian Furth, Nadine Biernath, Maria De Santis, Seyd Shnayien, Johannes Kolck, Dominik Geisel, Holger Amthauer, Nick Lasse Beetz
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引用次数: 0

Abstract

Background: 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.

Methods: We conducted a single-center, retrospective analysis of 92 patients with mCRPC undergoing [177Lu]Lu-PSMA RLT between September 2015 and December 2023. All patients had [68 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.

Results: 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).

Conclusions: 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.

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人工智能驱动的体成分监测及其在接受镥-177 PSMA放射配体治疗的mCRPC中的预后作用:来自回顾性单中心分析的见解
背景:进行体组成(BC)分析,以量化不同身体组织的相对数量,作为身体健康和肿瘤恶病质的衡量标准。我们假设,在放射配体治疗(RLT)开始前的基线成像和两个RLT周期后的中期分期之间,基于人工智能的pacs集成软件评估的体成分(BC)参数的相对变化可以预测转移性去势抵抗性前列腺癌患者的总生存期(OS)。方法:我们对2015年9月至2023年12月期间接受[177Lu]Lu-PSMA RLT治疗的92例mCRPC患者进行了单中心回顾性分析。所有患者在基线(第一个RLT周期前≤6周)和中期分期(第二个RLT周期后6-8周)进行了[68 Ga]Ga- psma -11 PET/CT检查,以便进行纵向BC评估。结果:随访期间死亡78例(85%)。中位OS为16.3个月。幸存者的中位随访时间为25.6个月。1年死亡率为32.6% (95%CI 23.0 ~ 42.2%), 5年死亡率为92.9% (95%CI 85.8 ~ 100.0%)。在多变量回归中,内脏脂肪组织(VAT)的相对变化(HR: 0.26, p = 0.006)、既往任何类型的化疗(HR: 2.4, p = 0.003)、肝转移的存在(HR: 2.4, p = 0.018)和较高的基线De - Ritis比率(HR: 1.4; p)得出结论:pacs集成、基于人工智能的BC监测检测到VAT的相对变化,这是我们接受RLT患者中较短生存期的独立预测因子。
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来源期刊
EJNMMI Research
EJNMMI Research RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING&nb-
CiteScore
5.90
自引率
3.10%
发文量
72
审稿时长
13 weeks
期刊介绍: EJNMMI Research publishes new basic, translational and clinical research in the field of nuclear medicine and molecular imaging. Regular features include original research articles, rapid communication of preliminary data on innovative research, interesting case reports, editorials, and letters to the editor. Educational articles on basic sciences, fundamental aspects and controversy related to pre-clinical and clinical research or ethical aspects of research are also welcome. Timely reviews provide updates on current applications, issues in imaging research and translational aspects of nuclear medicine and molecular imaging technologies. The main emphasis is placed on the development of targeted imaging with radiopharmaceuticals within the broader context of molecular probes to enhance understanding and characterisation of the complex biological processes underlying disease and to develop, test and guide new treatment modalities, including radionuclide therapy.
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