Quantification of normal bone and osseous metastases in castration-resistant prostate cancer using SPECT/CT with xSPECT Quant: prospective imaging sub-study of a phase 2 clinical trial investigating the combination of pembrolizumab plus radium-223 compared to radium-223 alone.

IF 9.7 1区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Radiologia Medica Pub Date : 2024-11-20 DOI:10.1007/s11547-024-01931-7
Matthew S Robertson, Yating Wang, SuChun Cheng, Hyesun Park, Shahar Glomski, Lauren C Harshman, Amanda Pace, Jacqueline Kilar, Meredith Flynn, Lauren Gilbert, Atish D Choudhury, Heather Jacene
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Abstract

Objective: The purpose of this study is to demonstrate the consistency and reproducibility of quantitative SPECT/CT by evaluating the maximum SUV (SUVmax) in normal bone, to provide the reference value of metastatic lesions, and to evaluate the clinical implication of SUVmax changes of osseous metastasis during treatment.

Material and methods: This prospective imaging sub-study was performed as part of a phase 2 clinical trial of patients with metastatic castration-resistant prostate cancer (mCRPC) randomized to the combination of pembrolizumab plus radium-223 or to radium-223 alone (NCT03093428). The maximum standardized uptake value (SUVmax) and mean Hounsfield Unit (HUmean) of normal bone as well as metastases were measured using a 1.5 cm region of interest (ROI) on CT and xSPECT Quant reconstruction on the baseline study (S0) and restaging scans. The most tracer-avid metastatic lesion in each patient on S0 was selected as a target lesion, and changes of SUVmax and HUmean of the target lesion were compared on the first restaging scan (S1). Correlations between the percentage changes of SUVmax of the target lesion with alkaline phosphatase (ALP) and prostate-specific antigen (PSA) were assessed.

Results: Twenty-one patients were enrolled on the imaging sub-study of which 15 had paired baseline S0 and S1 data. On S0, the median SUVmax and HUmean of normal bone was 5.85 g/mL (0.42-14.98) and 133.03 (range, 28.47-461.91), respectively. The median SUVmax and HUmean of metastasis were 42.2 g/mL (range, 17.96-143.36) and 549.58 (177.87-1107.64), respectively. There was significant reduction in SUVmax (- 40.1%, range - 86.2 to + 23.5%), p < 0.001) and increase in HUmean (+ 8.3%, range - 11.3 to + 61.7%, p = 0.0479, Wilcoxon signed-rank test) of target lesions between S0 and S1. Spearman correlation between the percentage changes of SUVmax of a target lesion and both serum PSA (r = 0.33, p = 0.226) and ALP (r = 0.45, p = 0.094) were not statistically significant.

Conclusion: Quantitative SPECT/CT provides consistent and objective imaging parameters, which can help monitor tumor burden. The median SUVmax of metastasis at baseline was roughly 7.2-fold higher than normal bone. Quantitative SPECT/CT may help visualize the early osteoblastic treatment response in prostate cancer patients treated with radium-223 alone or combined with pembrolizumab.

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使用xSPECT Quant的SPECT/CT对阉割耐药前列腺癌的正常骨和骨转移灶进行定量:研究pembrolizumab和镭-223联合治疗与单独使用镭-223相比的2期临床试验的前瞻性成像子研究。
研究目的本研究旨在通过评估正常骨的最大SUV(SUVmax)来证明定量SPECT/CT的一致性和可重复性,为转移病灶提供参考值,并评估治疗期间骨转移灶SUVmax变化的临床意义:这项前瞻性成像子研究是转移性耐阉割前列腺癌(mCRPC)患者随机接受pembrolizumab加镭-223联合治疗或单独接受镭-223治疗(NCT03093428)的2期临床试验的一部分。在基线研究(S0)和重新分期扫描的 CT 和 xSPECT 定量重建中,使用 1.5 厘米感兴趣区(ROI)测量正常骨骼和转移灶的最大标准化摄取值(SUVmax)和平均 Hounsfield 单位(HUmean)。每位患者在 S0 扫描时选择示踪剂拮抗程度最高的转移病灶作为靶病灶,在第一次重新分期扫描(S1)时比较靶病灶的 SUVmax 和 HUmean 变化。评估目标病灶的 SUVmax 百分比变化与碱性磷酸酶(ALP)和前列腺特异性抗原(PSA)之间的相关性:21名患者参加了成像子研究,其中15名患者的S0和S1基线数据成对。在 S0 阶段,正常骨骼的中位 SUVmax 和 HUmean 分别为 5.85 g/mL(0.42-14.98)和 133.03(范围为 28.47-461.91)。转移瘤的中位 SUVmax 和 HUmean 分别为 42.2 g/mL(范围为 17.96-143.36)和 549.58(177.87-1107.64)。在S0和S1之间,靶病灶的SUVmax(- 40.1%,范围- 86.2至+ 23.5%)和P平均值(+ 8.3%,范围- 11.3至+ 61.7%,P = 0.0479,Wilcoxon符号秩检验)明显下降。靶病灶 SUVmax 百分比变化与血清 PSA(r = 0.33,p = 0.226)和 ALP(r = 0.45,p = 0.094)之间的 Spearman 相关性无统计学意义:定量 SPECT/CT 可提供一致、客观的成像参数,有助于监测肿瘤负荷。基线时转移瘤的中位 SUVmax 比正常骨高约 7.2 倍。定量SPECT/CT有助于观察单独使用镭-223或联合使用彭博利珠单抗治疗的前列腺癌患者的早期成骨治疗反应。
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来源期刊
Radiologia Medica
Radiologia Medica 医学-核医学
CiteScore
14.10
自引率
7.90%
发文量
133
审稿时长
4-8 weeks
期刊介绍: Felice Perussia founded La radiologia medica in 1914. It is a peer-reviewed journal and serves as the official journal of the Italian Society of Medical and Interventional Radiology (SIRM). The primary purpose of the journal is to disseminate information related to Radiology, especially advancements in diagnostic imaging and related disciplines. La radiologia medica welcomes original research on both fundamental and clinical aspects of modern radiology, with a particular focus on diagnostic and interventional imaging techniques. It also covers topics such as radiotherapy, nuclear medicine, radiobiology, health physics, and artificial intelligence in the context of clinical implications. The journal includes various types of contributions such as original articles, review articles, editorials, short reports, and letters to the editor. With an esteemed Editorial Board and a selection of insightful reports, the journal is an indispensable resource for radiologists and professionals in related fields. Ultimately, La radiologia medica aims to serve as a platform for international collaboration and knowledge sharing within the radiological community.
期刊最新文献
Quantification of normal bone and osseous metastases in castration-resistant prostate cancer using SPECT/CT with xSPECT Quant: prospective imaging sub-study of a phase 2 clinical trial investigating the combination of pembrolizumab plus radium-223 compared to radium-223 alone. Enhancing detection of high-level axillary lymph node metastasis after neoadjuvant therapy in breast cancer patients with nodal involvement: a combined approach of axilla ultrasound and breast elastography. Long-term clinical outcomes of thermal ablation for benign thyroid nodules and unresolved issues: a comprehensive systematic review. Correction: Diagnostic accuracy of MRI for evaluating myometrial invasion in endometrial cancer: a comparison of MUSE-DWI, rFOV-DWI, and DCE-MRI. Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters.
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