Comparison of quantitative whole body PET parameters on [68Ga]Ga-PSMA-11 PET/CT using ordered Subset Expectation Maximization (OSEM) vs. bayesian penalized likelihood (BPL) reconstruction algorithms in men with metastatic castration-resistant prostate cancer.

IF 3.5 2区 医学 Q2 ONCOLOGY
Narjess Ayati, Lachlan McIntosh, James Buteau, Ramin Alipour, Michal Pudis, Nicholas Daw, Price Jackson, Michael S Hofman
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引用次数: 0

Abstract

Background: PSMA PET/CT is a predictive and prognostic biomarker for determining response to [177Lu]Lu-PSMA-617 in patients with metastatic castration resistant prostate cancer (mCRPC). Thresholds defined to date may not be generalizable to newer image reconstruction algorithms. Bayesian penalized likelihood (BPL) reconstruction algorithm is a novel reconstruction algorithm that may improve contrast whilst preventing introduction of image noise. The aim of this study is to compare the quantitative parameters obtained using BPL and the Ordered Subset Expectation Maximization (OSEM) reconstruction algorithms.

Methods: Fifty consecutive patients with mCRPC who underwent [68Ga]Ga-PSMA-11 PET/CT using OSEM reconstruction to assess suitability for [177Lu]Lu-PSMA-617 therapy were selected. BPL algorithm was then used retrospectively to reconstruct the same PET raw data. Quantitative and volumetric measurements such as tumour standardised uptake value (SUV)max, SUVmean and Molecular Tumour Volume (MTV-PSMA) were calculated on both reconstruction methods. Results were compared (Bland-Altman, Pearson correlation coefficient) including subgroups with low and high-volume disease burdens (MTV-PSMA cut-off 40 mL).

Results: The SUVmax and SUVmean were higher, and MTV-PSMA was lower in the BPL reconstructed images compared to the OSEM group, with a mean difference of 8.4 (17.5%), 0.7 (8.2%) and - 21.5 mL (-3.4%), respectively. There was a strong correlation between the calculated SUVmax, SUVmean, and MTV-PSMA values in the OSEM and BPL reconstructed images (Pearson r values of 0.98, 0.99, and 1.0, respectively). No patients were reclassified from low to high volume disease or vice versa when switching from OSEM to BPL reconstruction.

Conclusions: [68Ga]Ga-PSMA-11 PET/CT quantitative and volumetric parameters produced by BPL and OSEM reconstruction methods are strongly correlated. Differences are proportional and small for SUVmean, which is used as a predictive biomarker. Our study suggests that both reconstruction methods are acceptable without clinical impact on quantitative or volumetric findings. For longitudinal comparison, committing to the same reconstruction method would be preferred to ensure consistency.

使用有序子集期望最大化(OSEM)与贝叶斯惩罚似然(BPL)重建算法比较[68Ga]Ga-PSMA-11 PET/CT 对转移性抗性前列腺癌男性患者的全身 PET 定量参数。
背景:PSMA PET/CT 是一种预测和预后的生物标志物,用于确定转移性阉割抵抗性前列腺癌 (mCRPC) 患者对 [177Lu]Lu-PSMA-617 的反应。迄今为止定义的阈值可能无法适用于较新的图像重建算法。贝叶斯惩罚似然(BPL)重建算法是一种新型重建算法,它可以提高对比度,同时防止引入图像噪声。本研究的目的是比较使用贝叶斯惩罚似然(BPL)重建算法和有序子集期望最大化(OSEM)重建算法获得的定量参数:方法:选取连续接受[68Ga]Ga-PSMA-11 PET/CT检查的50名mCRPC患者,采用OSEM重建评估其是否适合接受[177Lu]Lu-PSMA-617治疗。然后使用 BPL 算法回顾性地重建相同的 PET 原始数据。两种重建方法都计算了定量和体积测量值,如肿瘤标准化摄取值(SUV)max、SUVmean 和分子肿瘤体积(MTV-PSMA)。对结果进行比较(Bland-Altman、皮尔逊相关系数),包括低体积和高体积疾病负担亚组(MTV-PSMA 临界值为 40 mL):与 OSEM 组相比,BPL 重建图像的 SUVmax 和 SUVmean 更高,MTV-PSMA 更低,平均差异分别为 8.4(17.5%)、0.7(8.2%)和 - 21.5 mL(-3.4%)。在 OSEM 和 BPL 重建图像中计算出的 SUVmax、SUVmean 和 MTV-PSMA 值之间存在很强的相关性(Pearson r 值分别为 0.98、0.99 和 1.0)。从OSEM到BPL重建时,没有患者从低体积疾病重新分类为高体积疾病,也没有患者从高体积疾病重新分类为低体积疾病:结论:BPL 和 OSEM 重建方法产生的[68Ga]Ga-PSMA-11 PET/CT 定量和容积参数密切相关。对于作为预测性生物标志物的 SUVmean 而言,两者之间的差异是成比例的,且差异较小。我们的研究表明,这两种重建方法都是可以接受的,不会对定量或容积结果产生临床影响。在进行纵向比较时,最好采用相同的重建方法,以确保一致性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cancer Imaging
Cancer Imaging ONCOLOGY-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
7.00
自引率
0.00%
发文量
66
审稿时长
>12 weeks
期刊介绍: Cancer Imaging is an open access, peer-reviewed journal publishing original articles, reviews and editorials written by expert international radiologists working in oncology. The journal encompasses CT, MR, PET, ultrasound, radionuclide and multimodal imaging in all kinds of malignant tumours, plus new developments, techniques and innovations. Topics of interest include: Breast Imaging Chest Complications of treatment Ear, Nose & Throat Gastrointestinal Hepatobiliary & Pancreatic Imaging biomarkers Interventional Lymphoma Measurement of tumour response Molecular functional imaging Musculoskeletal Neuro oncology Nuclear Medicine Paediatric.
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