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基于梯度的分割在测量18F-PSMA-1007 PET/CT上受摄取时间影响的前列腺癌代谢参数时的可靠性。

Reliability of gradient-based segmentation for measuring metabolic parameters influenced by uptake time on 18F-PSMA-1007 PET/CT for prostate cancer.

作者信息

Lau Yu Ching, Chen Sirong, Ho Chi Lai, Cai Jing

机构信息

Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China.

Department of Nuclear Medicine and Positron Emission Tomography, Hong Kong Sanatorium and Hospital, Hong Kong, Hong Kong SAR, China.

出版信息

Front Oncol. 2022 Sep 29;12:897700. doi: 10.3389/fonc.2022.897700. eCollection 2022.

DOI:10.3389/fonc.2022.897700
PMID:36249043
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9559596/
Abstract

PURPOSE

To determine an optimal setting for functional contouring and quantification of prostate cancer lesions with minimal variation by evaluating metabolic parameters on 18F-PSMA-1007 PET/CT measured by threshold-based and gradient-based methods under the influence of varying uptake time.

METHODS AND MATERIALS

Dual time point PET/CT was chosen to mimic varying uptake time in clinical setting. Positive lesions of patients who presented with newly diagnosed disease or biochemical recurrence after total prostatectomy were reviewed retrospectively. Gradient-based and threshold-based tools at 40%, 50% and 60% of lesion SUVmax (MIM 6.9) were used to create contours on PET. Contouring was considered completed if the target lesion, with its hottest voxel, was delineated from background tissues and nearby lesions under criteria specific to their operations. The changes in functional tumour volume (FTV) and metabolic tumour burden (MTB, defined as the product of SUVmean and FTV) were analysed. Lesion uptake patterns (increase/decrease/stable) were determined by the percentage change in tumour SUVmax at ±10% limit.

RESULTS

A total of 275 lesions (135 intra-prostatic lesions, 65 lymph nodes, 45 bone lesions and 30 soft tissue lesions in pelvic region) in 68 patients were included. Mean uptake time of early and delayed imaging were 94 and 144 minutes respectively. Threshold-based method using 40% to 60% delineated only 85 (31%), 110 (40%) and 137 (50%) of lesions which all were contoured by gradient-based method. Although the overall percentage change using threshold at 50% was the smallest among other threshold levels in FTV measurement, it was still larger than gradient-based method (median: 50%=-7.6% vs gradient=0%). The overall percentage increase in MTB of gradient-based method (median: 6.3%) was compatible with the increase in tumour SUVmax. Only a small proportion of intra-prostatic lesions (<2%), LN (<4%), bone lesions (0%) and soft tissue lesions (<4%) demonstrated decrease uptake patterns.

CONCLUSIONS

With a high completion rate, gradient-based method is reliable for prostate cancer lesion contouring on 18F-PSMA-1007 PET/CT. Under the influence of varying uptake time, it has smaller variation than threshold-based method for measuring volumetric parameters. Therefore, gradient-based method is recommended for tumour delineation and quantification on 18F-PSMA-1007 PET/CT.

摘要

目的

通过评估在不同摄取时间影响下,基于阈值法和基于梯度法测量的18F-PSMA-1007 PET/CT代谢参数,确定前列腺癌病灶功能轮廓勾画和定量分析的最佳设置,使变化最小。

方法与材料

选择双时相PET/CT以模拟临床环境中的不同摄取时间。对前列腺癌根治术后新诊断疾病或生化复发患者的阳性病灶进行回顾性分析。使用基于病灶SUVmax(MIM 6.9)的40%、50%和60%的基于梯度和基于阈值的工具在PET上创建轮廓。如果根据其操作的特定标准,目标病灶及其最热体素与背景组织和附近病灶区分开来,则认为轮廓勾画完成。分析功能肿瘤体积(FTV)和代谢肿瘤负荷(MTB,定义为SUVmean与FTV的乘积)的变化。通过肿瘤SUVmax在±10%范围内的百分比变化确定病灶摄取模式(增加/减少/稳定)。

结果

共纳入68例患者的275个病灶(135个前列腺内病灶、65个淋巴结、45个骨病灶和30个盆腔软组织病灶)。早期和延迟成像的平均摄取时间分别为94分钟和144分钟。基于阈值的方法使用40%至60%仅勾勒出85个(31%)、110个(40%)和137个(50%)病灶,而这些病灶均通过基于梯度的方法勾勒。尽管在FTV测量中,50%阈值的总体百分比变化在其他阈值水平中最小,但仍大于基于梯度的方法(中位数:50%=-7.6% vs 梯度法=0%)。基于梯度的方法MTB的总体百分比增加(中位数:6.3%)与肿瘤SUVmax的增加相符。只有一小部分前列腺内病灶(<2%)、淋巴结(<4%)、骨病灶(0%)和软组织病灶(<4%)表现出摄取模式下降。

结论

基于梯度的方法完成率高,在18F-PSMA-1007 PET/CT上对前列腺癌病灶轮廓勾画可靠。在不同摄取时间的影响下,其在测量体积参数方面的变化比基于阈值的方法小。因此,推荐基于梯度的方法用于18F-PSMA-1007 PET/CT上的肿瘤勾画和定量分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e93e/9559596/90c159f63c0b/fonc-12-897700-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e93e/9559596/eddc685ecb35/fonc-12-897700-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e93e/9559596/90c159f63c0b/fonc-12-897700-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e93e/9559596/eddc685ecb35/fonc-12-897700-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e93e/9559596/90c159f63c0b/fonc-12-897700-g002.jpg

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