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基于C-胆碱PET/CT的多代谢参数组合在鉴别早期前列腺癌与良性前列腺疾病中的价值

Value of C-Choline PET/CT-Based Multi-Metabolic Parameter Combination in Distinguishing Early-Stage Prostate Cancer From Benign Prostate Diseases.

作者信息

Zhou Shuoming, Fu Hongliang, Liu Changming, Zhu Ziqiang, Zhang Jiabin, Weng Wubin, Kang Jian, Liu Qiang

机构信息

Department of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Department of Nuclear Medicine, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.

出版信息

Front Oncol. 2021 Feb 1;10:600380. doi: 10.3389/fonc.2020.600380. eCollection 2020.

DOI:10.3389/fonc.2020.600380
PMID:33598428
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7882704/
Abstract

PURPOSE

The most common disadvantage of C-choline positron emission tomography and computed tomography (PET/CT) in diagnosing early-stage prostate cancer (PCa) is its poor sensitivity. In spite of many efforts, this imaging modality lacks the ideal parameter of choline metabolism for the diagnosis of PCa, and the single metabolic parameter, that is, maximal standardized uptake value (SUVmax), based on this imaging modality is insufficient. C-choline PET/CT-based multi-metabolic parameter combination can help break this limitation.

MATERIALS AND METHODS

Before surgery, SUVmax of choline, which is the most common metabolic parameter of C-choline PET/CT, mean standardized uptake value (SUVmean), prostate-to-muscle (P/M) ratio, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) from 74 patients with histologically proven PCa were quantified. A total of 13 patients with focal chronic prostatitis without severe features and 30 patients with benign prostate hyperplasia were used for comparison. Univariable and multivariable analyses were performed to compare the patient characteristics and metabolic parameters of C-choline PET/CT. The performance of single parameters and the combination of parameters were assessed by using logistic regression models.

RESULTS

The comparable c-statistics, which mean the area under the ROC curve in the logistic regression model, of SUVmax, SUVmean, and P/M ratio are 0.657, 0.667, and 0.672, respectively. The c-statistic significantly rose to 0.793 when SUVmax and SUVmean were combined with the P/M ratio. This parameter combination performed the best for PCa cases with all biochemical recurrence risks and for PCa patients grouped by different risk. The greatest improvement over a single parameter, such as P/M ratio, was noted in the group of low-risk PCa, with values of 0.535 to 0.772 for the three-parameter combination. And in the histopathological level, the Ki-67 index is positively correlated with the P/M ratio (r=0.491, =0.002).

CONCLUSION

P/M ratio is a more ideal parameter than SUVmax as a single parameter in early-stage PCa diagnosis. According to our data, the combination of SUVmax, SUVmean, and P/M ratio as a composite parameter for diagnosis of early stage PCa improves the diagnostic accuracy of C-choline PET/CT.

摘要

目的

C胆碱正电子发射断层扫描与计算机断层扫描(PET/CT)在诊断早期前列腺癌(PCa)时最常见的缺点是其敏感性较差。尽管做了很多努力,但这种成像方式缺乏用于诊断PCa的理想胆碱代谢参数,基于这种成像方式的单一代谢参数,即最大标准化摄取值(SUVmax)并不充分。基于C胆碱PET/CT的多代谢参数组合有助于突破这一限制。

材料与方法

对74例经组织学证实为PCa的患者在手术前对C胆碱PET/CT最常见的代谢参数胆碱的SUVmax、平均标准化摄取值(SUVmean)、前列腺与肌肉比值(P/M)、代谢肿瘤体积(MTV)和总病变糖酵解(TLG)进行量化。选取13例无严重特征的局灶性慢性前列腺炎患者和30例良性前列腺增生患者进行比较。进行单变量和多变量分析以比较C胆碱PET/CT的患者特征和代谢参数。使用逻辑回归模型评估单一参数和参数组合的性能。

结果

SUVmax、SUVmean和P/M比值在逻辑回归模型中的c统计量(即ROC曲线下面积)分别为0.657、0.667和0.672。当SUVmax、SUVmean与P/M比值组合时,c统计量显著升至0.793。这种参数组合对所有生化复发风险的PCa病例以及按不同风险分组的PCa患者表现最佳。在低风险PCa组中,相对于单一参数如P/M比值有最大改善,三参数组合的值为0.535至0.772。在组织病理学水平上,Ki-67指数与P/M比值呈正相关(r=0.491,P=0.002)。

结论

在早期PCa诊断中,P/M比值作为单一参数比SUVmax更理想。根据我们的数据,SUVmax、SUVmean和P/M比值组合作为诊断早期PCa的复合参数可提高C胆碱PET/CT的诊断准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a9/7882704/96cd9237070c/fonc-10-600380-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a9/7882704/3cae04410fb7/fonc-10-600380-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a9/7882704/5170a89e8a70/fonc-10-600380-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a9/7882704/51745208d53d/fonc-10-600380-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a9/7882704/5b0b19c21428/fonc-10-600380-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a9/7882704/078ad2684698/fonc-10-600380-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a9/7882704/96cd9237070c/fonc-10-600380-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a9/7882704/3cae04410fb7/fonc-10-600380-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a9/7882704/5170a89e8a70/fonc-10-600380-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a9/7882704/51745208d53d/fonc-10-600380-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a9/7882704/5b0b19c21428/fonc-10-600380-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a9/7882704/078ad2684698/fonc-10-600380-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c0a9/7882704/96cd9237070c/fonc-10-600380-g006.jpg

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