Suppr超能文献

多参数MRI联合临床信息预测前列腺癌初诊分期时盆腔淋巴结转移及PSMA PET阳性盆腔淋巴结

Prediction of pelvic lymph node metastases and PSMA PET positive pelvic lymph nodes with multiparametric MRI and clinical information in primary staging of prostate cancer.

作者信息

Hötker Andreas M, Mühlematter Urs, Beintner-Skawran Stephan, Ghafoor Soleen, Burger Irene, Huellner Martin, Eberli Daniel, Donati Olivio F

机构信息

University Hospital Zurich, Institute of Diagnostic and Interventional Radiology, Rämistrasse 100, 8091 Zürich, Switzerland.

University Hospital Zurich, Department of Nuclear Medicine, Rämistrasse 100, 8091 Zürich, Switzerland.

出版信息

Eur J Radiol Open. 2023 Mar 30;10:100487. doi: 10.1016/j.ejro.2023.100487. eCollection 2023.

Abstract

PURPOSE

To compare the accuracy of multiparametric MRI (mpMRI), Ga-PSMA PET and the Briganti 2019 nomogram in the prediction of metastatic pelvic lymph nodes (PLN) in prostate cancer, to assess the accuracy of mpMRI and the Briganti nomogram in prediction of PET positive PLN and to investigate the added value of quantitative mpMRI parameters to the Briganti nomogram.

METHOD

This retrospective IRB-approved study included 41 patients with prostate cancer undergoing mpMRI and Ga-PSMA PET/CT or MR prior to prostatectomy and pelvic lymph node dissection. A board-certified radiologist assessed the index lesion on diffusion-weighted (Apparent Diffusion Coefficient, ADC; mean/volume), T2-weighted (capsular contact length, lesion volume/maximal diameters) and contrast-enhanced (iAUC, k, K, v) sequences. The probability for metastatic pelvic lymph nodes was calculated using the Briganti 2019 nomogram. PET examinations were evaluated by two board-certified nuclear medicine physicians.

RESULTS

The Briganti 2019 nomogram performed superiorly (AUC: 0.89) compared to quantitative mpMRI parameters (AUCs: 0.47-0.73) and Ga-PSMA-11 PET (AUC: 0.82) in the prediction of PLN metastases and superiorly (AUC: 0.77) in the prediction of PSMA PET positive PLN compared to MRI parameters (AUCs: 0.49-0.73). The addition of mean ADC and ADC volume from mpMRI improved the Briganti model by a fraction of new information of 0.21.

CONCLUSIONS

The Briganti 2019 nomogram performed superiorly in the prediction of metastatic and PSMA PET positive PLN, but the addition of parameters from mpMRI can further improve its accuracy. The combined model could be used to stratify patients requiring ePLND or PSMA PET.

摘要

目的

比较多参数磁共振成像(mpMRI)、镓-前列腺特异性膜抗原(Ga-PSMA)正电子发射断层扫描(PET)和布里甘蒂2019列线图在预测前列腺癌盆腔转移淋巴结(PLN)方面的准确性,评估mpMRI和布里甘蒂列线图在预测PET阳性PLN方面的准确性,并研究定量mpMRI参数对布里甘蒂列线图的附加值。

方法

这项经机构审查委员会(IRB)批准的回顾性研究纳入了41例前列腺癌患者,这些患者在前列腺切除术和盆腔淋巴结清扫术前接受了mpMRI和Ga-PSMA PET/CT或磁共振成像(MR)检查。一名经过委员会认证的放射科医生在扩散加权(表观扩散系数,ADC;平均值/体积)、T2加权(包膜接触长度、病变体积/最大直径)和对比增强(iAUC、k、K、v)序列上评估索引病变。使用布里甘蒂2019列线图计算盆腔转移淋巴结的概率。PET检查由两名经过委员会认证的核医学医生进行评估。

结果

在预测PLN转移方面,布里甘蒂2019列线图(AUC:0.89)比定量mpMRI参数(AUC:0.47 - 0.73)和Ga-PSMA-11 PET(AUC:0.82)表现更优;在预测PSMA PET阳性PLN方面,与MRI参数(AUC:0.49 - 0.73)相比,布里甘蒂列线图(AUC:0.77)表现更优。mpMRI的平均ADC和ADC体积的加入,使布里甘蒂模型的新信息增加了0.21。

结论

布里甘蒂2019列线图在预测转移和PSMA PET阳性PLN方面表现更优,但加入mpMRI参数可进一步提高其准确性。联合模型可用于对需要进行扩大盆腔淋巴结清扫术(ePLND)或PSMA PET的患者进行分层。

相似文献

8
Ga-PSMA-11 PET has the potential to improve patient selection for extended pelvic lymph node dissection in intermediate to high-risk prostate cancer.
Eur J Nucl Med Mol Imaging. 2020 Jan;47(1):147-159. doi: 10.1007/s00259-019-04511-4. Epub 2019 Sep 14.

本文引用的文献

5
Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2.
Eur Urol. 2019 Sep;76(3):340-351. doi: 10.1016/j.eururo.2019.02.033. Epub 2019 Mar 18.
9
Ga-PSMA PET/CT: Joint EANM and SNMMI procedure guideline for prostate cancer imaging: version 1.0.
Eur J Nucl Med Mol Imaging. 2017 Jun;44(6):1014-1024. doi: 10.1007/s00259-017-3670-z.
10
Assessment of Prostate Cancer Aggressiveness by Use of the Combination of Quantitative DWI and Dynamic Contrast-Enhanced MRI.
AJR Am J Roentgenol. 2016 Apr;206(4):756-63. doi: 10.2214/AJR.15.14912. Epub 2016 Feb 22.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验