Partouche Ephraïm, Yeh Randy, Eche Thomas, Rozenblum Laura, Carrere Nicolas, Guimbaud Rosine, Dierickx Lawrence O, Rousseau Hervé, Dercle Laurent, Mokrane Fatima-Zohra
Radiology Department, Rangueil University Hospital, Toulouse, France.
Memorial Sloan Kettering Cancer Center, Molecular Imaging and Therapy Service., New York, NY, United States.
Front Oncol. 2021 Jul 14;11:628408. doi: 10.3389/fonc.2021.628408. eCollection 2021.
Medical imaging plays a central and decisive role in guiding the management of patients with pancreatic neuroendocrine tumors (PNETs). Our aim was to synthesize all recent literature of PNETs, enabling a comparison of all imaging practices.
based on a systematic review and meta-analysis approach, we collected; using MEDLINE, EMBASE, and Cochrane Library databases; all recent imaging-based studies, published from December 2014 to December 2019. Study quality assessment was performed by QUADAS-2 and MINORS tools.
161 studies consisting of 19852 patients were included. There were 63 'imaging' studies evaluating the accuracy of medical imaging, and 98 'clinical' studies using medical imaging as a tool for response assessment. A wide heterogeneity of practices was demonstrated: imaging modalities were: CT (57.1%, n=92), MR (42.9%, n=69), PET/CT (13.3%, n=31), and SPECT/CT (9.3%, n=15). International imaging guidelines were mentioned in 2.5% (n=4/161) of studies. In clinical studies, imaging protocol was not mentioned in 30.6% (n=30/98) of cases and only mentioned imaging modality without further information in 63.3% (n=62/98), as compared to imaging studies (1.6% (n=1/63) of (p<0.001)). QUADAS-2 and MINORS tools deciphered existing biases in the current literature.
We provide an overview of the updated current trends in use of medical imaging for diagnosis and response assessment in PNETs. The most commonly used imaging modalities are anatomical (CT and MRI), followed by PET/CT and SPECT/CT. Therefore, standardization and homogenization of PNETs imaging practices is needed to aggregate data and leverage a big data approach for Artificial Intelligence purposes.
医学成像在指导胰腺神经内分泌肿瘤(PNETs)患者的管理中起着核心和决定性作用。我们的目的是综合所有关于PNETs的最新文献,以便对所有成像方法进行比较。
基于系统评价和荟萃分析方法,我们通过MEDLINE、EMBASE和Cochrane图书馆数据库收集了2014年12月至2019年12月发表的所有基于成像的最新研究。采用QUADAS - 2和MINORS工具进行研究质量评估。
纳入了161项研究,共19852例患者。其中有63项“成像”研究评估医学成像的准确性,98项“临床”研究将医学成像用作疗效评估工具。研究显示出广泛的方法异质性:成像方式包括:CT(57.1%,n = 92)、MR(42.9%,n = 69)、PET/CT(13.3%,n = 31)和SPECT/CT(9.3%,n = 15)。2.5%(n = 4/161)的研究提及了国际成像指南。在临床研究中,30.6%(n = 30/98)的病例未提及成像方案,63.3%(n = 62/98)仅提及成像方式而无更多信息,而成像研究中这一比例为1.6%(n = 1/63)(p<0.001)。QUADAS - 2和MINORS工具揭示了当前文献中存在的偏差。
我们概述了医学成像在PNETs诊断和疗效评估中使用的最新趋势。最常用的成像方式是解剖学成像(CT和MRI),其次是PET/CT和SPECT/CT。因此,需要对PNETs成像方法进行标准化和同质化,以汇总数据并利用大数据方法服务于人工智能目的。