Literature DB >> 25893503

Comparison of PET with PET/CT in detecting peritoneal carcinomatosis: a meta-analysis.

Jinkui Li1, Ruifeng Yan1, Junqiang Lei2, Changqin Jiang1.   

Abstract

PURPOSE: The study aims to perform a meta-analysis to compare the diagnostic value of FDG PET with PET/CT in detecting peritoneal carcinomatosis (PC) to identify the potentially most useful diagnostic modality.
METHODS: A computer-aided search was performed in the Cochrane Library, PubMed, EMBASE, Web of Science, the China Biological Medicine Database, VIP, China National Knowledge Infrastructure database, and Wanfang databases for articles concerning diagnosis of peritoneal metastases with PET or PET/CT. QUADAS was used to evaluate the included articles' quality.
RESULTS: On a per-patient basis, the pooled sensitivity of PET/CT (84%) was significantly higher than that of PET (60%), and the pooled specificity of PET (98%) was markedly higher than that for PET/CT (94%). On a per-lesion basis, the pooled sensitivity and specificity of PET/CT were 87 and 95%, respectively. Only 1 PET study on a per-lesion basis, its sensitivity is 65.8 and specificity is 94.1%.
CONCLUSIONS: PET and PET/CT are powerful imaging techniques for detection and characterization of PC. PET/CT can be used as a screening tool and it may be acceptable to use PET as a diagnosis tool.

Entities:  

Keywords:  Meta-analysis; PET; PET/CT; Peritoneal carcinomatosis; Positron emission tomography

Mesh:

Substances:

Year:  2015        PMID: 25893503     DOI: 10.1007/s00261-015-0418-8

Source DB:  PubMed          Journal:  Abdom Imaging        ISSN: 0942-8925


  8 in total

Review 1.  Primary and metastatic peritoneal surface malignancies.

Authors:  Delia Cortés-Guiral; Martin Hübner; Mohammad Alyami; Aditi Bhatt; Wim Ceelen; Olivier Glehen; Florian Lordick; Robert Ramsay; Olivia Sgarbura; Kurt Van Der Speeten; Kiran K Turaga; Manish Chand
Journal:  Nat Rev Dis Primers       Date:  2021-12-16       Impact factor: 52.329

2.  Association between biopsy method and development of peritoneal metastases in perihilar cholangiocarcinoma.

Authors:  Victoria G Aveson; Crisanta H Ilagan; Joanne F Chou; Mithat Gönen; Vinod P Balachandran; Jeffrey A Drebin; William R Jarnagin; Alice C Wei; T Peter Kingham; Michael I D'Angelica
Journal:  HPB (Oxford)       Date:  2021-11-10       Impact factor: 3.842

Review 3.  Peritoneal malignancy: anatomy, pathophysiology and an update on modern day imaging.

Authors:  Jack W Power; Philip J Dempsey; Andrew Yates; Helen Fenlon; Jurgen Mulsow; Conor Shields; Carmel G Cronin
Journal:  Br J Radiol       Date:  2021-12-08       Impact factor: 3.629

Review 4.  Colorectal Peritoneal Metastases: A Systematic Review of Current and Emerging Trends in Clinical and Translational Research.

Authors:  Foteini Stefania Koumpa; Diamantis Xylas; Maciej Konopka; Dieter Galea; Kirill Veselkov; Anthony Antoniou; Akash Mehta; Reza Mirnezami
Journal:  Gastroenterol Res Pract       Date:  2019-04-01       Impact factor: 2.260

5.  Prediction of Anal Cancer Recurrence After Chemoradiotherapy Using Quantitative Image Features Extracted From Serial 18F-FDG PET/CT.

Authors:  Jiahui Wang; Hao Zhang; Michael Chuong; Kujtim Latifi; Shan Tan; Wookjin Choi; Sarah Hoffe; Ravi Shridhar; Wei Lu
Journal:  Front Oncol       Date:  2019-09-27       Impact factor: 6.244

6.  P.R.O.P.S. - A novel Pre-Operative Predictive Score for unresectability in patients with colorectal peritoneal metastases being considered for cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC).

Authors:  Zachary Zihui Yong; Grace Hwei Ching Tan; Nicholas Shannon; Claramae Chia; Melissa Ching Ching Teo
Journal:  World J Surg Oncol       Date:  2019-08-07       Impact factor: 2.754

Review 7.  Patient selection for cytoreductive surgery and HIPEC for the treatment of peritoneal metastases from colorectal cancer.

Authors:  Geert A Simkens; Koen P Rovers; Simon W Nienhuijs; Ignace H de Hingh
Journal:  Cancer Manag Res       Date:  2017-06-30       Impact factor: 3.989

8.  Predicting Peritoneal Metastasis of Gastric Cancer Patients Based on Machine Learning.

Authors:  Chengmao Zhou; Ying Wang; Mu-Huo Ji; Jianhua Tong; Jian-Jun Yang; Hongping Xia
Journal:  Cancer Control       Date:  2020 Jan-Dec       Impact factor: 3.302

  8 in total

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