» Articles » PMID: 15547236

Accuracy of CT in the Diagnosis of Pulmonary Embolism: a Systematic Literature Review

Overview
Specialties Oncology
Radiology
Date 2004 Nov 18
PMID 15547236
Citations 19
Authors
Affiliations
Soon will be listed here.
Abstract

Objective: We sought to summarize systematically the published evidence describing the accuracy of contrast-enhanced helical CT for diagnosing pulmonary embolism.

Materials And Methods: We selected all systematic reviews published before December 2003 that evaluated the accuracy of CT angiography for the diagnosis of pulmonary embolism. We also selected all prospective studies from the same time period in the primary literature in which all subjects underwent both CT and conventional angiography, the latter being considered the reference standard. Articles were identified through a computerized MEDLINE search and by other means. The quality and content of each article were evaluated independently by pairs of researchers.

Results: Six systematic reviews and eight primary studies were selected. The combined sensitivities of CT for detecting pulmonary embolism ranged from 66% to 93% across the systematic reviews and the combined specificities ranged from 89% to 97%. Only one of the reviews reported a combined sensitivity of greater than 90%. Among the eight primary studies, the sensitivities ranged from 45% to 100% and specificities ranged from 78% to 100%. Only three of the eight primary studies reported a sensitivity greater than 90%. None of the primary studies used scanners with four or more detectors.

Conclusion: A systematic literature review revealed a wide range of reported sensitivities, only a minority of which exceeded 90%. Pooled estimates of sensitivity and specificity reported by systematic literature reviews should be interpreted with caution because of potential selection bias and heterogeneity in the reviewed studies. Accuracy studies of recent generations of MDCT scanners are not yet available despite the current dissemination of this technology.

Citing Articles

Contribution of an Artificial Intelligence Tool in the Detection of Incidental Pulmonary Embolism on Oncology Assessment Scans.

Ammari S, Camez A, Ayobi A, Quenet S, Zemmouri A, Mniai E Life (Basel). 2024; 14(11).

PMID: 39598146 PMC: 11595865. DOI: 10.3390/life14111347.


A systematic review of artificial intelligence tools for chronic pulmonary embolism on CT pulmonary angiography.

Abdulaal L, Maiter A, Salehi M, Sharkey M, Alnasser T, Garg P Front Radiol. 2024; 4:1335349.

PMID: 38654762 PMC: 11035730. DOI: 10.3389/fradi.2024.1335349.


Improving the radiological diagnosis of hepatic artery thrombosis after liver transplantation: Current approaches and future challenges.

Lindner C, Riquelme R, San Martin R, Quezada F, Valenzuela J, Maureira J World J Transplant. 2024; 14(1):88938.

PMID: 38576750 PMC: 10989478. DOI: 10.5500/wjt.v14.i1.88938.


Deep Learning-Based Algorithm for Automatic Detection of Pulmonary Embolism in Chest CT Angiograms.

Grenier P, Ayobi A, Quenet S, Tassy M, Marx M, Chow D Diagnostics (Basel). 2023; 13(7).

PMID: 37046542 PMC: 10093638. DOI: 10.3390/diagnostics13071324.


Pulmonary embolism presenting as delirium: an acute confusional state in an elderly patient-a case report.

Ahaneku C, Akpu B, Njoku C, Elem D, Ekeng B Egypt J Intern Med. 2023; 35(1):8.

PMID: 36777903 PMC: 9899661. DOI: 10.1186/s43162-023-00193-5.