Lung nodule and cancer detection in computed tomography screening.

Published

Journal Article (Review)

Fundamental to the diagnosis of lung cancer in computed tomography (CT) scans is the detection and interpretation of lung nodules. As the capabilities of CT scanners have advanced, higher levels of spatial resolution reveal tinier lung abnormalities. Not all detected lung nodules should be reported; however, radiologists strive to detect all nodules that might have relevance to cancer diagnosis. Although medium to large lung nodules are detected consistently, interreader agreement and reader sensitivity for lung nodule detection diminish substantially as the nodule size falls below 8 to 10 mm. The difficulty in establishing an absolute reference standard presents a challenge to the reliability of studies performed to evaluate lung nodule detection. In the interest of improving detection performance, investigators are using eye tracking to analyze the effectiveness with which radiologists search CT scans relative to their ability to recognize nodules within their search path in order to determine whether strategies might exist to improve performance across readers. Beyond the viewing of transverse CT reconstructions, image processing techniques such as thin-slab maximum-intensity projections are used to substantially improve reader performance. Finally, the development of computer-aided detection has continued to evolve with the expectation that one day it will serve routinely as a tireless partner to the radiologist to enhance detection performance without significant prolongation of the interpretive process. This review provides an introduction to the current understanding of these varied issues as we enter the era of widespread lung cancer screening.

Full Text

Duke Authors

Cited Authors

  • Rubin, GD

Published Date

  • March 2015

Published In

Volume / Issue

  • 30 / 2

Start / End Page

  • 130 - 138

PubMed ID

  • 25658477

Pubmed Central ID

  • 25658477

Electronic International Standard Serial Number (EISSN)

  • 1536-0237

Digital Object Identifier (DOI)

  • 10.1097/RTI.0000000000000140

Language

  • eng

Conference Location

  • United States