Literature DB >> 25842017

Role of the Quantitative Imaging Biomarker Alliance in optimizing CT for the evaluation of lung cancer screen-detected nodules.

James L Mulshine1, David S Gierada2, Samuel G Armato3, Rick S Avila4, David F Yankelevitz5, Ella A Kazerooni6, Michael F McNitt-Gray7, Andrew J Buckler8, Daniel C Sullivan9.   

Abstract

The Quantitative Imaging Biomarker Alliance (QIBA) is a multidisciplinary consortium sponsored by the RSNA to define processes that enable the implementation and advancement of quantitative imaging methods described in a QIBA profile document that outlines the process to reliably and accurately measure imaging features. A QIBA profile includes factors such as technical (product-specific) standards, user activities, and relationship to a clinically meaningful metric, such as with nodule measurement in the course of CT screening for lung cancer. In this report, the authors describe how the QIBA approach is being applied to the measurement of small pulmonary nodules such as those found during low-dose CT-based lung cancer screening. All sources of variance with imaging measurement were defined for this process. Through a process of experimentation, literature review, and assembly of expert opinion, the strongest evidence was used to define how to best implement each step in the imaging acquisition and evaluation process. This systematic approach to implementing a quantitative imaging biomarker with standardized specifications for image acquisition and postprocessing for a specific quantitative measurement of a pulmonary nodule results in consistent performance characteristics of the measurement (eg, bias and variance). Implementation of the QIBA small nodule profile may allow more efficient and effective clinical management of the diagnostic workup of individuals found to have suspicious pulmonary nodules in the course of lung cancer screening evaluation.
Copyright © 2015 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Lung cancer screening; low-dose CT scans; metrology; pulmonary nodules; quantitative imaging biomarker

Mesh:

Substances:

Year:  2015        PMID: 25842017     DOI: 10.1016/j.jacr.2014.12.003

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  16 in total

Review 1.  The Pursuit of Noninvasive Diagnosis of Lung Cancer.

Authors:  Thomas Atwater; Christine M Cook; Pierre P Massion
Journal:  Semin Respir Crit Care Med       Date:  2016-10-12       Impact factor: 3.119

2.  Statistical characterization of noise for spatial standardization of CT scans: Enabling comparison with multiple kernels and doses.

Authors:  Gonzalo Vegas-Sánchez-Ferrero; Maria J Ledesma-Carbayo; George R Washko; Raúl San José Estépar
Journal:  Med Image Anal       Date:  2017-06-07       Impact factor: 8.545

Review 3.  Quality assurance and quantitative imaging biomarkers in low-dose CT lung cancer screening.

Authors:  Chara E Rydzak; Samuel G Armato; Ricardo S Avila; James L Mulshine; David F Yankelevitz; David S Gierada
Journal:  Br J Radiol       Date:  2017-10-27       Impact factor: 3.039

4.  Status of lung cancer screening.

Authors:  James L Mulshine
Journal:  J Thorac Dis       Date:  2017-11       Impact factor: 2.895

5.  Low-dose lung cancer screening with photon-counting CT: a feasibility study.

Authors:  Rolf Symons; Tyler E Cork; Pooyan Sahbaee; Matthew K Fuld; Steffen Kappler; Les R Folio; David A Bluemke; Amir Pourmorteza
Journal:  Phys Med Biol       Date:  2016-12-17       Impact factor: 3.609

6.  Harmonization of chest CT scans for different doses and reconstruction methods.

Authors:  Gonzalo Vegas-Sánchez-Ferrero; Maria Jesus Ledesma-Carbayo; George R Washko; Raúl San José Estépar
Journal:  Med Phys       Date:  2019-06-07       Impact factor: 4.071

7.  Quantitative CT characterization of pediatric lung development using routine clinical imaging.

Authors:  Jill M Stein; Laura L Walkup; Alan S Brody; Robert J Fleck; Jason C Woods
Journal:  Pediatr Radiol       Date:  2016-08-30

Review 8.  Risk factors assessment and risk prediction models in lung cancer screening candidates.

Authors:  Mariusz Adamek; Ewa Wachuła; Sylwia Szabłowska-Siwik; Agnieszka Boratyn-Nowicka; Damian Czyżewski
Journal:  Ann Transl Med       Date:  2016-04

9.  Autocalibration method for non-stationary CT bias correction.

Authors:  Gonzalo Vegas-Sánchez-Ferrero; Maria J Ledesma-Carbayo; George R Washko; Raúl San José Estépar
Journal:  Med Image Anal       Date:  2017-12-08       Impact factor: 8.545

10.  Intra-scan inter-tissue variability can help harmonize radiomics features in CT.

Authors:  Hubert Beaumont; Antoine Iannessi; Jean Michel Cucchi; Anne-Sophie Bertrand; Olivier Lucidarme
Journal:  Eur Radiol       Date:  2021-08-06       Impact factor: 5.315

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