Literature DB >> 36042989

Emerging Approaches to Complement Low-Dose Computerized Tomography for Lung Cancer Screening: A Narrative Review.

Bradley Maller1, Tawee Tanvetyanon2.   

Abstract

Lung cancer screening by low-dose computed tomography (LDCT) can save lives. Nevertheless, the test suffers from low accuracy. Improving its accuracy will reduce unnecessary invasive procedures and allow lung cancer treatment to be delivered sooner. This review describes the principles, advantages, and disadvantages of selected emerging modalities potentially useful to improve the accuracy of LDCT. A literature search was conducted using PubMed and Google scholar for relevant publications. We identified four key emerging approaches: radiomics, breath analysis, urine test, and blood test. Radiomics, which uses a computer program to extract various radiological features from radiographic images, holds the potential to improve the accuracy of LDCT. However, to date, there remains no adequately validated system. Breath analysis and urine tests represent a noninvasive and convenient means of screening by detecting substances such as volatile organic compounds associated with lung cancer. However, the results can be confounded by diets, medications, and concurrent medical conditions. Finally, a blood test to screen for protein biomarkers or methylation profiles such as Galleri® has high specificity. However, its sensitivity is low, especially for detecting early-stage lung cancer. Furthermore, the cost for mass public use can be significant. Based on our review, blood tests may have potential for future clinical utility. Its high specificity may be useful to rule in a suspicious lung nodule as malignant, so that other additional tests can be omitted. Data from a well-designed clinical trial will be needed to understand the clinical utility of this strategy.
Copyright © 2022, Maller et al.

Entities:  

Keywords:  lung cancer; lung cancer screening; methylation profile; radiomics; volatolomics

Year:  2022        PMID: 36042989      PMCID: PMC9410538          DOI: 10.7759/cureus.27309

Source DB:  PubMed          Journal:  Cureus        ISSN: 2168-8184


  30 in total

Review 1.  Serologic autoantibodies as diagnostic cancer biomarkers--a review.

Authors:  Pauline Zaenker; Melanie R Ziman
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2013-09-20       Impact factor: 4.254

2.  A Bronchial Genomic Classifier for the Diagnostic Evaluation of Lung Cancer.

Authors:  Gerard A Silvestri; Anil Vachani; Duncan Whitney; Michael Elashoff; Kate Porta Smith; J Scott Ferguson; Ed Parsons; Nandita Mitra; Jerome Brody; Marc E Lenburg; Avrum Spira
Journal:  N Engl J Med       Date:  2015-05-17       Impact factor: 91.245

3.  Urinary volatile compounds as biomarkers for lung cancer.

Authors:  Yosuke Hanai; Ken Shimono; Koichi Matsumura; Anil Vachani; Steven Albelda; Kunio Yamazaki; Gary K Beauchamp; Hiroaki Oka
Journal:  Biosci Biotechnol Biochem       Date:  2012-04-07       Impact factor: 2.043

Review 4.  Radiomics: extracting more information from medical images using advanced feature analysis.

Authors:  Philippe Lambin; Emmanuel Rios-Velazquez; Ralph Leijenaar; Sara Carvalho; Ruud G P M van Stiphout; Patrick Granton; Catharina M L Zegers; Robert Gillies; Ronald Boellard; André Dekker; Hugo J W L Aerts
Journal:  Eur J Cancer       Date:  2012-01-16       Impact factor: 9.162

5.  NCCN Guidelines® Insights: Lung Cancer Screening, Version 1.2022.

Authors:  Douglas E Wood; Ella A Kazerooni; Denise Aberle; Abigail Berman; Lisa M Brown; Georgie A Eapen; David S Ettinger; J Scott Ferguson; Lifang Hou; Dipen Kadaria; Donald Klippenstein; Rohit Kumar; Rudy P Lackner; Lorriana E Leard; Inga T Lennes; Ann N C Leung; Peter Mazzone; Robert E Merritt; David E Midthun; Mark Onaitis; Sudhakar Pipavath; Christie Pratt; Varun Puri; Dan Raz; Chakravarthy Reddy; Mary E Reid; Kim L Sandler; Jacob Sands; Matthew B Schabath; Jamie L Studts; Lynn Tanoue; Betty C Tong; William D Travis; Benjamin Wei; Kenneth Westover; Stephen C Yang; Beth McCullough; Miranda Hughes
Journal:  J Natl Compr Canc Netw       Date:  2022-07       Impact factor: 12.693

6.  Derivation of a bronchial genomic classifier for lung cancer in a prospective study of patients undergoing diagnostic bronchoscopy.

Authors:  Duncan H Whitney; Michael R Elashoff; Kate Porta-Smith; Adam C Gower; Anil Vachani; J Scott Ferguson; Gerard A Silvestri; Jerome S Brody; Marc E Lenburg; Avrum Spira
Journal:  BMC Med Genomics       Date:  2015-05-06       Impact factor: 3.063

Review 7.  Do we need to see to believe?-radiomics for lung nodule classification and lung cancer risk stratification.

Authors:  Ali Khawaja; Brian J Bartholmai; Srinivasan Rajagopalan; Ronald A Karwoski; Cyril Varghese; Fabien Maldonado; Tobias Peikert
Journal:  J Thorac Dis       Date:  2020-06       Impact factor: 2.895

8.  Highly accurate model for prediction of lung nodule malignancy with CT scans.

Authors:  Jason L Causey; Junyu Zhang; Shiqian Ma; Bo Jiang; Jake A Qualls; David G Politte; Fred Prior; Shuzhong Zhang; Xiuzhen Huang
Journal:  Sci Rep       Date:  2018-06-18       Impact factor: 4.379

9.  Urinary detection of lung cancer in mice via noninvasive pulmonary protease profiling.

Authors:  Jesse D Kirkpatrick; Andrew D Warren; Ava P Soleimany; Peter M K Westcott; Justin C Voog; Carmen Martin-Alonso; Heather E Fleming; Tuomas Tammela; Tyler Jacks; Sangeeta N Bhatia
Journal:  Sci Transl Med       Date:  2020-04-01       Impact factor: 17.956

10.  Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set.

Authors:  E A Klein; D Richards; A Cohn; M Tummala; R Lapham; D Cosgrove; G Chung; J Clement; J Gao; N Hunkapiller; A Jamshidi; K N Kurtzman; M V Seiden; C Swanton; M C Liu
Journal:  Ann Oncol       Date:  2021-06-24       Impact factor: 32.976

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