Literature DB >> 28837413

Radiologic Features of Small Pulmonary Nodules and Lung Cancer Risk in the National Lung Screening Trial: A Nested Case-Control Study.

Ying Liu1, Hua Wang1, Qian Li1, Melissa J McGettigan1, Yoganand Balagurunathan1, Alberto L Garcia1, Zachary J Thompson1, John J Heine1, Zhaoxiang Ye1, Robert J Gillies1, Matthew B Schabath1.   

Abstract

Purpose To extract radiologic features from small pulmonary nodules (SPNs) that did not meet the original criteria for a positive screening test and identify features associated with lung cancer risk by using data and images from the National Lung Screening Trial (NLST). Materials and Methods Radiologic features in SPNs in baseline low-dose computed tomography (CT) screening studies that did not meet NLST criteria to be considered a positive screening examination were extracted. SPNs were identified for 73 incident case patients who were given a diagnosis of lung cancer at either the first or second follow-up screening study and for 157 control subjects who had undergone three consecutive negative screening studies. Multivariable logistic regression was used to assess the association between radiologic features and lung cancer risk. All statistical tests were two sided. Results Nine features were significantly different between case patients and control subjects. Backward elimination followed by bootstrap resampling identified a reduced model of highly informative radiologic features with an area under the receiver operating characteristic curve of 0.932 (95% confidence interval [CI]: 0.88, 0.96), a specificity of 92.38% (95% CI: 52.22%, 84.91%), and a sensitivity of 76.55% (95% CI: 87.50%, 95.35%) that included total emphysema score (odds ratio [OR] = 1.71; 95% CI: 1.39, 2.01), attachment to vessel (OR = 2.41; 95% CI: 0.99, 5.81), nodule location (OR = 3.25; 95% CI: 1.09, 8.55), border definition (OR = 7.56; 95% CI: 1.89, 30.8), and concavity (OR = 2.58; 95% CI: 0.89, 5.64). Conclusion A set of clinically relevant radiologic features were identified that that can be easily scored in the clinical setting and may be of use to determine lung cancer risk among participants with SPNs. © RSNA, 2017 Online supplemental material is available for this article.

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Year:  2017        PMID: 28837413      PMCID: PMC5738292          DOI: 10.1148/radiol.2017161458

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  32 in total

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2.  Solitary pulmonary nodule: high-resolution CT and radiologic-pathologic correlation.

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Journal:  Radiology       Date:  1991-05       Impact factor: 11.105

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4.  Fleischner Society: glossary of terms for thoracic imaging.

Authors:  David M Hansell; Alexander A Bankier; Heber MacMahon; Theresa C McLoud; Nestor L Müller; Jacques Remy
Journal:  Radiology       Date:  2008-01-14       Impact factor: 11.105

5.  Peripheral solitary pulmonary nodule: CT findings in patients with pulmonary emphysema.

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6.  Combined pulmonary fibrosis and emphysema: a distinct underrecognised entity.

Authors:  V Cottin; H Nunes; P-Y Brillet; P Delaval; G Devouassoux; I Tillie-Leblond; D Israel-Biet; I Court-Fortune; D Valeyre; J-F Cordier
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8.  New classification of small pulmonary nodules by margin characteristics on high-resolution CT.

Authors:  K Furuya; S Murayama; H Soeda; J Murakami; Y Ichinose; H Yabuuchi; Y Katsuda; M Koga; K Masuda
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10.  Small pulmonary nodules: detection at chest CT and outcome.

Authors:  Matthew S Benjamin; Elizabeth A Drucker; Theresa C McLoud; Jo-Anne O Shepard
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1.  Quantification of Perinodular Emphysema in High-risk Patients Offers No Benefit in Lung Nodule Risk-Stratification of Malignancy Potential.

Authors:  William H Amundson; Eric J Swanson; Ashley Petersen; Brian J Bell; Charles Hatt; Chris H Wendt
Journal:  J Thorac Imaging       Date:  2020-03       Impact factor: 3.000

2.  Representation of Deep Features using Radiologist defined Semantic Features.

Authors:  Rahul Paul; Ying Liu; Qian Li; Lawrence Hall; Dmitry Goldgof; Yoganand Balagurunathan; Matthew Schabath; Robert Gillies
Journal:  Proc Int Jt Conf Neural Netw       Date:  2018-09-15

3.  Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: a case-control study.

Authors:  Wei Wu; Larry A Pierce; Yuzheng Zhang; Sudhakar N J Pipavath; Timothy W Randolph; Kristin J Lastwika; Paul D Lampe; A McGarry Houghton; Haining Liu; Liming Xia; Paul E Kinahan
Journal:  Eur Radiol       Date:  2019-05-21       Impact factor: 5.315

4.  Optimal lung cancer screening intervals following a negative low-dose computed tomography result.

Authors:  Monica E Reyes; Matthew B Schabath
Journal:  J Thorac Dis       Date:  2019-09       Impact factor: 2.895

5.  Volume doubling time and radiomic features predict tumor behavior of screen-detected lung cancers.

Authors:  Jaileene Pérez-Morales; Hong Lu; Wei Mu; Ilke Tunali; Tugce Kutuk; Steven A Eschrich; Yoganand Balagurunathan; Robert J Gillies; Matthew B Schabath
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6.  The Value of PETRA in Pulmonary Nodules of <3 cm Among Patients With Lung Cancer.

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8.  The multidisciplinary team plays an important role in the prediction of small solitary pulmonary nodules: a propensity-score-matching study.

Authors:  Chaoyuan Liu; Lishu Zhao; Fang Wu; Yeqian Feng; Rong Jiang; Chunhong Hu
Journal:  Ann Transl Med       Date:  2019-12

9.  Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge.

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Journal:  IEEE Trans Med Imaging       Date:  2021-11-30       Impact factor: 11.037

10.  A Noninvasive Multianalytical Approach for Lung Cancer Diagnosis of Patients with Pulmonary Nodules.

Authors:  Quan-Xing Liu; Dong Zhou; Tian-Cheng Han; Xiao Lu; Bing Hou; Man-Yuan Li; Gui-Xue Yang; Qing-Yuan Li; Zhi-Hua Pei; Yuan-Yuan Hong; Ya-Xi Zhang; Wei-Zhi Chen; Hong Zheng; Ji He; Ji-Gang Dai
Journal:  Adv Sci (Weinh)       Date:  2021-05-07       Impact factor: 16.806

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