Literature DB >> 8421744

Determining the likelihood of malignancy in solitary pulmonary nodules with Bayesian analysis. Part II. Application.

J W Gurney1, D M Lyddon, J A McKay.   

Abstract

Four board-certified radiologists estimated the probability of malignancy in 66 cases of solitary pulmonary nodules. Two other radiologists evaluated the same nodules according to various radiographic and clinical findings. These findings were then used to estimate the probability of malignancy by using previously derived likelihood ratios and the Bayes theorem. The readers using Bayesian analysis performed significantly better than the expert readers (P < .05) when individual radiographs were considered and when all radiologic studies were combined. In addition, the readers using Bayesian analysis misclassified fewer malignant nodules as benign (mean, 6.5) than did the expert readers (mean, 6.5) than did the expert readers (mean, 16.5). The authors conclude that Bayesian analysis may be a useful aid in the evaluation of solitary pulmonary nodules.

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Year:  1993        PMID: 8421744     DOI: 10.1148/radiology.186.2.8421744

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


  43 in total

1.  The impact of radiologists' expertise on screen results decisions in a CT lung cancer screening trial.

Authors:  Marjolein A Heuvelmans; Matthijs Oudkerk; Pim A de Jong; Willem P Mali; Harry J M Groen; Rozemarijn Vliegenthart
Journal:  Eur Radiol       Date:  2014-11-04       Impact factor: 5.315

2.  Management of solitary pulmonary nodules: how do thoracic computed tomography and guided fine needle biopsy influence clinical decisions?

Authors:  D R Baldwin; T Eaton; J Kolbe; T Christmas; D Milne; J Mercer; E Steele; J Garrett; M L Wilsher; A U Wells
Journal:  Thorax       Date:  2002-09       Impact factor: 9.139

3.  Characterization of solitary pulmonary nodules: Use of washout characteristics at contrast-enhanced computed tomography.

Authors:  Xiao-Dan Ye; Jian-Ding Ye; Zheng Yuan; Sheng Dong; Xiang-Sheng Xiao
Journal:  Oncol Lett       Date:  2011-12-12       Impact factor: 2.967

4.  Solid pulmonary nodule risk assessment and decision analysis: comparison of four prediction models in 285 cases.

Authors:  Simone Perandini; Gian Alberto Soardi; Massimiliano Motton; Arianna Rossi; Manuel Signorini; Stefania Montemezzi
Journal:  Eur Radiol       Date:  2015-12-08       Impact factor: 5.315

5.  Improved pulmonary nodule classification utilizing quantitative lung parenchyma features.

Authors:  Samantha K N Dilger; Johanna Uthoff; Alexandra Judisch; Emily Hammond; Sarah L Mott; Brian J Smith; John D Newell; Eric A Hoffman; Jessica C Sieren
Journal:  J Med Imaging (Bellingham)       Date:  2015-09-01

Review 6.  Identifying lung cancer in patients with active pulmonary tuberculosis.

Authors:  Cassandra S Parker; Carrie G Siracuse; Virginia R Litle
Journal:  J Thorac Dis       Date:  2018-10       Impact factor: 2.895

Review 7.  Management of an incidentally discovered pulmonary nodule.

Authors:  Catherine Beigelman-Aubry; Catherine Hill; Philippe A Grenier
Journal:  Eur Radiol       Date:  2006-10-05       Impact factor: 5.315

8.  Quantitative MDCT analysis of pulmonary solid nodules using three parameters.

Authors:  Naoki Kutuya; Yutaka Ozaki; Yoshihisa Kurosaki
Journal:  Radiat Med       Date:  2008-09-04

Review 9.  Computer-aided diagnosis of lung cancer and pulmonary embolism in computed tomography-a review.

Authors:  Heang-Ping Chan; Lubomir Hadjiiski; Chuan Zhou; Berkman Sahiner
Journal:  Acad Radiol       Date:  2008-05       Impact factor: 3.173

Review 10.  Current role of positron emission tomography in thoracic oncology.

Authors:  V J Lowe; K S Naunheim
Journal:  Thorax       Date:  1998-08       Impact factor: 9.139

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