Literature DB >> 24737068

[The importance of risk models for management of pulmonary nodules].

H Prosch1, P Baltzer.   

Abstract

CLINICAL/METHODICAL ISSUE: Pulmonary nodules are a frequent finding in computed tomography (CT) investigations. STANDARD RADIOLOGICAL
METHODS: Further diagnostic work-up of detected nodules mainly depends on the so-called pre-test probability, i.e. the probability that the nodule is malignant or benign. METHODICAL INNOVATIONS: The pre-test probability can be calculated by combining all relevant information, such as the age and the sex of the patient, the smoking history, and history of previous malignancies, as well as the size and CT morphology of the nodule. PERFORMANCE: If additional investigations are performed to further investigate the nodules, all results must be interpreted taking into account the pre-test probability and the test performance of the investigation in order to estimate the post-test probability. ACHIEVEMENTS: In cases with a low pre-test probability, a negative result from an exact test can exclude malignancies but a positive test cannot prove malignancy in such a setting. In cases with a high pre-test probability, a positive test result can be considered as proof of malignancy but a negative test result does not exclude malignancy.

Entities:  

Mesh:

Year:  2014        PMID: 24737068     DOI: 10.1007/s00117-013-2600-8

Source DB:  PubMed          Journal:  Radiologe        ISSN: 0033-832X            Impact factor:   0.635


  15 in total

1.  Solitary pulmonary nodules: clinical prediction model versus physicians.

Authors:  S J Swensen; M D Silverstein; E S Edell; V F Trastek; G L Aughenbaugh; D M Ilstrup; C D Schleck
Journal:  Mayo Clin Proc       Date:  1999-04       Impact factor: 7.616

2.  Development and validation of diagnostic prediction model for solitary pulmonary nodules.

Authors:  Kan Yonemori; Ukihide Tateishi; Hajime Uno; Yoko Yonemori; Koji Tsuta; Masahiro Takeuchi; Yoshihiro Matsuno; Yasuhiro Fujiwara; Hisao Asamura; Masahiko Kusumoto
Journal:  Respirology       Date:  2007-11       Impact factor: 6.424

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

Authors:  J W Gurney; D M Lyddon; J A McKay
Journal:  Radiology       Date:  1993-02       Impact factor: 11.105

4.  Determining the likelihood of malignancy in solitary pulmonary nodules with Bayesian analysis. Part I. Theory.

Authors:  J W Gurney
Journal:  Radiology       Date:  1993-02       Impact factor: 11.105

5.  Existing general population models inaccurately predict lung cancer risk in patients referred for surgical evaluation.

Authors:  James M Isbell; Stephen Deppen; Joe B Putnam; Jonathan C Nesbitt; Eric S Lambright; Aaron Dawes; Pierre P Massion; Theodore Speroff; David R Jones; Eric L Grogan
Journal:  Ann Thorac Surg       Date:  2011-01       Impact factor: 4.330

6.  Targeting of low-dose CT screening according to the risk of lung-cancer death.

Authors:  Anil K Chaturvedi; Hormuzd A Katki; Stephanie A Kovalchik; Martin Tammemagi; Christine D Berg; Neil E Caporaso; Tom L Riley; Mary Korch; Gerard A Silvestri
Journal:  N Engl J Med       Date:  2013-07-18       Impact factor: 91.245

7.  Validation of two models to estimate the probability of malignancy in patients with solitary pulmonary nodules.

Authors:  E M Schultz; G D Sanders; P R Trotter; E F Patz; G A Silvestri; D K Owens; M K Gould
Journal:  Thorax       Date:  2007-10-26       Impact factor: 9.139

Review 8.  Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines.

Authors:  Michael K Gould; Jessica Donington; William R Lynch; Peter J Mazzone; David E Midthun; David P Naidich; Renda Soylemez Wiener
Journal:  Chest       Date:  2013-05       Impact factor: 9.410

9.  UK Lung Screen (UKLS) nodule management protocol: modelling of a single screen randomised controlled trial of low-dose CT screening for lung cancer.

Authors:  D R Baldwin; S W Duffy; N J Wald; R Page; D M Hansell; J K Field
Journal:  Thorax       Date:  2011-02-11       Impact factor: 9.139

10.  The LLP risk model: an individual risk prediction model for lung cancer.

Authors:  A Cassidy; J P Myles; M van Tongeren; R D Page; T Liloglou; S W Duffy; J K Field
Journal:  Br J Cancer       Date:  2007-12-18       Impact factor: 7.640

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