Literature DB >> 18598934

Is our natural-history model of lung cancer wrong?

Peter B Bach1.   

Abstract

For screening to be successful in lung cancer, programmes need to identify a precursor of fatal disease that is highly treatable. This precursor is currently presumed to be early-stage lung cancer, and thus our current efforts in lung-cancer screening have focused on finding, and treating, early-stage lung cancer. In other words, our current natural-history model of lung cancer is based on the notion that for a period before a lung cancer becomes advanced and fatal, it is localised and treatable. Empirical findings emerging from studies of lung-cancer screening suggest this notion to be incorrect. These studies all suggest that early-stage histologically confirmed lung cancers identified by screening are not precursors of advanced or incurable disease. If true, these findings have potentially important implications for the development and assessment of new lung-cancer screening approaches.

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Year:  2008        PMID: 18598934     DOI: 10.1016/S1470-2045(08)70176-1

Source DB:  PubMed          Journal:  Lancet Oncol        ISSN: 1470-2045            Impact factor:   41.316


  13 in total

1.  Classification of malignant and benign tumors of the lung by infrared spectral histopathology (SHP).

Authors:  Ali Akalin; Xinying Mu; Mark A Kon; Ayşegül Ergin; Stan H Remiszewski; Clay M Thompson; Dan J Raz; Max Diem; Benjamin Bird; Miloš Miljković
Journal:  Lab Invest       Date:  2015-02-09       Impact factor: 5.662

Review 2.  The pulmonary nodule: clinical and radiological characteristics affecting a diagnosis of malignancy.

Authors:  L Cardinale; F Ardissone; S Novello; M Busso; F Solitro; M Longo; D Sardo; M Giors; C Fava
Journal:  Radiol Med       Date:  2009-05-29       Impact factor: 3.469

Review 3.  Imaging-based screening: maximizing benefits and minimizing harms.

Authors:  Jessica C Germino; Joann G Elmore; Ruth C Carlos; Christoph I Lee
Journal:  Clin Imaging       Date:  2015-06-12       Impact factor: 1.605

4.  Comparing the benefits of screening for breast cancer and lung cancer using a novel natural history model.

Authors:  Ray S Lin; Sylvia K Plevritis
Journal:  Cancer Causes Control       Date:  2011-11-25       Impact factor: 2.506

5.  Lung cancers diagnosed at annual CT screening: volume doubling times.

Authors:  Claudia I Henschke; David F Yankelevitz; Rowena Yip; Anthony P Reeves; Ali Farooqi; Dongming Xu; James P Smith; Daniel M Libby; Mark W Pasmantier; Olli S Miettinen
Journal:  Radiology       Date:  2012-03-27       Impact factor: 11.105

6.  MicroRNA signatures in tissues and plasma predict development and prognosis of computed tomography detected lung cancer.

Authors:  Mattia Boeri; Carla Verri; Davide Conte; Luca Roz; Piergiorgio Modena; Federica Facchinetti; Elisa Calabrò; Carlo M Croce; Ugo Pastorino; Gabriella Sozzi
Journal:  Proc Natl Acad Sci U S A       Date:  2011-02-07       Impact factor: 11.205

7.  Addressing overdiagnosis and overtreatment in cancer: a prescription for change.

Authors:  Laura J Esserman; Ian M Thompson; Brian Reid; Peter Nelson; David F Ransohoff; H Gilbert Welch; Shelley Hwang; Donald A Berry; Kenneth W Kinzler; William C Black; Mina Bissell; Howard Parnes; Sudhir Srivastava
Journal:  Lancet Oncol       Date:  2014-05       Impact factor: 41.316

8.  Angiogenin and vascular endothelial growth factor expression in lungs of lung cancer patients.

Authors:  Ales Rozman; Mira Silar; Mitja Kosnik
Journal:  Radiol Oncol       Date:  2012-11-09       Impact factor: 2.991

9.  Circulating miRNA signature for early diagnosis of lung cancer.

Authors:  Pei-Ying Lin; Pan-Chyr Yang
Journal:  EMBO Mol Med       Date:  2011-07-08       Impact factor: 12.137

10.  Gene Signatures Stratify Computed Tomography Screening Detected Lung Cancer in High-Risk Populations.

Authors:  Jiangting Hu; Mattia Boeri; Gabriella Sozzi; Dongxia Liu; Alfonso Marchianò; Luca Roz; Giuseppe Pelosi; Kevin Gatter; Ugo Pastorino; Francesco Pezzella
Journal:  EBioMedicine       Date:  2015-07-08       Impact factor: 8.143

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