Literature DB >> 28625647

Quantification of growth patterns of screen-detected lung cancers: The NELSON study.

Marjolein A Heuvelmans1, Rozemarijn Vliegenthart2, Harry J de Koning3, Harry J M Groen4, Michel J A M van Putten5, Uraujh Yousaf-Khan6, Carla Weenink7, Kristiaan Nackaerts8, Pim A de Jong9, Matthijs Oudkerk10.   

Abstract

OBJECTIVES: Although exponential growth is assumed for lung cancer, this has never been quantified in vivo. Aim of this study was to evaluate and quantify growth patterns of lung cancers detected in the Dutch-Belgian low-dose computed tomography (CT) lung cancer screening trial (NELSON), in order to elucidate the development and progression of early lung cancer.
MATERIALS AND METHODS: Solid lung nodules found at ≥3 CT examinations before lung cancer diagnosis were included. Lung cancer volume (V) growth curves were fitted with a single exponential, expressed as V=V1 exp(t/τ), with t time from baseline (days), V1 estimated baseline volume (mm3), and τ estimated time constant. The R2 coefficient of determination was used to evaluate goodness of fit. Overall volume-doubling time for the individual lung cancer is given by τ*log(2).
RESULTS: Forty-seven lung cancers in 46 participants were included. Forty participants were male (87.0%); mean age was 61.7 years (standard deviation, 6.2 years). Median nodule size at baseline was 99.5mm3 (IQR: 46.8-261.8mm3). Nodules were followed for a median of 770 days (inter-quartile range: 383-1102 days) before lung cancer diagnosis. One cancer (2.1%) was diagnosed after six CT examinations, six cancers (12.8%) were diagnosed after five CTs, 14 (29.8%) after four CTs, and 26 cancers (55.3%) after three CTs. Lung cancer growth could be described by an exponential function with excellent goodness of fit (R2 0.98). Median overall volume-doubling time was 348 days (inter-quartile range: 222-492 days).
CONCLUSION: This study based on CT lung cancer screening provides in vivo evidence that growth of cancerous small-to-intermediate sized lung nodules detected at low-dose CT lung cancer screening can be described by an exponential function such as volume-doubling time.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Growth charts; Lung neoplasms; Mass screening; Pulmonary nodule

Mesh:

Year:  2017        PMID: 28625647     DOI: 10.1016/j.lungcan.2017.02.021

Source DB:  PubMed          Journal:  Lung Cancer        ISSN: 0169-5002            Impact factor:   5.705


  14 in total

1.  In vivo growth of 60 non-screening detected lung cancers: a computed tomography study.

Authors:  Onno M Mets; Kaman Chung; Pieter Zanen; Ernst T Scholten; Wouter B Veldhuis; Bram van Ginneken; Mathias Prokop; Cornelia M Schaefer-Prokop; Pim A de Jong
Journal:  Eur Respir J       Date:  2018-04-12       Impact factor: 16.671

2.  Pulmonary nodules measurements in CT lung cancer screening.

Authors:  Marjolein A Heuvelmans; Matthijs Oudkerk
Journal:  J Thorac Dis       Date:  2018-06       Impact factor: 2.895

Review 3.  Appropriate screening intervals in low-dose CT lung cancer screening.

Authors:  Marjolein A Heuvelmans; Matthijs Oudkerk
Journal:  Transl Lung Cancer Res       Date:  2018-06

4.  Lung Cancer Stage Shift as a Result of COVID-19 Lockdowns in New York City, a Brief Report.

Authors:  Nathan Mynard; Ashish Saxena; Alexandra Mavracick; Jeffrey Port; Benjamin Lee; Sebron Harrison; Oliver Chow; Jonathan Villena-Vargas; Ronald Scheff; Giuseppe Giaccone; Nasser Altorki
Journal:  Clin Lung Cancer       Date:  2021-08-29       Impact factor: 4.840

5.  Challenges and research opportunities for lung cancer screening in China.

Authors:  Zixing Wang; Yuyan Wang; Yao Huang; Fang Xue; Wei Han; Yaoda Hu; Lei Wang; Wei Song; Jingmei Jiang
Journal:  Cancer Commun (Lond)       Date:  2018-06-07

6.  Early detection of lung cancer in Czech high-risk asymptomatic individuals (ELEGANCE): A study protocol.

Authors:  Lukas Lambert; Lenka Janouskova; Matej Novak; Bianka Bircakova; Zuzana Meckova; Jiri Votruba; Pavel Michalek; Andrea Burgetova
Journal:  Medicine (Baltimore)       Date:  2021-02-05       Impact factor: 1.817

Review 7.  Pulmonary nodule radiological diagnostic algorithm in lung cancer screening.

Authors:  Katarzyna Dziadziuszko; Edyta Szurowska
Journal:  Transl Lung Cancer Res       Date:  2021-02

8.  Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics.

Authors:  Mingyu Tan; Weiling Ma; Yingli Sun; Pan Gao; Xuemei Huang; Jinjuan Lu; Wufei Chen; Yue Wu; Liang Jin; Lin Tang; Kaiming Kuang; Ming Li
Journal:  Front Oncol       Date:  2021-04-15       Impact factor: 6.244

9.  Dynamic Observation of Lung Nodules on Chest CT Before Diagnosis of Early Lung Cancer.

Authors:  Qiaodan Du; Jia Peng; Xiuyu Wang; MingFang Ji; Yuting Liao; Binghang Tang
Journal:  Front Oncol       Date:  2022-03-09       Impact factor: 6.244

10.  The growth feature and its diagnostic value for benign and malignant pulmonary nodules met in routine clinical practice.

Authors:  Rui Zhang; Panwen Tian; Zhixin Qiu; Yiying Liang; Weimin Li
Journal:  J Thorac Dis       Date:  2020-05       Impact factor: 3.005

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