Literature DB >> 29661918

Characteristics of new solid nodules detected in incidence screening rounds of low-dose CT lung cancer screening: the NELSON study.

Joan E Walter1, Marjolein A Heuvelmans1,2, Geertruida H de Bock3, Uraujh Yousaf-Khan4, Harry J M Groen5, Carlijn M van der Aalst4, Kristiaan Nackaerts6, Peter M A van Ooijen1, Harry J de Koning4, Rozemarijn Vliegenthart1, Matthijs Oudkerk1.   

Abstract

PURPOSE: New nodules after baseline are regularly found in low-dose CT lung cancer screening and have a high lung cancer probability. It is unknown whether morphological and location characteristics can improve new nodule risk stratification by size.
METHODS: Solid non-calcified nodules detected during incidence screening rounds of the randomised controlled Dutch-Belgian lung cancer screening (NELSON) trial and registered as new or previously below detection limit (15 mm3) were included. A multivariate logistic regression analysis with lung cancer as outcome was performed, including previously established volume cut-offs (<30 mm3, 30-<200 mm3 and ≥200 mm3) and nodule characteristics (location, distribution, shape, margin and visibility <15 mm3 in retrospect).
RESULTS: Overall, 1280 new nodules were included with 73 (6%) being lung cancer. Of nodules ≥30 mm3 at detection and visible <15 mm3 in retrospect, 22% (6/27) were lung cancer. Discrimination based on volume cut-offs (area under the receiver operating characteristic curve (AUC): 0.80, 95% CI 0.75 to 0.84) and continuous volume (AUC: 0.82, 95% CI 0.77 to 0.87) was similar. After adjustment for volume cut-offs, only location in the right upper lobe (OR 2.0, P=0.012), central distribution (OR 2.4, P=0.001) and visibility <15 mm3 in retrospect (OR 4.7, P=0.003) remained significant predictors for lung cancer. The Hosmer-Lemeshow test (P=0.75) and assessment of bootstrap calibration curves indicated adequate model fit. Discrimination based on the continuous model probability (AUC: 0.85, 95% CI 0.81 to 0.89) was superior to volume cut-offs alone, but when stratified into three risk groups (AUC: 0.82, 95% CI 0.78 to 0.86), discrimination was similar.
CONCLUSION: Contrary to morphological nodule characteristics, growth-independent characteristics may further improve volume-based new nodule lung cancer prediction, but in a three-category stratification approach, this is limited. TRIAL REGISTRATION NUMBER: ISRCTN63545820; pre-results. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Entities:  

Keywords:  lung cancer

Mesh:

Year:  2018        PMID: 29661918     DOI: 10.1136/thoraxjnl-2017-211376

Source DB:  PubMed          Journal:  Thorax        ISSN: 0040-6376            Impact factor:   9.139


  10 in total

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Journal:  Lung       Date:  2019-07-11       Impact factor: 2.584

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3.  Primary solid lung cancerous nodules with different sizes: computed tomography features and their variations.

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Review 5.  Lung cancer risk prediction models based on pulmonary nodules: A systematic review.

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Journal:  Thorac Cancer       Date:  2022-02-08       Impact factor: 3.500

6.  Deep Learning-based Artificial Intelligence Improves Accuracy of Error-prone Lung Nodules.

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Journal:  Int J Med Sci       Date:  2022-03-06       Impact factor: 3.738

7.  A Novel Deep Learning Model to Distinguish Malignant Versus Benign Solid Lung Nodules.

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8.  Development and clinical application of deep learning model for lung nodules screening on CT images.

Authors:  Sijia Cui; Shuai Ming; Yi Lin; Fanghong Chen; Qiang Shen; Hui Li; Gen Chen; Xiangyang Gong; Haochu Wang
Journal:  Sci Rep       Date:  2020-08-12       Impact factor: 4.379

9.  Executive Summary: Screening for Lung Cancer: Chest Guideline and Expert Panel Report.

Authors:  Peter J Mazzone; Gerard A Silvestri; Lesley H Souter; Tanner J Caverly; Jeffrey P Kanne; Hormuzd A Katki; Renda Soylemez Wiener; Frank C Detterbeck
Journal:  Chest       Date:  2021-07-13       Impact factor: 9.410

10.  Screening for Lung Cancer: CHEST Guideline and Expert Panel Report.

Authors:  Peter J Mazzone; Gerard A Silvestri; Lesley H Souter; Tanner J Caverly; Jeffrey P Kanne; Hormuzd A Katki; Renda Soylemez Wiener; Frank C Detterbeck
Journal:  Chest       Date:  2021-07-13       Impact factor: 9.410

  10 in total

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