University of Groningen Pulmonary Nodules: 2D versus 3D evaluation in lung cancer screening Han, Daiwei

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Pulmonary Nodules: 2D versus 3D evaluation in lung cancer screening

Han, Daiwei

DOI:

10.33612/diss.172563513

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Publication date: 2021

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Han, D. (2021). Pulmonary Nodules: 2D versus 3D evaluation in lung cancer screening. University of Groningen. https://doi.org/10.33612/diss.172563513

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General introduction

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Lung cancer is the main cause of cancer related mortality worldwide (1). Despite improvement in the treatment of lung cancer the curation rate is still poor. The five-year survival rate for a localized non-small cell lung cancer (NSCLC) is 61% while for distant NSCLC the 5 year survival rate is only 6% (2). Therefore, early detection is crucial at reducing lung cancer related mortality.

The introduction of thin slice low-dose computed tomography (LDCT) has allowed the detection of sub-centimeter sized nodules, which could not be reliably found by chest radiography. The National Lung Screening Trial (NLST), the largest lung cancer screening trial to date, compared the use of CT and chest radiography and has concluded that annual screening by low-dose CT could reduce lung cancer specific mortality by 20% (3). Other screening studies, mostly conducted in Europe, compared LDCT screening with no screening. Recently published result of the largest European lung cancer screening trial, the NELSON trial, has provided further evidence that LDCT screening can significantly reduce lung cancer specific mortality (4).

In the United States, based on the results of the NLST, most guidelines now recommend the use of LDCT for screening of high-risk individuals (5). In Europe, leading experts in the field of lung cancer screening have put forth joint statements recommending LDCT screening (6,7). However, the inclusion of lung cancer into the existing EU guidelines on screening and diagnosis of a list of cancers (breast, cervical and colorectal cancer) is still awaited.

A major drawback of the NLST was the high prevalence of false positive results (26.6%). One of the main differences between the NLST and most of European lung cancer trials is the nodule assessment method. In the NLST, diameter-based assessment was used. The use of a volume-based nodule management strategy in the NELSON trial has led to significantly less false-positives (1.7%) compared to the results reported by the NLST. Currently the use of volumetric nodule assessment for lung cancer screening is supported by most of recommendations in Europe (8).

Several subtypes of lung nodules have been studied previously. These consist of both solid and subsolid nodules (including ground glass opacities). In the NELSON trial, the baseline malignancy probability of solid nodules and subsolid nodules (including ground glass opacity) were 2.5% and 10.6%, respectively (9,10). Perifissural nodules (PFN) are a sub-category of solid pulmonary nodules, representing 20% of all solid nodules found in baseline screening, and are thought to represent benign intrapulmonary lymph nodes (11). The automatic detection exclusion of PFN in a screening setting using could allow increased efficiency of lung cancer screening.

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GENERAL INTRODUCTION

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In the past decade, great advancements in computer science has benefitted the field of Radiology. Advances in computer algorithms has provided new post processing, and diagnostic techniques which could be utilized to optimize nodule management in CT-based lung cancer screening.

The main goal of this thesis is to assemble evidence to support the use of volumetric management of pulmonary nodules in LDCT lung cancer screening and to evaluate the influence of CT post processing techniques on the assessment of pulmonary nodules. Furthermore, the malignancy probability of new PFNs found in the NELSON trial is evaluated, eventually leading to the development and evaluation of a novel automatic PFN classifier to rule out PFNs.

OUTLINE OF THE THESIS

The thesis is structured in to three sections.

The first section contains chapter 1-5. While chapter one provides the outline of this thesis, Chapter 2 reviews current European lung cancer screening trials and compares lung cancer screening recommendations from experts and societies in order to provide a bird’s eye view of current progress in Europe. In Chapter 3, current literature on both diameter and volume LDCT screening were reviewed. The results from this chapter provides the basis for the use of volumetry for nodule evaluation in this thesis. As a nodule has only one volume but infinite diameters, it can be hypothesized that the reliability of diameter assessment for asymmetrical nodules could be poorer than volumetric assessment. Therefore, in chapter 4, the influence of nodule margin on volume- and diameter-based evaluation of lung nodules was evaluated.

As lung cancer screening could soon become more widespread worldwide, the risks caused by its ionizing radiation has become a concern. ALARA (an acronym for as low as reasonably achievable) is a principle in radioprotection that strives to reduce ionizing radiation exposure when it must be applied to humans or animals. CT images produced using reduced radiation dose have increased image noise which could hinder the performance of radiologists in assessing pulmonary nodules. Chapters 5 focuses on the evaluation of an iterative reconstruction techniques for ultralow-dose CT and its influence on lung nodule assessment, which could allow the possibility of lung cancer screening at a tenth of radiation dose compared to the current lung cancer screening. New solid nodules have been shown to have higher malignancy probability than nodules

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from baseline screening round (12). PFNs, being a sub-category of solid nodules, have been shown to be benign in both screening and clinical populations. Whether new PFNs found in incidence screening rounds also present with increased malignancy probability is not known. The second section contains chapters 6-8. Chapter 6 investigates malignancy and the classification of new PFN nodules found in the NELSON incidence screening rounds. Chapter 7 examines current evidence on PFNs and their benign nature as well as discusses future research direction. Chapter 8 focuses on the development of a novel deep-learning based classifier to automatically classify PFNs.

The fourth section contains chapter 9 and 10. Chapter 9 discusses the results of this thesis and chapter 10 provides the summary of this thesis.

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BIBLIOGRAPHY

1. BW S, CP W. World Cancer Report 2014 [Internet]. [cited 2020 Nov 19]. Available from: https://publications.iarc.fr/Non-Series-Publications/World-Cancer-Reports/ World-Cancer-Report-2014

2. Lung Cancer Survival Rates | 5-Year Survival Rates for Lung Cancer [Internet]. [cited 2020 Nov 20]. Available from: https://www.cancer.org/cancer/lung-cancer/ detection-diagnosis-staging/survival-rates.html

3. Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. New England Journal of Medicine. 2011 Aug 4;365(5):395–409.

4. de Koning HJ, van der Aalst CM, de Jong PA, Scholten ET, Nackaerts K, Heuvelmans MA, et al. Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial. New England Journal of Medicine. 2020 Feb 6;382(6):503–13.

5. Wood DE, Kazerooni EA, Baum SL, Eapen GA, Ettinger DS, Hou L, et al. Lung Cancer Screening, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology. Journal of the National Comprehensive Cancer Network. 2018 Apr 1;16(4):412–41.

6. Oudkerk M, Devaraj A, Vliegenthart R, Henzler T, Prosch H, Heussel CP, et al. European position statement on lung cancer screening. The Lancet Oncology. 2017 Dec 1;18(12):e754–66.

7. Kauczor H-U, Baird A-M, Blum TG, Bonomo L, Bostantzoglou C, Burghuber O, et al. ESR/ERS statement paper on lung cancer screening. European Respiratory Journal [Internet]. 2020 Feb 1 [cited 2020 Nov 15];55(2). Available from: https://erj. ersjournals.com/content/55/2/1900506

8. Han D, Heuvelmans MA, Vliegenthart R, Rook M, Dorrius MD, Oudkerk M. An Update on the European Lung Cancer Screening Trials and Comparison of Lung Cancer Screening Recommendations in Europe. Journal of Thoracic Imaging. 2019 Jan;34(1):65–71.

9. Horeweg N, van Rosmalen J, Heuvelmans MA, van der Aalst CM, Vliegenthart R, Scholten ET, et al. Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening. The Lancet Oncology. 2014 Nov 1;15(12):1332–41.

10. Scholten ET, Jong PA de, Hoop B de, Klaveren R van, Vorst S van A de, Oudkerk M, et al. Towards a close computed tomography monitoring approach for screen detected subsolid pulmonary nodules? European Respiratory Journal. 2015 Mar 1;45(3):765–73.

11. de Hoop B, van Ginneken B, Gietema H, Prokop M. Pulmonary Perifissural Nodules on CT Scans: Rapid Growth Is Not a Predictor of Malignancy. Radiology. 2012 Nov 1;265(2):611–6.

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RB, et al. Occurrence and lung cancer probability of new solid nodules at incidence screening with low-dose CT: analysis of data from the randomised, controlled NELSON trial. The Lancet Oncology. 2016 Jul 1;17(7):907–16.

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2D versus 3D assessment of

pulmonary nodules in lung cancer screening

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