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Matthijs Oudkerk
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ES08 - Critical Concerns in Screening (ID 11)
- Event: WCLC 2019
- Type: Educational Session
- Track: Screening and Early Detection
- Presentations: 1
- Now Available
- Moderators:Ugo Pastorino, John Kirkpatrick Field
- Coordinates: 9/08/2019, 13:30 - 15:00, Copenhagen (1980)
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ES08.02 - Nodule Growth Assessment (Now Available) (ID 3192)
13:30 - 15:00 | Presenting Author(s): Matthijs Oudkerk
- Abstract
- Presentation
Abstract
In the past 15 years several lung cancer screening trials have been performed in Europe1. Experts involved in these trials recently published a position paper in the Lancet Oncology2.
Several conclusions and recommendations were drawn to enable a smooth and timely implementation of lung cancer screening in Europe. The experts made a strong and conditional argument on the methodology of lung cancer screening by low-dose computed tomography (CT). Quality control by regular CT phantom testing enabling standardization of CT data acquisition as well as benchmarking of CT software post processing and data analysis are mandatory. It is promoted to execute the CT screening Q/A control through national reference centers similar to the organizational structure of breast screening programs. In this manner the CT screening programs can be implemented in a responsible way avoiding the detrimental side effects of too high false positive CT outcome rates on the one hand and keeping up the most effective lung cancer early detection rate on the other hand.
Apart from the acquisition protocol, the CT lung nodule analysis methodology is a critical factor for successful implementation of CT lung cancer screening 3. The diameter based NLST protocol used a 4 mm threshold (~ 30 mm3) as significant suspicion for malignancy. This approach yielded a positive rate of approximately 27% in the baseline round with a very low positive predictive value for lung cancer of 3.8%4. The NELSON study introduced a nodule volume analysis and a volume doubling time methodology with 2 CT measurements with a 3-month interval to calculate the volume doubling time as a biomarker for growth rate in indeterminate nodules. This approach resulted in a 2.6% positive rate in the baseline round with a high positive predictive value for lung cancer of approximately 36%, which is within the criteria needed for lung cancer screening implementation3. In the meantime, a ten times higher threshold of 8 mm (~300 mm3) was recommended to correct the high false positive rate of the NLST diameter methodology by several international societies5. After the publications of the NELSON data on increased lung cancer probability in baseline nodules at a threshold of 100 mm3the US guideline recommendations shifted from 300 mm3to 100 mm3(~ 6mm)6. A direct comparison of diameter and volume protocols cannot be performed through the assumption that all nodules are spherical. While this approach was chosen in a recent publication of the IELCAP investigators7, even the slightest correction for the assumption of sphericity reveals the substantial inferiority of diameter protocols. At follow-up CT examination at annual incident screen new nodules represent a high lung cancer probability at lower volumes than at baseline screen8,9. The upper threshold is at 200 mm3as indication for further clinical workup while new nodule at incident screen between the 30-200 mm3are classified as indeterminate and need a repeat scan at 3 months to calculate the volume doubling time. Lung cancer screening should be integrated in a defined national program and therefore opportunistic screening is not recommended. Calcium scoring as a screening methodis not yet established as a validated tool for early detection of coronary artery disease and the outcomes of the ROBINSCA study (risk or benefit in screening for cardiovascular disease www.robinsca.nl) are being awaited. Thus, so far, it is not indicated as a combined clinical routine screening methodology since ECG triggering is mandatory10. Non-triggered CT coronary artery calcium scoring will result in high false negative percentages. A lower CT radiation exposure threshold at a DLP of 50 mGy or 0.6 mSv is defined to assure the calibration of the quantitative imaging biomarkers for lung nodule detection. Lower radiation doses will induce false negative and unreliable growth rate results.
References
1 An update on the European Lung Cancer Screening Trials and Comparison of the Lung Cancer Screening Recommendations in Europe . Han et alJournal of Thoracic Imaging 2019; 34(1): 65–71.
2 European position statement on lung cancer screening Oudkerk M Devaraj ALancet Oncology 2017 Dec;18(12):e754-e766.
3 Management of lung nodules detected by volume CT scanning van Klaveren R Oudkerk M et al N Engl J Med. 2009 Dec 3;361(23):2221-9.
4 Reduced lung-cancer mortality with low-dose computed tomographic screening Aberle DR et al N Engl J Med 2011 Aug 4;365(5):395-409.
5 CT screening for lung cancer: alternative definitions of positive test result based on the national lung screening trial and international early lung cancer action program databases Yip R Henschke CI Radiology 2014 Nov;273(2):591-6.
6 Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017 Mac Mahon H Radiology: Volume 284: Number 1—July 2017
7 CT screening for lung cancer: comparison of three baseline screening protocol Henschke CI et al Eur Rad 2018 Dec 3
8 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 Walter JE Heuvelmans MA et al Lancet Oncology 2016 Jul;17(7):907-916.
9.Persisting new nodules in incidence rounds of the NELSON CT lungcancer screening study Walter JE Heuvelmnans MA Thorax. 2018 Dec 27
10 Can nontriggered thoracic CT be used for coronary artery calcium scoring? A phantom study Xueqian Xie et al Medical Physics, Vol. 40, No. 8, August 2013
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OA06 - Refining Lung Cancer Screening (ID 131)
- Event: WCLC 2019
- Type: Oral Session
- Track: Screening and Early Detection
- Presentations: 1
- Now Available
- Moderators:Tomasz Grodzki, Lluis Esteban Tejero
- Coordinates: 9/09/2019, 11:00 - 12:30, Hilton Head (1978)
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OA06.05 - Evaluation of a Deep Learning-Based Automatic Classifier for the Classification of Perifissural Nodules (Now Available) (ID 1928)
11:00 - 12:30 | Author(s): Matthijs Oudkerk
- Abstract
- Presentation
Background
Perifissural nodules (PFNs) comprise approximately 20% of screening-detected nodules and are almost certainly benign. Automatic PFN classification could therefore reduce the number of follow-up procedures required for nodule work-up. Prior work has shown some success in AI classification with limited datasets. Here we evaluate the performance of a new deep convolutional neural network (CNN) for PFN classification, trained on a dataset of nodules retrospectively collected from multiple European centers, including validation on an independent reader-study dataset.
Method
Data (1103 Patients, 1557 unique nodules and 3320 nodule images) were collected from three centers in the UK and the Netherlands. Each nodule was categorized into subtypes, including “PFN”, by on-site radiologists. Labels were reviewed centrally, overseen by a single clinician to ensure consistency between sites.
A CNN classifier was trained to produce a score that classifies nodules as (typical) PFN or not, using five-fold cross validation. The PFN classifier was developed by “transfer learning” from an existing benign-vs-malignant AI trained on the US National Lung Screening Trial.
To compare the CNN with human performance, independent validation was performed on a separate dataset of 158 benign patients (196 nodules/nodule images) from two of the sites. Three readers (two radiologists and a radiology resident) were asked to label each nodule as typical PFN, atypical PFN, or non-PFN. To match the AI training procedure, only the typical-PFN labels were used in the reader study, and compared to atypical/non-PFN classified nodules.
Model performance was evaluated by area under the ROC curve (AUC). For the independent validation, Cohen’s kappa was used to measure both the model’s agreement with reader consensus (at least 2 in agreement) and inter-reader agreement. For Cohen’s kappa calculations the CNN score was binarized using a threshold determined from the internal validation data.
Result
The mean cross-validated AUC on the internal dataset was 92% (95% CI = 90.6–92.9). For the independent dataset, the classifier labelled 61/196 (31%) as typical PFNs, and reader consensus gave 45/196 (23%). Versus reader consensus, the AUC of the CNN on the reader-study dataset was 96% (95% CI 93.3–98.4). Both the classifier–reader agreement [(k=0.74) 90%] and the inter-reader agreement [(k=0.64–0.79) 88%-92%] were substantial.
Conclusion
The performance of the PFN classifier is similar to that of radiologists and is within the inter-reader variability of radiologists. This demonstrates the potential utility of CNN-based systems for automatic PFN classification.
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P1.11 - Screening and Early Detection (ID 177)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Screening and Early Detection
- Presentations: 2
- Moderators:
- Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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P1.11-27 - Computed Tomography Screening for Early Lung Cancer, COPD and Cardiovascular Disease in Shanghai: Rationale and Design of a Population-Based Comparative Study (ID 1863)
09:45 - 18:00 | Author(s): Matthijs Oudkerk
- Abstract
Background
Volume-based management for lung nodules is associated with a lower rate of unnecessary referral for further work up as compared to diameter-based management in European population. Screening for chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD), in addition to lung cancer, may significantly increase the benefits of lung cancer low-dose computed tomography (CT) screening. While this is unclear in Chinese population. The aim of this study is to assess the diagnostic performance of volume-based lung nodule management for lung cancer CT screening as compared to diameter-based management, and to improve the effectiveness of CT screening for COPD and CVD based on quantitative measurement of CT imaging biomarkers in a Chinese screening setting.
Method
A comparative population-based study is ongoing, that will include 10,000 asymptomatic participants between 40 and 74 years old from Shanghai urban population in China.
Result
Participants will be randomized into the intervention and control groups and will undergo a low-dose chest CT scan at baseline and one year after baseline. NELCIN-B3 protocol will be applied in the intervention group. It recommends management of detected solid and part-solid lung nodules based on the volume and volume doubling time (VDT) of a lung nodule. The imaging biomarkers for COPD and CVD, such as emphysema score, bronchial wall thickness from inspiratory and expiratory chest CT scan, and coronary calcium score from ECG-triggered cardiac CT scan will be evaluated. In addition data on laboratory parameters and lung function test will be collected. The participants in the control group will be managed according to the standard hospital protocol based on visual assessment of the CT images. It recommends management of detected lung nodules based on the diameter according to the NCCN Clinical Practice Guideline in Oncology for Lung Cancer Screening. Epidemiological data (eg., risk factors) will be collected through questionnaires for all participants. Four years after the initial assessment the incidence of the three diseases will be evaluated. The design is shown in Figure 1.
The unnecessary referral rate will be compared between the NELCIN-B3 and standard protocol for early detected lung nodules management. The effectiveness of quantitative measurement of CT imaging biomarkers for early detection of lung cancer, COPD and CVD will be evaluated.
Conclusion
We expect that the quantitative assessment of the CT imaging biomarkers will reduce the number of unnecessary referrals for early detected lung nodules and improve the early detection of COPD and CVD in Chinese urban populations.
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P1.11-32 - The UKLS Nodule Risk Model (UKLS-NRM): Utilising Nodule Volumetry (ID 728)
09:45 - 18:00 | Author(s): Matthijs Oudkerk
- Abstract
Background
Estimating the clinical probability of malignancy in patients with pulmonary nodules will facilitate early diagnosis, determine optimum patient management strategies and reduce overall costs.
Currently there are two risk prediction models, which are recommended by BTS; the Brock University model, for nodules ≥300mm3 or ≥8mm diameter, and where the risk is estimated at >10%, the Herder model after PET-CT. However, none of these models employ volumetry and all were developed for use at baseline
Method
The UK Lung Cancer Screening (UKLS) trial data were analysed, utilising multivariable logistic regression models to identify independent predictors and develop a parsimonious model to estimate the probability of lung cancer in lung nodules detected at baseline, three month and twelve months repeat screening.
Result
Conclusion
1994 UKLS participants had a CT scan; 1013 had a total of 5063 lung nodules and 52 (2.6%) developed lung cancer during a 4 year median follow-up. Covariates that predict lung cancer included: female gender, asthma, bronchitis, asbestos exposure, history of previous cancer, early and late onset of family history of lung cancer, smoking duration, forced vital capacity, nodule type and volume. The final model had excellent discrimination; area under the receiver-operating characteristic curve (AUC [95% CI] = 0.885 [0.880 to 0.889]). Internal validation indicated that the model will discriminate well when applied to new data (optimism-corrected AUC = 0.882 [0.848-.907]). The risk model had a good calibration (goodness-of-fit χ 8.13, P = 0.42).
The UKLS Nodule Risk Model (UKLS-NRM) estimates the probability of lung cancer in nodules detected at baseline, three months and twelve months from baseline. The model is based on readily available, strong, and plausible covariates that have been implicated in the aetiology of lung cancer. The application of UKLS-NRM has the potential to be used in both the research and clinical setting.
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P2.10 - Prevention and Tobacco Control (ID 176)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Prevention and Tobacco Control
- Presentations: 1
- Moderators:
- Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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P2.10-16 - Lung Cancer Occurrence Attributable to Passive Smoking Among Never Smokers in China: A Systematic Review and Meta-Analysis (ID 1946)
10:15 - 18:15 | Author(s): Matthijs Oudkerk
- Abstract
Background
Quantifying lung cancer occurrence due to passive smoking is a necessary step for policy makers. The aim of this study is to estimate the proportion of lung cancer cases attributable to passive smoking among never smokers in China.
Method
A systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. We comprehensively searched six databases up to July 2019 for original observational studies in both English and Chinese languages. Studies that reported relative risks (RR) or odds ratios (OR) for lung cancer occurrence associated with passive smoking in Chinese never smokers were included. For each selected publication, two reviewers assessed publications in English and Chinese independently and assessed the quality of included studies using the Newcastle-Ottawa Scale (NOS). Any disagreements encountered were settled through a consensus. The population attributable fraction (PAF) was calculated using the combined proportion of lung cancer cases exposed to passive smoking and the pooled OR yielded from meta-analysis under the assumption of homogeneity.
Result
Thirty-one studies (all designed as case-control) were identified, comprising 9,614 cases and 13,093 controls. The overall proportion of lung cancer cases among never smokers attributable to passive smoking was estimated at 20.5% (95% CI: 16.0% - 24.7%), based on the proportion of lung cancer cases exposed to passive smoking (61.6%) and the pooled OR for passive smoking and lung cancer risk of 1.50 (95% CI: 1.35-1.76). ). The PAF was 15.5% (95%CI: 9.3%-21.0%) based on population-based studies and was 22.7% (95%CI: 16.6%-28.0%) based on hospital-based studies. The subgroup analysis (Table 1) showed that the PAF was similar in non-smoking men (20.9%) and women (21.3%). The proportion of lung cancer cases attributable to household passive smoking was much higher than workplace passive smoking (19.2% vs 10.5%).
Table 1 Population attributable fraction (PAF) of lung cancer caused by passive smoking among never smokers in subgroups subgroup
No. of studies
NOS score
cases
cases_
exposed
cases-exposed(%)
Pooled OR
95%CI
I2
P
PAF
95%CI
Study year
before 2000
13
6.23
2600
1639
63.04%
1.70
1.43-2.03
48.40%
0.025
25.96%
18.96% - 31.99%
after 2000
20
5.57
7000
4348
62.11%
1.50
1.31-1.72
67.60%
<0.001
20.70%
14.70% - 26.00%
Gender
men
9
6.22
809
473
58.47%
1.55
1.10-2.19
62.00%
0.007
20.75%
5.32% - 31.77%
women
26
5.77
7248
4803
66.27%
1.49
1.34-1.66
47.80%
0.004
21.79%
16.81% - 26.35%
Region
mainland
23
5.83
6468
3925
60.68%
1.55
1.35-1.79
67.60%
<0.001
21.53%
15.73% - 26.78%
non-mainland*
10
5.80
3132
2062
65.84%
1.61
1.36-1.90
49.00%
0.039
24.94%
17.43% - 31.19%
Exposure age
childhood/adulthood
5
6.40
1219
972
79.74%
1.50
1.14-1.96
52.70%
0.078
26.58%
9.79% - 39.06%
childhood
4
6.50
654
315
48.17%
1.50
1.08-2.10
43.30%
0.152
16.06%
3.57% - 25.23%
adulthood
5
6.60
835
517
61.92%
1.65
1.05-2.58
69.70%
0.010
24.39%
2.95% - 37.92%
Cancer type
all types
28
5.89
7642
4759
62.27%
1.62
1.44-1.82
59.80%
<0.001
23.83%
19.03% - 28.06%
adenocarcinoma
10
6.10
2485
1627
65.47%
1.52
1.20-1.91
67.80%
0.001
22.40%
10.91% - 31.19%
squamous cell carcinoma
3
6.67
101
57
56.44%
1.36
0.80-2.32
0.00%
0.400
14.94%
-14.11% - 32.11%
Publication language
EN
22
5.87
7082
4633
65.42%
1.40
1.27-1.54
35.40%
0.049
18.69%
13.91% - 22.94%
CN
11
5.72
2518
1354
53.77%
1.99
1.63-2.42
55.90%
0.012
26.75%
20.78% - 31.55%
Source of passive smoking
household/workplace
19
5.73
5183
3668
70.77%
1.70
1.46-1.99
72.20%
<0.001
29.14%
22.30% - 35.21%
household
7
6.14
1170
595
50.85%
1.67
1.32-2.10
41.40%
0.115
20.40%
12.33% - 26.64%
workplace
5
6.40
1178
245
20.80%
2.01
1.62-2.50
0.00%
0.514
10.45%
7.96% - 12.48%
Study setting
hospital-based
24
5.60
7428
4767
64.18%
1.67
1.47-1.9
68.50%
<0.001
25.75%
20.52% - 30.40%
population-based
9
6.44
2172
1220
56.17%
1.31
1.14-1.51
0.00%
0.464
13.29%
6.90% - 18.97%
Conclusion
Around 20% of lung cancer cases in never smokers, both men and women, are potentially attributable to passive smoking in China. These lung cancer cases in never smokers might be potentially prevented by eliminating exposure to passive smoking, in particular with regards to household passive smoking.
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S01 - IASLC CT Screening Symposium: Forefront Advances in Lung Cancer Screening (Ticketed Session) (ID 96)
- Event: WCLC 2019
- Type: Symposium
- Track: Screening and Early Detection
- Presentations: 2
- Now Available
- Moderators:John Kirkpatrick Field, James L Mulshine
- Coordinates: 9/07/2019, 07:00 - 12:00, Colorado Springs (1994)
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S01.12 - Panel Discussion (Now Available) (ID 3638)
07:00 - 12:00 | Presenting Author(s): Matthijs Oudkerk
- Abstract
- Presentation
Abstract
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S01.13 - Session IV: What Do We Do Next? Group Discussion Behind Implementing Lung Cancer Screening (ID 3639)
07:00 - 12:00 | Author(s): Matthijs Oudkerk
- Abstract
Abstract not provided
