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R. Bhatia
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MA 14 - Diagnostic Radiology, Staging and Screening for Lung Cancer I (ID 672)
- Event: WCLC 2017
- Type: Mini Oral
- Track: Radiology/Staging/Screening
- Presentations: 1
- Moderators:H. Kondo, Hong Kwan Kim
- Coordinates: 10/17/2017, 15:45 - 17:30, F205 + F206 (Annex Hall)
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MA 14.11 - Malignancy Risk Prediction of Pulmonary Nodule in Lung Cancer Screening – Diameter Or Volumetric Measurement (ID 9113)
15:45 - 17:30 | Author(s): R. Bhatia
- Abstract
- Presentation
Background:
Nodule size is an important parameter to determine malignancy risk. Semi-automated size measurements have the potential to replace manual measurements due to their higher accuracy and reproducibility, and less inter/intra-user variation. However, controversy exists regarding the relative accuracy of 2D diameter versus 3D volumetric measurement to predict malignancy risk. The objective of this study is to compare nodule malignancy prediction models based on 2D mean diameter versus volumetric measurement, both generated by a CAD Software.
Method:
We analyzed baseline LDCT reconstructed using high spatial frequency algorithm from 1746 participants (47% women, 53% men, age: 62.5 ± 5.8 yrs) in the Pan-Canadian Early Detection of Lung Cancer Study (PanCan), who had ≥1 non-calcified nodules ≥3mm in diameter. CAD software (CIRRUS Lung Screening, Radboud University Medical Center, Nijmegen, the Netherlands) performed an automatic nodule segmentation, which could be optimised manually, measurement of mean diameter and volume was generated. Malignant or benign nodule status was confirmed by pathology or prolonged follow-up (median follow-up 5.5 years). Logistic regression models predicting cancer were prepared with one including mean diameter and the other including volume. The discrimination, the ability to classify cancer versus benign nodules correctly, was evaluated by the area under the receiver operator characteristic cure (AUC). The calibration - do predicted probabilities match observed probabilities, was assessed using Spiegelhalter’s z-test and graphically by plotting the observed and predicted mean probabilities of cancer by deciles of model risk.
Result:
There were in total 5878 nodules, including 119 cancers in 115 individuals. Both models gave similar predictive performances. AUC was 0.947 (95% CI 0.922-0.964) in the mean diameter model and 0.946 (95% CI 0.921-0.966) in the volumetric model (p=0.83). The calibrations were similar between the two models (figure). Figure 1
Conclusion:
The predictive performances of nodule malignancy prediction models using mean 2D nodule diameter and 3D volumetric data were indistinguishable.
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MA 20 - Recent Advances in Pulmonology/Endoscopy (ID 685)
- Event: WCLC 2017
- Type: Mini Oral
- Track: Pulmonology/Endoscopy
- Presentations: 1
- Moderators:C. Lee, S. Sasada
- Coordinates: 10/18/2017, 14:30 - 16:15, F205 + F206 (Annex Hall)
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MA 20.11 - Chronic Obstructive Pulmonary Disease Prevalence in a Lung Cancer Screening Population (ID 9588)
14:30 - 16:15 | Author(s): R. Bhatia
- Abstract
- Presentation
Background:
Chronic obstructive pulmonary disease (COPD) and lung cancer are associated through tobacco use. COPD is underdiagnosed in both the primary care and lung cancer populations. Diagnosis of COPD should lead to improved care and quality of life. Screening programs could provide an opportunity to capture undiagnosed COPD. We analyzed the Pan-Canadian Early Detection of Lung Cancer Study (PanCan Study) to evaluate the prevalence of COPD in a screening population.
Method:
The PanCan Study was a single arm lung cancer screening trial which recruited individuals to low dose CT scan, autofluorescence bronchoscopy, and biomarker screening. Eligible individuals were 50-75 years of age, had smoked within 15 years, and had a minimum six-year risk of lung cancer ≥ 2% based on a risk prediction model derived from PLCO study data, which included COPD as a risk factor. Consenting subjects completed a questionnaire including background medical conditions, high-risk work exposures, and smoking history. Baseline spirometry was performed, and COPD was defined by GOLD criteria. For individuals not receiving post-bronchodilator spirometry, COPD was defined as ‘probable’ if GOLD criteria were met pre-bronchodilator and there was no prior diagnosis of asthma. Individuals with definite or probable COPD were defined as having COPD.
Result:
Of 2537 individuals recruited, 2514 had available spirometry data. Mean age was 62.3 years, 55.3% were male, median pack-years smoked was 50, 62.3% were active smokers, 45.1% had symptoms of dyspnea, 52.4% cough, and 37.5% wheeze. 35.2% had worked in a high-risk occupation. Overall, 1136 (45.2%) met spirometry criteria for COPD. Of 1987 individuals without a prior history of COPD, 41.9% met spirometry criteria for COPD, of which 53.7% had moderate to severe disease. Of 527 individuals (21%) reporting a diagnosis of COPD at baseline, 57.5% met spirometry criteria for COPD, 32.2% did not, and 10.3% had a prior diagnosis of asthma. In a multivariate model for risk of COPD, age (odds ratio (OR)~per year~ 1.06), dyspnea (OR 1.42), being a current smoker (OR 1.43), and pack-years (log transformed OR 1.42) were significant (all p < 0.001) as were high-risk occupation (OR 1.24, p=0.013) and wheeze (OR 1.24, p = 0.024).
Conclusion:
A diagnosis of COPD by spirometry is common in a lung cancer screening trial population. Individuals with a pre-existing self-reported diagnosis of COPD often fail to meet spirometry criteria for their diagnosis. Testing a lung cancer screening population for COPD could significantly improve COPD diagnosis and treatment.
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OA 15 - Diagnostic Radiology, Staging and Screening for Lung Cancer II (ID 684)
- Event: WCLC 2017
- Type: Oral
- Track: Radiology/Staging/Screening
- Presentations: 1
- Moderators:Y. Satoh, Jin Mo Goo
- Coordinates: 10/18/2017, 14:30 - 16:15, Room 303 + 304
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OA 15.01 - Lung Cancer Screening: Participant Selection by Risk Model – the Pan-Canadian Study (ID 8466)
14:30 - 16:15 | Author(s): R. Bhatia
- Abstract
- Presentation
Background:
Retrospective studies indicate that selecting individuals for low dose computed tomography (LDCT) lung cancer screening based on a highly predictive risk model is superior to applying National Lung Screening Trial (NLST)-like criteria, which use only categorized age, pack-year and smoking quit-time information. The Pan-Canadian Early Detection of Lung Cancer Study (PanCan Study) was designed to prospectively evaluate whether individuals at high risk for lung cancer could be identified for screening using a risk prediction model. This paper describes the study design and results.
Method:
2537 individuals were recruited through 8 centers across Canada based on a ≥2% of lung cancer risk estimated by the PanCan model, a precursor to the validated PLCOm2012 model. Individuals were screened at baseline and 1 and 4 years post-baseline.
Result:
At a median 5.5 years of follow-up, 164 individuals (6.5%) were diagnosed with 172 lung cancers. This was a significantly greater percentage of persons diagnosed with lung cancers than was observed in the NLST(4.0%)(p<0·001). Compared to 57% observed in the NLST, 77% of lung cancers in the PanCan Study were early stage (I or II) (p<0.001) and to 25% in a comparable population, age 50-75 during 2007-2009 in Ontario, Canada’s largest province, (p<0·001).
Conclusion:
Enrolling high-risk individuals into a LDCT screening study or program using a highly predictive risk model, is efficient in identifying individuals who will be diagnosed with lung cancer and is compatible with a strong stage shift – identifying a high proportion at early, potentially curable stage. Funding This study was funded by the Terry Fox Research Institute and Canadian Partnership Against Cancer. ClinicalTrials.gov number, NCT00751660
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