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H.C. Schmidt
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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: 2
- 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)
16:55 - 17:00 | Author(s): H.C. Schmidt
- 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 14.14 - The First 100 Days: Early findings from the Lung Cancer Screening Pilot for People at High Risk in Ontario, Canada (ID 8579)
17:10 - 17:15 | Author(s): H.C. Schmidt
- Abstract
- Presentation
Background:
An estimated 330,000 people in the province of Ontario are at high risk of developing lung cancer and eligible for screening with low-dose computed tomography (LDCT). On June 1 2017 Cancer Care Ontario launched the Lung Cancer Screening Pilot for People at High Risk with the purpose of informing the design and implementation of a province-wide organized screening program. Organized screening is available at 3 hospitals, and provider and public recruitment strategies are being implemented to engage the target population in regional catchment areas. Key aspects of pilot design include eligibility based on the PLCO~M2012noRace~ risk prediction model, navigation support, informed participation, embedded smoking cessation services, radiology quality assurance, LDCT findings categorized in accordance with Lung-RADS™, provision of same-visit screening results and seamless transition to a Diagnostic Assessment Program (DAP) for assessment of findings suspicious for lung cancer. Data collected for 3,000 participants over a 2-year period will inform a comprehensive evaluation of the pilot.
Method:
Indicators were selected to assess impacts of early recruitment efforts and outcomes of key screening processes related to eligibility assessment, the LDCT scan and smoking cessation. Data were collected by the pilot sites and submitted to Cancer Care Ontario. Participant feedback on the screening experience was collected by survey. Data were collected in June and July 2017; data from August 2017 will be available for presentation.
Result:
The majority (87%) of the 862 people recruited into the pilot were provider-referred. Of the 472 people who completed a risk assessment, 71% were found to be eligible for screening (PLCO~M2012noRace~ 6-year risk ≥2.0%). Baseline LDCT scans were conducted for 156 participants; approximately 8% of these participants were referred to a DAP for further assessment. Uptake of smoking cessation services by current smokers was high (data to be included in presentation). Feedback surveys were received from 78 of 156 participants screened. Overall experience with the screening visit was rated as ‘excellent’ by 91% of respondents, and 70% indicated a preference to receive results during the same visit as the LDCT.
Conclusion:
Provider-led recruitment supports the identification of screen-eligible individuals. Implementation of navigator-guided organized screening, following a detailed screening pathway that features provision of same-visit results, has contributed to high participant satisfaction to date. To our knowledge, this pilot involves the most detailed organized screening pathway and comprehensive evaluation plan developed to date. Learnings from this pilot will be highly relevant to jurisdictions around the world that are adopting screening.
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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 - 14:40 | Author(s): H.C. Schmidt
- 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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