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P. Burrowes



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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
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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): P. Burrowes

      • Abstract
      • Presentation
      • Slides

      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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    OA 15 - Diagnostic Radiology, Staging and Screening for Lung Cancer II (ID 684)

    • Event: WCLC 2017
    • Type: Oral
    • Track: Radiology/Staging/Screening
    • Presentations: 1
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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): P. Burrowes

      • Abstract
      • Presentation
      • Slides

      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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    P2.13 - Radiology/Staging/Screening (ID 714)

    • Event: WCLC 2017
    • Type: Poster Session with Presenters Present
    • Track: Radiology/Staging/Screening
    • Presentations: 1
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      P2.13-011 - Optimal Selection Criteria for LDCT Lung Cancer Screening (ID 9628)

      09:30 - 09:30  |  Author(s): P. Burrowes

      • Abstract

      Background:
      Lung cancer screening programs with low dose computed tomography (LDCT) could be economically viable if they targeted high-risk people. The optimal selection criteria have not been defined in prospective clinical trials. The goal of this prospective study is to test the hypothesis that lung cancer screening based on a highly predictive risk model: The Prostate, Lung, Colon, Ovarian (PLCO~m2012~) is superior to applying National Lung Screening Trial (NLST)-like criteria.

      Method:
      Participants were enrolled through three screening studies, two in Canada (Vancouver and Alberta) and one in London, UK. Eligibility included a PLCOm2012 6-year lung cancer risk ≥1.5% or NLST-like criteria (≥30 pack-years smoking history and quit ≤15 years with some variation in age limits – 55 to 80 years in BC, 55 to 74 in Alberta and 60 to 75 in UCL). The proportion of participants who have been found to have lung cancer or high risk lung nodules, requiring repeat imaging studies or biopsy prior to the next scheduled annual screening were compared between the two selection methods.

      Result:
      The demographics of participants are shown in Table 1. To date, 1,533 received a LDCT, of these, 341 met the PLCOm2012 criteria alone, 169 met NLST-like criteria and 1023 met both criteria. Twenty-seven participants have been found to have lung cancers. All 27 met the PLCOm2012 selection criteria alone while 62% met NLST- like criteria. No lung cancer was found in participants who met NLST-like criteria alone. There are 129 participants with suspicious lung nodules under close surveillance or scheduled for biopsy. Among these, 97% met the PLCOm2012 criteria and 74% met NLST-like criteria.

      Table 1. Clinical and Demographic Features of Study Cohorts
      Study Site British Columbia Alberta London Total
      No. Contacted 802 1661 1990 4453
      No. Eligible 364 741 812 1917
      No. Screened 241 688 604 1533
      Age (yrs) 65+/- 6.3 63.5 +/- 4.2 66+/-4.2 64.8+/- 5.7
      Sex (female/Male) 91F:150M 342F:346M 273F:331M 706M;827M
      Current:Former Smoker 103CS:138Ex 341CS:347Ex 443CS:161Ex 887CS:646Ex
      Pack Years (Mean +/-SD) 47.3+/-22 42.4+/-15.8 47.7+/-22.3 45.3+/-19.8
      Median Follow-up(months) 7.5 9.7 9.7
      No. of lung Cancers 3 7 17 27
      Participants with suspicios nodules 21 41 67 129


      Conclusion:
      Our preliminary results show that fewer people are eligible for screening using NLST-like criteria compare to a highly predictive risk model such as PLCOm2012. Thirty-seven percent more participants with lung cancer are identified by PLCOm2012.