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Renelle Myers



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    MA10 - Emerging Technologies for Lung Cancer Detection (ID 129)

    • Event: WCLC 2019
    • Type: Mini Oral Session
    • Track: Screening and Early Detection
    • Presentations: 3
    • Now Available
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      MA10.01 - Invasive Adenocarcinoma in Screen Detected Pure Ground-Glass Nodules (GGN) (Now Available) (ID 2736)

      15:15 - 16:45  |  Presenting Author(s): Renelle Myers

      • Abstract
      • Presentation
      • Slides

      Background

      A major criticism of lung cancer screening initiatives is their propensity to instigate enhanced surveillance and over-treatment of otherwise indolent disease, including adenocarcinoma-in-situ (AIS). These nodules present radiographically as GGN. There are wide variations in the recommendations for surveillance (repeat imaging), diagnosis (biopsy) and therapeutic intervention (resection) for these lesions. To further our understanding of the optimal management of screen detected GGN, we used data from two screening studies in Canada with up to 17 years of follow-up to determine the proportion of persistent GGN that are invasive adenocarcinomas.

      Method

      Two lung cancer screening studies data sets were reviewed: the BC Lung Health Study (BCLHS) with 1365 participants and the Pan-Canadian Early Detection of Lung Cancer Study (PanCan) with 2537 participants. BCLHS enrolled ever smokers 45-74 years of age with >30-year smoking history. The median follow-up in this cohort was 12 years (0.1-17.6) The PanCan study screened participants age 50-75 years with a PLCOm2008 6-year lung cancer risk > 2%. The median follow-up was 5.5 years (3.2-6.1). The nodules were followed until they resolved, demonstrated stability for >2 yrs or were surgically resected. All pure GGO resected were re-reviewed and classified by two pulmonary pathologists according to the revised 2015 World Health Organization classification of lung tumours. Cancers were staged using the 8th edition of the AJCC/UICC cancer staging manual.

      Result

      A total of 18,589 nodules in 3902 participants were reviewed. 2392 (13% of all nodules) were classified as pure GGN. 1073 of the 2392 were > 5mm at the baseline scan. Of these 1073 GGN, 156 (15%) resolved, 879 (82%) remained pure GGN, 38 (3.5%) became part-solid or solid. 32(3%) of the GGN from 29 patients that demonstrated growth were resected. The median size prior to resection was 16 mm (range 7 to 33 mm). The histopathology distribution included: 19 invasive adenocarcinomas, 7 minimally invasive adenocarcinomas, 6 adenocarcinoma-in-situ. The TNM stage distribution and average size of the GGN on the CT prior to resection are listed in Table 1. Sixty-one percent of the invasive cancers (Stage IA1 to IIIA) were less than 20 mm. Eleven percent of the invasive adenocarcinomas had lymph node metastasis.

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      Conclusion

      A high proportion of pure GGN that demonstrate growth are invasive cancers. The majority were < 20mm in size when they were resected. This has significant implication in the development of recommendations to manage screen detected GGN.

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      MA10.06 - Randomized Clinical Trial with Computer Assisted Diagnosis (CAD) Versus Radiologist as First Reader of Lung Screening LDCT (Now Available) (ID 2102)

      15:15 - 16:45  |  Author(s): Renelle Myers

      • Abstract
      • Presentation
      • Slides

      Background

      CAD has been studied extensively in lung nodule detection while its value in lung cancer screening has not been tested in a prospective randomized clinical study. We aim to evaluate the value of a CAD in radiologist work-flow and for quality assurance in reading and reporting lung cancer screening LDCT.

      Method

      Between August 2016 and February 2019, 1386 ever smokers were enrolled in the BC Lung Screen Trial. The median follow-up was 10 months. Their chest CTs were randomized to CAD reading first arm using a CAD system (Philips IntelliSpace Portal) (n=741), or Radiologist reading first (RAD) arm (n=645). In CAD-1st arm, a radiologist read CTs with the CAD findings displayed concurrently, accepting, rejecting and adding nodule(s). Radiologist’s reading time was recorded, and management recommendation was made using the automatically generated PanCan lung nodule risk score (N Engl J Med 2013; 369:910-919). In Rad-1st arm, the radiologist read the CT without using CAD, gave the management recommendation using Lung-RADS, and the reading time was recorded. Then the radiologist turned on CAD annotations to accept, reject and add nodule(s). The PanCan nodule risk scores were generated. Nodule management was categorized into 3 groups: I: Scheduled follow-up CT ≥1yr for those with no or very low risk lung nodules; II: Early recall CT <1 yr; or III: Referral to clinical diagnostic pathway for suspicious malignancy.

      Result

      Radiologist’s reading time was shorter in CAD-1st than Radiologist-1st arm (9±3 vs. 10±3 minutes, p<0.01). The time saved was greatest for Group I scans (85% of workload) (8±3 vs. 10±3 minutes, p<0.01). In 20/741 (2.7%) participants in CAD-1st arm, the additional nodule added by the radiologist upgraded the patient’s management; 5 of 20 were later confirmed to be malignant. Two of 5 were >3cm masses, the other three included a 19 mm GGO and two solid ones abutting vessels. In 1/645 (0.15%) participants in Radiologist-1st arm, the additional nodule detected by CAD upgraded the patient’s management from Group I to II. Over 31-months of follow-up, 29 cancers (2.1%) have been detected, and 1 of 29 (3.4%), a 5 mm solid nodule in the left lower lobe abutting the fissure and vessels, was missed by both radiologist and CAD.

      Conclusion

      CAD saves radiologist’s time in reading large numbers of screening LDCT especially in those with no or very low risk lung nodules. However, reading by experienced radiologist is still needed.

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      MA10.09 - Evaluation of the Clinical Utility of the PanCan, EU-NELSON and Lung-RADS Protocols for Management of Screen Detected Lung Nodules at Baseline (Now Available) (ID 2137)

      15:15 - 16:45  |  Presenting Author(s): Renelle Myers

      • Abstract
      • Presentation
      • Slides

      Background

      Several protocols are available to guide management of lung nodules identified by the first (baseline) low-dose screening CT. It is important to objectively assess their clinical utility, health care resource utilization and potential harms. We aim to compare the PanCan (NEJM 2013;369:908 & J Thorac Oncol 2018; 13(10): S362-S363), EU-NELSON (Lancet Oncol. 2017 Dec;18(12):e754-e766 & Lung Cancer 2006;54:177-184) and Lung-RADS(https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/Lung-Rads) lung nodule management protocols on our data set from two sites of the International Lung Screen Trial (ILST), in Vancouver, Canada and Perth, Western Australia.

      Method

      Ever smokers age 55 to 80 years were enrolled into ILST if they had a ≥30 pack-years smoking history and smoked within 15 years or if their PLCO m2012 6 year lung cancer risk was ≥1.51%. The participants were managed via the PanCan lung nodule risk based protocol. The NELSON and Lung-RADS nodule protocols were applied to the ILST data set. The potential difference in the proportion of the participants having an early recall CT scan (< 1 year) or referral to a clinical diagnostic pathway was compared between the PanCan, NELSON, Lung-RADS protocols. The participants were divided into 3 groups: Group 1 (next scheduled annual/biennial CT) included PanCan CAT 1, 2, NELSON NODCAT I, II, Lung-RADS CAT 1, 2. Group 2 (early recall CT <1 year) included PanCan CAT 3, NELSON NODCAT III, Lung-RADS CAT 3, 4A and Group 3 (Diagnostic Pathway) included PanCan CAT 4, 5, NELSON NODCAT IV (solid nodule), Lung-RADS CAT 4B, 4X. The number of participants and the lung cancer rate in each group was compared between the three protocols.

      Result

      A total of 1386 participants with a median follow-up of 10 months (ranging from 4-31 mos) were evaluated. The results are shown in Table 1.

      PanCan selected the fewest individuals to early recall (Group 2 & 3) versus NELSON p=0.004 and detected the same number of lung cancers as did the NELSON and more than by Lung-RADS.

      In addition, 81% of the PanCan group 1 participants were triaged to biennial repeat screening instead of annual screening in the NELSON and Lung-RADS protocols, which has substantial resource utilization and radiation exposure implications.

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      Conclusion

      The personalized risk-based PanCan Protocol may decrease resource utilization and potentially minimize risk of screening for participants.

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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
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      OA06.01 - Comparison Between Radiomics-Based Machine Learning and Deep Learning Image Classification for Sub-Cm Lung Nodules (Now Available) (ID 2810)

      11:00 - 12:30  |  Author(s): Renelle Myers

      • Abstract
      • Presentation
      • Slides

      Background

      New clinical challenges have arisen from the recent recognition for an improved mortality of cancers via lung cancer screening using LDCT. A particular challenge for physicians and CADx systems is the classification and prediction of behavior for sub-cm lung nodules that are frequently present in screening CT scans. By predicting and classifying the behavior of these small nodules, we can identify potential cancerous nodules into the earlier stages of malignancy making them more easily treatable.

      Method

      We have evaluated a multitude of image processing techniques to assist in CADx systems for these small nodules such as Radiomic feature-based machine learning algorithms (linear discriminant analysis) as well as leveraging pretrained convolution neural networks such as VGG19 and InceptionV3 using deep learning/transfer learning techniques. The linear discriminate Radiomic analysis (LDA) classified a sample of CT imaged nodules (n=514) using quasi-volumetric nodule data (images of the nodules from CT slices above and below the central slice) into three discriminate categories: cancerous (clinically confirmed, n = 140) versus resolved (not present in follow up CT scans, n=107) versus stable (a negligible change in shape, texture, size in multi year follow up CT scans, n=267). Each nodule was segmented from the original CT scan using an inhouse lung CT image segmenation routine. This routine generated 2167 discrete CT nodule images upon which 133 Radiomic shape and texture features were calculated.

      Result

      The LDA Radiomic analysis correctly classified the individual nodual sections with an accuracy of 75.1% (jackknife - leave one out result) using only 18 features predefined traditional image analysis features (4 shape feature(s), 14 texture feature(s)) for cancer vs resolved + stable nodules. Requiring that more than or equal to 50% of sections from a nodule be classified as cancer for the nodule to be classified as cancer individual nodules could be correctly classified with an 80% accuracy.

      The leveraged pretrained networks (VGG19, and InceptionV3) trained using standard data augmentation and finetuning techniques, trained on this same quasi-volumetric image data as a binary classification task (malignant vs. benign nodules) achieved an average classification accuracy of 71% and 75% respectively through 10-crossfold validation.

      Conclusion

      Machine learning using 18 Radiomics features was able to classify 75.1% of the 2167 CT nodule images (up to 5 images/CT slices per nodule) and 80% of the nodules correctly. The best of the Deep Learning networks achieved almost equivalent results.

      The image classification deep neural network results suggest the implementation of more advanced regularization and initialization deep learning techniques to further refine the decision boundaries for these pretrained networks might be benefitial. We believe the development of visualization neural network software to highlight the defining nodule features during classification would clinically assist in providing context clues for nodule diagnosis.

      This work has been supported by TFRI project ref:1068

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    OA09 - Lung Cancer: A Preventable Disease? (ID 134)

    • Event: WCLC 2019
    • Type: Oral Session
    • Track: Prevention and Tobacco Control
    • Presentations: 1
    • Now Available
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      OA09.01 - Opt-Out Smoking Cessation Program in Lung Cancer Screening Provides Excellent Quit Rates (Now Available) (ID 2141)

      11:00 - 12:30  |  Presenting Author(s): Renelle Myers

      • Abstract
      • Presentation
      • Slides

      Background

      The most effective way to integrate smoking cessation into lung cancer screening has not been established. The opt -out approach has been shown to produce quit rates in those ‘not interested in quitting’ to equal quit rate in those individuals ‘ready to quit.’ The BC Lung Screen Trial recruits ever smokers 55 to 80 years of age who meet either the PLCOm2012 6 years lung cancer risk ≥1.5% or USPSTF smoking criteria. The purpose of this study is to prospectively evaluate the smoking cessation rate of current smokers using an opt-out approach.

      Method

      Screened participants who were interviewed for screening eligibility were given brief, 10 minute, smoking cessation counselling in-person by a research assistants(RA) trained in smoking cessation. They discussed the health benefits of smoking cessation and outlined resources available including free nicotine replacement therapy. All participants were given an information pamphlet and automatically referred to QuitNow; the provincial telephone based smoking cessation line. They were then contacted by the study staff at 3 and 6 months to determine smoking status.

      Result

      presentation4.jpgBetween January 2018 and Jan 2019, 396 current smokers participated in a face to face interview prior to screening and 355 (90%) accepted referral to QuitNow. All of these participants were contacted by QuitNow/study staff. 309 (87%) accepted some form of cessation service including telephone counselling, text messaging, on-line coaching, pharmacotherapy or a combination of services. Of those accepting a service, the 3 and 6-month self-reported quit rate was 27% with 75% of participants who quit used telephone or online coaching.

      Conclusion

      Most current smokers participating in a lung cancer screening program are interested in smoking cessation. Of those who accept some form of smoking cessation counselling service, an excellent (27%) quit rate was observed using an opt-out approach.

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    PL02 - Presidential Symposium including Top 7 Rated Abstracts (ID 89)

    • Event: WCLC 2019
    • Type: Plenary Session
    • Track:
    • Presentations: 1
    • Now Available
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      PL02.02 - Lung Cancer Screenee Selection by USPSTF Versus PLCOm2012 Criteria – Interim ILST Findings (Now Available) (ID 2804)

      08:00 - 10:15  |  Author(s): Renelle Myers

      • Abstract
      • Presentation
      • Slides

      Background

      The National Lung Screening Trial showed that lung cancer screening of high-risk individuals with low dose computed tomography can reduce lung cancer mortality by 20%. Critically important is enrolling high-risk individuals. Most current guidelines including the United States Preventive Services Task Force (USPSTF) and Center for Medicare and Medicaid Services (CMS) recommend screening using variants of the NLST eligibility criteria: smoking ≥30 pack-years, smoking within 15 years, and age 55-80 and 55-77 years. Many studies indicate that using accurate risk prediction models is superior for selecting individuals for screening, but these findings are based on retrospective analyses. The International Lung Screen Trial (ILST) was implemented to prospectively identify which approach is superior.

      Method

      ILST is a multi-centred trial enrolling 4000 participants. Individuals will be offered screening if they are USPSTF criteria positive or have PLCOm2012 model 6-year risk ≥1.5%. Participants will receive two annual screens and will be followed for six years for lung cancer outcomes. Individuals not qualifying by either criteria will not be offered screening, but samples of them will be followed for lung cancer outcomes. Outcomes in discordant groups, USPSTF+ve/PLCOm2012-ve and PLCOm2012+ve/USPSTF-ve, are informative. Numbers of lung cancers and individuals enrolled, sensitivity, specificity and positive predictive values (PPV) of the two criteria will be compared.

      Result

      As of March 2019, ILST centers in Canada (British Columbia), Australia, Hong Kong, and the United Kingdom had enrolled and scanned 3673 individuals. Study results are summarized in Figure 1.

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      Conclusion

      Interim analysis of ILST data, indicates that classification accuracy of lung cancer screening outcomes support the PLCOm2012 criteria over the USPSTF criteria. The PLCOm2012 criteria detected significantly more lung cancers. Individuals who are USPSTF+ve and PLCOm2012-ve appear to be at such low baseline risk (0.2%) that they may be unlikely to benefit from screening.

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