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Anand Devaraj



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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: 1
    • Now Available
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      MA10.10 - Uptake in Lung Cancer Screening – Does CT Location Matter? A Pilot Study Comparison of a Mobile and Hospital Based CT Scanner (Now Available) (ID 2165)

      15:15 - 16:45  |  Author(s): Anand Devaraj

      • Abstract
      • Presentation
      • Slides

      Background

      Community based lung cancer screening has been proposed as a method of increasing uptake for lung cancer screening by reducing barriers to participation. We report baseline statistics for a lung cancer screening pilot study in which patients were scanned on either a community based mobile CT unit or on a University Hospital based fixed-site CT scanner.

      Method

      Ever smokers aged 60-75 registered at 17 participating general practitioner practices (GP) in West London were invited for a lung health check at either a mobile unit situated in a supermarket car park or in a hospital site. The location offered was based upon proximity to the participant’s home address. On attendance a lung health check, assessing lung cancer risk, was undertaken. Participants with a LLPv2 score of ≥2.0% and/or PLCOM2012 score of ≥1.51% were offered a same day low dose CT (LDCT) scan. Uptake, attendance and non-attendance (DNA) rates were compared using Chi-squared (χ2) test.

      Result

      8366 potentially eligible participants were invited for a lung health check appointment; 5135 (61.4%) to the hospital site, and 3231 (38.6%) to the mobile site. 1749/8366 (20.9%) participants responded (males n=954/1749 (54.5%)). 1047/5135 (20.4%) were booked an appointment at the hospital site and 702/3231 (21.7%) at the mobile site (p=0.14). No difference was observed in lung cancer risk between participants at the two sites. Patients at the mobile site were more likely to be ex-smokers (p=0.048). The DNA rate at the hospital site was 96/1047 (9.2%) and at the mobile site was 48/702 (6.8%) (p=0.08). On attendance, 63 patients were ineligible for screening; 52/1749 (3.0%) did not meet the entry criteria and 11/1749 (0.6%) were acutely unwell. Therefore 1542 patients attended and had a risk score calculated and of these 1145/1542 (74.3%) underwent CT. Median [range] risk scores for scanned patients were 1.97 [0-25.34] for PLCOM2012 and 4.71 [0.94-35.92] for LLPv2. Lung cancer was confirmed in 17/1145 (1.5%) participants at baseline. A further 151/1145 (13.2%) participants will undergo interval CT for indeterminate nodules.

      Conclusion

      There was a small but non-significant increase in participant response rates for the community based mobile site compared to the hospital site CT scanner, but no difference in DNA rates. While community based mobile scanners may provide valuable additional capacity to lung screening programmes, the magnitude of any benefit to participant uptake needs to be balanced against the additional complexity of setting up these stand-alone facilities. Further work is ongoing to understand the interaction between CT location and other factors that influence recruitment, with a view to using effective methods to increase uptake at all sites for future screening invitations.

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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: 3
    • Moderators:
    • Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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      P1.11-19 - Trial in Progress: Cancer Screening Study With or Without Low Dose Lung CT to Validate a Multi-Cancer Early Detection Blood Test (ID 1840)

      09:45 - 18:00  |  Author(s): Anand Devaraj

      • Abstract

      Background

      Few effective screening tests exist for cancer, and each is specific to a single cancer type. A single blood test that directly measures tumor cell-free DNA and can detect several cancer types, including lung, pancreatic, colon, and head & neck cancers, with high specificity may reduce cancer burden. The SUMMIT Study is designed to validate the ability of an investigational blood test to detect multiple cancer types among a high-risk population undergoing LDCT for lung cancer screening, as well as in a lower risk population.

      Method

      SUMMIT is designed to enroll 50,000 participants aged 50-77, and will follow participants for up to 10 years via medical records and the national cancer registry. There will be two groups: Group A (n=25,000), individuals at high-risk for lung and other cancers due to substantial smoking history (per United States Preventive Services Task Force LDCT screening criteria, or PLCOm2012 six-year risk estimate of ≥1.3%); and Group B (n=25,000), individuals not meeting Group A criteria. Exclusion criteria include active cancer treatments. Potential participants are identified from the records of 540 general practices across North Central and East London, and are invited by letter to attend a dedicated LDCT scanning unit (Group A) or clinical unit (Group B), where eligibility is confirmed and consent obtained.

      Group A participants will provide a blood sample, complete a questionnaire, and receive a baseline LDCT, and then provide a blood sample and complete a questionnaire at 12 and 24 months post-baseline. Those with a negative baseline LDCT (without lung nodules) will be randomised to either have a LDCT at 12 and 24 months or no further scans. Participants with lung nodules at baseline could have more frequent scans. Group B participants complete a questionnaire and provide a blood sample at three study appointments (baseline, 12 months, 24 months). The primary endpoint is cancer incidence and stage. Blood test performance will be determined by sensitivity and false-positive rates (specificity). Blood test results will not be returned to physicians or participants. Group A enrollment began in April 2019, and Group B enrollment is targeted to start later in 2019.

      The study is designed to determine the performance and cost-effectiveness of this investigational multi-cancer blood test compared to or combined with LDCT for identifying lung cancer, and in detecting cancers for which there are no effective screening tests. The SUMMIT Study may therefore inform new approaches to finding cancer early.

      Result

      Section not applicable

      Conclusion

      Section not applicable

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      P1.11-30 - Very Rapid Growth of Small Pulmonary Nodules Predicts Benignity (ID 704)

      09:45 - 18:00  |  Author(s): Anand Devaraj

      • Abstract
      • Slides

      Background

      Growth of pulmonary nodules on repeat CT is used to identify malignant lesions, although very rapid growth is thought to imply an inflammatory process. Few data exist examining the optimum threshold at which rate of growth predicts a benign aetiology.

      Method

      Using an institutional CT database of small (<15mm) solid pulmonary nodules (n=784), we identified patients with antecedent (≥30 days prior) thin section (≤2mm) CT imaging and a final diagnosis of primary lung malignancy or a definite benign diagnosis based on pathology or longitudinal CT follow up data (n=137). Enlarging nodules (volume growth >25%) were identified (n=63) using semi-automated volumetry, and the volume doubling time (VDT) calculated. In cases where no nodule existed on the antecedent CT, a volume of 5mm3 was assigned, permitting the calculation of a ‘virtual’ VDT. Comparison of volume doubling time between benign and malignant nodules was made using Wilcoxon signed rank test. A receiver operator curve was constructed, and the optimum threshold of nodule growth rate predictive of benignity was calculated using the methods of Miller.

      Result

      The final study population consisted of 63 nodules in 57 patients [32/62 (50.8%) malignant, median age 67 years (range 34–85 years), male = 30/57 (52.6%)]. There was no difference in patient age nor in smoking status between groups, although patients with malignant diagnoses significantly more likely to be female (p < 0.001).

      The median time between baseline (T1) and antecedent (T0) scans was 260 days (interquartile range 343 days). At baseline (T1), benign lesions (median diameter 10mm, median volume 380 mm3, range 10-4300mm3) were significantly smaller than malignant nodules (median diameter 13mm, median volume 890mm3, range 60-4250 mm3); p = 0.001.

      24/31 benign lesions and 3/32 malignant lesions were not visible on the T0 scan, and were assigned a volume of 5mm3. The median benign lesion VDT was 70 days (interquartile range 270 days), malignant median VDT was 188 days (interquartile range 170 days); p = 0.2. The majority of lesions with very rapid growth (VDT < 90 days) were benign diagnoses (n= 17/24 [70.8%]). When examining these rapidly growing nodules, the optimal cut-point of the receiver-operator was a VDT of 50 days, AUC = 0.735. This provided 100% specificity for benign disease.

      Conclusion

      Our results confirm that very rapid nodule growth predicts benignity; a VDT of <50 days was 100% specific for benignity. Further work is required to validate these findings in other cohorts.

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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): Anand Devaraj

      • 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

      figure 2.png1994 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).

      Conclusion

      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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    P2.11 - Screening and Early Detection (ID 178)

    • Event: WCLC 2019
    • Type: Poster Viewing in the Exhibit Hall
    • Track: Screening and Early Detection
    • Presentations: 1
    • Now Available
    • Moderators:
    • Coordinates: 9/09/2019, 10:15 - 18:15, Exhibit Hall
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      P2.11-13 - What Is the Impact of Localised Data When Training Deep Neural Networks for Lung Cancer Prediction?  (Now Available) (ID 2485)

      10:15 - 18:15  |  Presenting Author(s): Anand Devaraj

      • Abstract
      • Slides

      Background

      Deep neural networks (DNN) have been shown to offer a viable alternative for risk cancer prediction of indeterminate pulmonary nodules (IPNs). While the type of data used for training is known to impact performance, this issue has not been extensively studied. We present, for the first time, a study of the effect of including training data that matches the clinical pathway of the independent validation dataset, a nodule clinic of incidental findings.

      Method

      Two identical DNNs were trained on the task of diagnosis prediction of pulmonary nodules from CT images. The first one (DNNnlst) used purely screening data from the US National Lung Screening Trial (922 cancer and 14733 benign nodules), while the second one (DNNnlst+incidental) included data of incidentally detected nodules from European hospitals (1064 cancer and 7207 benign nodules). Both models were evaluated in an independent validation set of nodules coming from a referral center in the UK (Royal Brompton and Harefield Hospital, London) consisting of baseline scans of 406 cancer and 325 benign nodules. The models were compared in terms of AUC, as well as their ability to reclassify cancer patients with intermediate risk nodules. The Intermediate risk sub-population was defined by selecting patients with nodules in the size range of 8 to 15mm, and who were followed-up within a year with CT, referred to PET-CT, or referred to biopsy. Within this sub-population, a cancer prevalence of 30% was assumed. The operating points of the cancer prediction models were chosen by setting a cancer risk of 70%, corresponding to high-risk nodules in the guidelines of the British Thoracic Society.

      Result

      The DNNnlst and DNNnlst+incidental models achieved an AUC of 84.33 (95%CI: 81.49, 87.15) and 87.43 (95%CI: 84.79, 89.82) respectively on the entire validation set, showing an improvement in the discrimination capabilities (p < 0.01). For reference, using the nodule’s maximum axial diameter as a predictor led to an AUC of 79.07 (95%CI: 75.73, 82.73). Additionally, considering only the intermediate risk population of the data, all of which would require workup according to guidelines, the DNNnlst+incidental model correctly classified as high risk 34.34% more cases than the DNNnlst model (sensitivity 59.39% (95%CI: 47.68, 75.17) vs. 44.21% (95%CI: 20.98, 64.42)), an improvement significant at p < 0.05.

      Conclusion

      Although a DNN trained only on the US lung cancer screening data could have clinical utility in an incidental setting, exposing it to further incidental data can not only increase its discriminability, as expected, but also make it a potentially more effective tool for speeding up the diagnosis of cancer patients with intermediate risk nodules and reducing unnecessary workups.

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