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A. Langhammer



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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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      P2.13-026a - A Validated Clinical Lung Cancer Risk-Prediction Model for Light-, Heavy- and Ex-Smokers: the Lung-HUNT Model (ID 10328)

      09:30 - 16:00  |  Author(s): A. Langhammer

      • Abstract
      • Slides

      Background:
      Lung cancer screening will become an important way of reducing lung cancer mortality. Identifying high-risk population based purely on age and pack years may leave out 3/4 of high-risk individuals. There is an urgent need for validated, accurate risk-prediction models for all ages and types of smokers.

      Method:
      In the prospective cohort of 65 237 people aged 20-100 years participating in the HUNT2 study in Norway in 1995-97 (70% of the regional adult population), median follow-up time of 15·2 years (800 845 person-years), 583 incident lung cancer cases were diagnosed (cumulative incidence 0·9%). Thirty-six candidate risk variables for lung cancer were examined using univariate and multivariate analyses and backwards feature selection using multiple imputation. The model was validated in ten comparable Norwegian population studies of 44 600 ever-smokers (CONOR), with a median follow-up time of 11·6 years and 675 incident lung cancer events.

      Result:
      In the total HUNT2 cohort at base-line, the smokers were light smokers (median 10·3 pack-years). Among the lung cancer cases 94·7% were ever-smokers (median 22·5 pack-years) and 70% of lung cancer cases had reported smoking <30 pack-years at base-line. There were only seven risk variables selected in the final model; age, pack-years, smoking intensity (number of cigarettes daily), years since quitting, body mass index, daily cough and hours of daily exposure to cigarette smoke. The model for ever-smokers had a concordance index of 0·869 (interquartile range 0·868-0·870). A nomogram was made to calculate the personal 5, 10, and 15-year risk of lung cancer. External validation of the model in CONOR on 44600 ever-smokers showed a similar concordance index of 0·867 ([0·854, 0·880]95% CI). Selecting a threshold of median risk one would need to screen only 22·7% of ever-smokers to identify 78% of all lung cancers.

      Conclusion:
      The resulting Lung-HUNT model is simple, robust and accurate, and identify lung cancer risk individuals of all ages and smoking patterns. This model is useful for prospective screening studies for lung cancer and can motivate smokers to quit smoking. ​

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