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Laszlo T Vaszar



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    EP1.11 - Screening and Early Detection (ID 201)

    • Event: WCLC 2019
    • Type: E-Poster Viewing in the Exhibit Hall
    • Track: Screening and Early Detection
    • Presentations: 1
    • Now Available
    • Moderators:
    • Coordinates: 9/08/2019, 08:00 - 18:00, Exhibit Hall
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      EP1.11-24 - The TREAT Model 2.0: Predicting Lung Cancer in Patients Seeking Care in High-Risk Clinics (Now Available) (ID 2713)

      08:00 - 18:00  |  Author(s): Laszlo T Vaszar

      • Abstract
      • Slides

      Background

      Appropriate risk-stratification of indeterminate pulmonary nodules (IPNs) is necessary to estimate the best diagnostic strategy. Validated models for patients with high-risk IPNs are poorly calibrated. We sought to expand our previous Thoracic Research Evaluation And Treatment (TREAT) model into a more generalized, robust model for lung cancer prediction, the TREAT 2.0.

      Method

      A total of 1402 patients with known or suspected lung cancer were used to recalibrate the TREAT 1.0 model. Clinical data and patient demographics were retrospectively collected from six clinics located in four U.S. states. Six datasets were divided into 3 clinical groups: patients who presented to a pulmonary nodule clinic (n=375), patients who presented to an outpatient thoracic surgery clinic (n=553) and patients who presented for surgical resection (n=474). A logistic regression model using multiple imputation was developed and validated. Model variables included age, body mass index, gender, smoking pack-years, size of nodule, spiculation, growth over time, location in upper lobe, prior cancer history, pre-operative FEV1, pre-operative symptoms, FDG-PET positivity, and clinical group. The discrimination and calibration of the TREAT 2.0 model was estimated and compared to two other common models for lung nodules, the Mayo Clinic and Herder models.

      Result

      Lung cancer prevalence was as follows: pulmonary nodule clinic 42%, thoracic surgery clinic 73%, and surgical resection cohort 90%. The strongest predictors of cancer were clinical group, age, nodule growth, PET positivity, and smoking pack-years. The median TREAT 2.0 area under the receiver operating curve (AUC) for the imputed dataset was 0.86 (95% confidence interval (CI), 0.86-0.87) and the Brier score was 0.13. The TREAT 2.0 model had better accuracy (p < 0.001) (Figure 1) and calibration than the Mayo Clinic (AUC =0.74 95% CI: 0.74-0.75; Brier score=0.21) or Herder models (AUC=0.75; 95%CI: 0.74-0.75 and Brier score=0.19).figure1_abstract.png

      Conclusion

      The TREAT 2.0 model is more accurate and better calibrated than the Mayo Clinic or Herder models in patients presenting with nodules at high risk for lung cancer. Nodule calculators such as the TREAT 2.0 that account for variation in lung cancer prevalence with a variable for clinical group may improve generalizability and increase use in clinical practice.

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