Author of

• 10850

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  P1.12 - Poster Session 1 - NSCLC Early Stage (ID 203)

  • Event: WCLC 2013
  • Type: Poster Session
  • Track: Medical Oncology
  • Presentations: 1
  • Moderators:
  • Coordinates: 10/28/2013, 09:30 - 16:30, Exhibit Hall, Ground Level

  • 10871

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    P1.12-021 - Development and validation of a clinical prediction model for N2 lymph node metastasis in stage I non-small cell lung cancer (ID 2949)

    09:30 - 09:30  |  Author(s): K. Chen
    • Abstract

    Background
    The true incidence of occult N2 lymph node metastasis in patients with clinical stageⅠnon-small cell lung cancer (NSCLC) remains controversial. Estimation of the probability of N2 lymph node metastasis can assist physicians when making diagnosis and treatment decisions.

    Methods
    We reviewed the medical records of 739 patients with computed tomography–defined N0 NSCLC that had an exact Tumor-Node-Metastasis stage after surgery. A random subset of three fourths of the patients (n=554) were selected to develope the prediction model. Logistic regression analysis of the clinical characteristics was used to estimate the independent predictors of N2 lymph node metastasis. A prediction model was then built and internally validated by using cross validation and externally validated by the remaining one fourth(n=185) patients which made up the validation data set. The model was also compared to 2 previously described models.

    Results
    We identified 4 independent predictors of N2 disease: a younger age, larger tumor size, central tumor location, and adenocarcinoma or adenosquamous carcinoma pathology. The model showed good calibration (Hosmer–Lemeshow test: P = .92) with an area under the receiver operating characteristic curve (AUC) of 0.748 (95% confidence interval, 0.687-0.809). The AUC of our model was better than those of the other two models when validated with independent data.

    Table 1 . Multivariate Logistic Regression Analysis

    Variable	Regression Coefficient	P	OR	95%CI Lower	95%CI Upper
    Age	-0.032	0.007	0.969	0.957	0.981
    Tumor Size	0.456	<0.001	1.577	1.449	1.705
    Central	1.753	<0.001	5.771	5.453	6.089
    Adenocarcinoma /mixed	1.787	<0.001	5.970	5.546	6.394
    Constant	-2.983	0.001	0.051

    Figure 1 Figure 1. The ROC curves of our model, the VA model and the Fudan model for all patients of group B. The AUC of our model was 0.786 (95% CI, 0.690-0.881); the VA model was 0.673 (95% CI, 0.554-0.792); the Fudan model was 0.757 (95% CI, 0.659-0.885).

    Conclusion
    Our prediction model estimated the pretest probability of N2 disease in computed tomography–defined stageⅠNSCLC and was more accurate than the existing models. Use of our model can be of assistance when making clinical decisions about invasive or expensive mediastinal staging procedures.