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Chenchen Zhang



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    P17 - Locoregional and Oligometastatic Disease - Biomarkers (ID 127)

    • Event: WCLC 2020
    • Type: Posters
    • Track: Locoregional and Oligometastatic Disease
    • Presentations: 1
    • Moderators:
    • Coordinates: 1/28/2021, 00:00 - 00:00, ePoster Hall
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      P17.01 - Adaptive Elastic-Net Nomogram Predicting Disease-Free Survival in Resected Stage IIIA (N2) Non–Small Cell Lung Cancer (ID 2188)

      00:00 - 00:00  |  Presenting Author(s): Chenchen Zhang

      • Abstract
      • Slides

      Introduction

      Pathologic stage IIIA (N2) non–small cell lung cancer (NSCLC) is prominently intrinsic heterogeneous. This study was designed to establish a nomogram to predict disease-free survival and individualize forward therapy selection of this population.

      Methods

      We retrospectively selected patients with pathologic T1-3N2M0 NSCLC treated in one institution from 2013 to 2015 and randomly allocated them (3:1) to the training set and validation set 1. Meanwhile, we collected patients from another institution between 2005 and 2011 with the same inclusion and exclusion criteria. Significant prognostic variables identified by Log-rank test were used to build a multivariate adaptive Elastic-Net Cox regression model. Nomogram was built based on the regression model and was validated by internal (validation set 1) and external validation (validation set 2) to ensure model’s generalization ability. Discriminative ability and calibration ability of the model was assessed by time-dependent ROCs and calibration curves.

      Results

      A total of 1189 patients were included in this study (624 in the training set, 208 in the validation set 1 and 357 patients in the validation set 2). Pathologic T stage, histology, skip N2 (yes or no), involved N2 stations (single or multiple), positive lymph nodes rate(pLNR) and adjuvant treatment pattern were identified as significant prognostic factors and entered into the adaptive Elastic-Net Cox regression model. A nomogram was developed from the training set and validated in two validation sets. The calibration curves showed optimal consistency between nomogram prediction and actual observation. The median AUC in the validation set 1 (0.68; range 0.62 to 0.71) was similar to that in the validation set 2 (0.67; range 0.61 to 0.73).

      Conclusion

      We developed and validated a nomogram to predict disease-free survival of patients with resected stage IIIA (N2) NSCLC individually, through which clinicians could make specific treatment strategy precisely.

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