Author of

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  MO08 - NSCLC - Early Stage (ID 117)

  • Event: WCLC 2013
  • Type: Mini Oral Abstract Session
  • Track: Medical Oncology
  • Presentations: 1
  • Moderators:K. Nakagawa, J. Douillard
  • Coordinates: 10/28/2013, 16:15 - 17:45, Bayside Gallery B, Level 1

  • 11325

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    MO08.09 - PET-CT scanning derived Artificial Neural Network can<br /> predict mediastinal lymph nodes metastases in NSCLC<br /> patients. Preliminary report. (ID 1198)

    16:15 - 17:45  |  Author(s): B. Małkowski
    • Abstract
    • Presentation
    • Slides

    Background
    Mediastinal lymph nodes staging in NSCLC is of paramount importance. Although relatively precise, diagnostic modalities still employ certain level of invasiveness. Artificial Neural Network (ANN) is a well established predictor tool which, due to underlying distribution and relationship among the given variables, allow for construction of multidimensional models trained in prognosis of given outcome. Their performance in mediastinal staging based on radiological data only, currently remains unknown.

    Methods
    Samples from 467 lymph nodes were obtained from 160 patients with primary NSCLC by means of endobronchial ultrasound guided-transbronchial needle aspiration (EBUS-TBNA), mediastinoscopy or lymphadenectomy during thoracotomy and microscopically analyzed. ANN models were created and prospectively validated on unmatched cohort of 50 consecutive patients (158 groups of lymph nodes). To identify factors correlated with nodal involvement single factor tests and logistic regression analysis were performed.Figure 1 Figure 1. The multilayer perceptron (MLP). Artificial Neural Network (ANN) structure for predicting metastatic involvement of mediastinal lymph nodes in NSCLC patients.

    Results
    Size and standard uptake value (SUV) of the node along with primary tumour T characteristics were identified as the most sensitive variables regardless of the analysis conducted. Two ANN models predicted metastatic involvement with 89% and 92% accuracy. Single factor tests maintained high accuracy only for 2 out of 4 most sensitive variables (SUV >2.8 and length >15mm) in prospective validation. Additionally, logistic regression analysis allowed for construction of scoring model with certain parameters corresponding to risk thresholds of metastatic disease.Figure 1 Figure 2. Artificial Neural Network (ANN) characteristics. ROC curves for 2 manually designed ANNs (A); Sensitivity analyses of coefficients (B) and overall characteristics of ANNs performance (C).

    Conclusion
    ANN is a repeatable and accurate diagnostic tool in mediastinal staging in NSCLC patients. Before its role in clinical practice will be established in large multi-centre study, findings of this preliminary report should be considered as exploratory only.

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