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Title: A Validated Prediction Model for Overall Survival From Stage III Non-Small Cell Lung Cancer: Toward Survival Prediction for Individual Patients

Purpose: Although patients with stage III non-small cell lung cancer (NSCLC) are homogeneous according to the TNM staging system, they form a heterogeneous group, which is reflected in the survival outcome. The increasing amount of information for an individual patient and the growing number of treatment options facilitate personalized treatment, but they also complicate treatment decision making. Decision support systems (DSS), which provide individualized prognostic information, can overcome this but are currently lacking. A DSS for stage III NSCLC requires the development and integration of multiple models. The current study takes the first step in this process by developing and validating a model that can provide physicians with a survival probability for an individual NSCLC patient. Methods and Materials: Data from 548 patients with stage III NSCLC were available to enable the development of a prediction model, using stratified Cox regression. Variables were selected by using a bootstrap procedure. Performance of the model was expressed as the c statistic, assessed internally and on 2 external data sets (n=174 and n=130). Results: The final multivariate model, stratified for treatment, consisted of age, gender, World Health Organization performance status, overall treatment time, equivalent radiation dose, number of positive lymph node stations, andmore » gross tumor volume. The bootstrapped c statistic was 0.62. The model could identify risk groups in external data sets. Nomograms were constructed to predict an individual patient's survival probability ( (www.predictcancer.org)). The data set can be downloaded at (https://www.cancerdata.org/10.1016/j.ijrobp.2015.02.048). Conclusions: The prediction model for overall survival of patients with stage III NSCLC highlights the importance of combining patient, clinical, and treatment variables. Nomograms were developed and validated. This tool could be used as a first building block for a decision support system.« less
Authors:
 [1] ;  [1] ;  [2] ;  [1] ; ;  [3] ;  [4] ;  [5] ;  [6] ; ;  [4] ;  [1]
  1. Radiation Oncology, Research Institute GROW of Oncology, Maastricht University Medical Center, Maastricht (Netherlands)
  2. (Belgium)
  3. Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam (Netherlands)
  4. Memorial Sloan Kettering Cancer Center, New York (United States)
  5. Department of Radiation Oncology, Netherlands Cancer Institute, Amsterdam (Netherlands)
  6. Department of Pulmonology, University Hospital Maastricht, Research Institute GROW of Oncology, Maastricht (Netherlands)
Publication Date:
OSTI Identifier:
22462389
Resource Type:
Journal Article
Resource Relation:
Journal Name: International Journal of Radiation Oncology, Biology and Physics; Journal Volume: 92; Journal Issue: 4; Other Information: Copyright (c) 2015 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; DECISION MAKING; EQUIVALENT RADIATION DOSES; FORECASTING; HAZARDS; LUNGS; LYMPH NODES; MULTIVARIATE ANALYSIS; NEOPLASMS; NOMOGRAMS; PATIENTS; PROBABILITY; WHO