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

TitleA Validated Prediction Model for Overall Survival From Stage III Non-Small Cell Lung Cancer: Toward Survival Prediction for Individual Patients.
Publication TypeJournal Article
Year of Publication2015
AuthorsOberije, C, De Ruysscher, D, Houben, R, van de Heuvel, M, Uyterlinde, W, Deasy, JO, Belderbos, J, Dingemans, A-MC, Rimner, A, Din, S, Lambin, P
JournalInt J Radiat Oncol Biol Phys
Volume92
Issue4
Pagination935-44
Date Published2015 Jul 15
Publication LanguageENG
ISSN1879-355X
KeywordsAge Factors, Aged, Analysis of Variance, Antineoplastic Combined Chemotherapy Protocols, Carboplatin, Carcinoma, Non-Small-Cell Lung, Chemoradiotherapy, Cisplatin, Deoxycytidine, Etoposide, Female, Humans, Lung Neoplasms, Male, Middle Aged, Models, Statistical, Neoplasm Staging, Nomograms, Probability, Radiotherapy Dosage, Regression Analysis, Severity of Illness Index, Sex Factors, Vinblastine
Abstract

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, and 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.

DOI10.1016/j.ijrobp.2015.02.048
Alternate JournalInt. J. Radiat. Oncol. Biol. Phys.
PubMed ID25936599
PubMed Central IDPMC4786012
Grant ListP30 CA008748 / CA / NCI NIH HHS / United States
U01 CA143062-01 / CA / NCI NIH HHS / United States