Predicting distant failure in early stage NSCLC treated with SBRT using clinical parametersPredicting distant failure in lung SBRT

Zhiguo Zhou, Michael Folkert, Nathan Cannon, Puneeth Iyengar, Kenneth Westover, Yuanyuan Zhang, Hak Choy, Robert Timmerman, Jingsheng Yan, Xian J. Xie, Steve Jiang, Jing Wang

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Purpose/objective The aim of this study is to predict early distant failure in early stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT) using clinical parameters by machine learning algorithms. Materials/methods The dataset used in this work includes 81 early stage NSCLC patients with at least 6 months of follow-up who underwent SBRT between 2006 and 2012 at a single institution. The clinical parameters (n = 18) for each patient include demographic parameters, tumor characteristics, treatment fraction schemes, and pretreatment medications. Three predictive models were constructed based on different machine learning algorithms: (1) artificial neural network (ANN), (2) logistic regression (LR) and (3) support vector machine (SVM). Furthermore, to select an optimal clinical parameter set for the model construction, three strategies were adopted: (1) clonal selection algorithm (CSA) based selection strategy; (2) sequential forward selection (SFS) method; and (3) statistical analysis (SA) based strategy. 5-cross-validation is used to validate the performance of each predictive model. The accuracy was assessed by area under the receiver operating characteristic (ROC) curve (AUC), sensitivity and specificity of the system was also evaluated. Results The AUCs for ANN, LR and SVM were 0.75, 0.73, and 0.80, respectively. The sensitivity values for ANN, LR and SVM were 71.2%, 72.9% and 83.1%, while the specificity values for ANN, LR and SVM were 59.1%, 63.6% and 63.6%, respectively. Meanwhile, the CSA based strategy outperformed SFS and SA in terms of AUC, sensitivity and specificity. Conclusions Based on clinical parameters, the SVM with the CSA optimal parameter set selection strategy achieves better performance than other strategies for predicting distant failure in lung SBRT patients.

Original languageEnglish (US)
Pages (from-to)501-504
Number of pages4
JournalRadiotherapy and Oncology
Volume119
Issue number3
DOIs
StatePublished - Jun 1 2016

Keywords

  • Clinical parameter
  • Distant failure
  • Feature selection
  • Machine learning
  • SBRT

ASJC Scopus subject areas

  • Hematology
  • Oncology
  • Radiology Nuclear Medicine and imaging

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