Journal article
Predicting distant failure in early stage NSCLC treated with SBRT using clinical parametersPredicting distant failure in lung SBRT
Radiotherapy and Oncology, Vol.119(3), pp.501-504
2016
DOI: 10.1016/j.radonc.2016.04.029
PMID: 27156652
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 6months 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.
Details
- Title: Subtitle
- Predicting distant failure in early stage NSCLC treated with SBRT using clinical parametersPredicting distant failure in lung SBRT
- Creators
- Zhiguo ZhouMichael FolkertNathan CannonPuneeth IyengarKenneth WestoverYuanyuan Zhang - Southwestern Medical CenterHak ChoyRobert TimmermanSteve JiangJing Wang - The University of Texas Southwestern Medical CenterJingsheng YanXian J Xie
- Resource Type
- Journal article
- Publication Details
- Radiotherapy and Oncology, Vol.119(3), pp.501-504
- DOI
- 10.1016/j.radonc.2016.04.029
- PMID
- 27156652
- NLM abbreviation
- Radiother Oncol
- ISSN
- 1879-0887
- eISSN
- 1879-0887
- Publisher
- Elsevier BV
- Grant note
- DOI: 10.13039/100004917, name: Cancer Prevention and Research Institute of Texas, award: RP130109; DOI: 10.13039/100000048, name: American Cancer Society, award: RSG-13-326-01-CCE, ACS-IRG-02-196; name: US National Health Institute, award: R01 EB020366
- Language
- English
- Date published
- 2016
- Academic Unit
- Preventive and Community Dentistry; Biostatistics; Dental Research
- Record Identifier
- 9983917782902771
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