ArticleRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology2016
Predicting distant failure in early stage NSCLC treated with SBRT using clinical parameters.
Article in Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Applying Artificial Neural Networks to Develop a Decision Support Tool for Tis-4N0M0 Non-Small-Cell Lung Cancer Treated With Stereotactic Body Radiotherapy.JCO clinical cancer informatics · 2022Article
- Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients.BMC cancer · 2022Article
- An Artificial Neural Network Prediction Model for Posttraumatic Epilepsy: Retrospective Cohort Study.Journal of medical Internet research · 2021Article
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- Predictors of Distant Recurrence Following Stereotactic Body Radiation Therapy for Stage I Non-Small Cell Lung Cancer.American journal of clinical oncology · 2020Article
- Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century.The British journal of radiology · 2020Review
- Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features.Physics in medicine and biology · 2019Article
- Predictors of Nodal and Metastatic Failure in Early Stage Non-small-cell Lung Cancer After Stereotactic Body Radiation Therapy.Clinical lung cancer · 2019Article
- Early Identification of High-Risk TIA or Minor Stroke Using Artificial Neural Network.Frontiers in neurology · 2019Article
- A pilot study using kernelled support tensor machine for distant failure prediction in lung SBRT.Medical image analysis · 2018Article
- Validating a Predictive Atlas of Tumor Shrinkage for Adaptive Radiotherapy of Locally Advanced Lung Cancer.International journal of radiation oncology, biology, physics · 2018Article
- Machine learning and modeling: Data, validation, communication challenges.Medical physics · 2018Article
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Authors and funding
12 authors.
Funding
Abstract
PURPOSE/
objectiveThe 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/
methodsThe 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.
resultsThe 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.
conclusionsBased 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.
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