Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers

Medical Physics
Timo M DeistPhilippe Lambin

Abstract

Machine learning classification algorithms (classifiers) for prediction of treatment response are becoming more popular in radiotherapy literature. General Machine learning literature provides evidence in favor of some classifier families (random forest, support vector machine, gradient boosting) in terms of classification performance. The purpose of this study is to compare such classifiers specifically for (chemo)radiotherapy datasets and to estimate their average discriminative performance for radiation treatment outcome prediction. We collected 12 datasets (3496 patients) from prior studies on post-(chemo)radiotherapy toxicity, survival, or tumor control with clinical, dosimetric, or blood biomarker features from multiple institutions and for different tumor sites, that is, (non-)small-cell lung cancer, head and neck cancer, and meningioma. Six common classification algorithms with built-in feature selection (decision tree, random forest, neural network, support vector machine, elastic net logistic regression, LogitBoost) were applied on each dataset using the popular open-source R package caret. The R code and documentation for the analysis are available online (https://github.com/timodeist/classifier_selection_code). All ...Continue Reading

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Nov 13, 2018·Journal of Toxicology and Environmental Health. Part a·Zhiwei GaoAmbikaipakan Senthilselvan
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Software Mentioned

rf
R
svmRadial
LogitBoost
rpart
nnet
R package caret
glmnet
caret

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