||<p>Background: Radiomics is a process of converting medical images into high-dimensional
quantitative features and the subsequent mining these features for providing decision
support. It is conducted as a potential noninvasive, low-cost, and patient-specific
routine clinical tool. Building a predictive model which is reliable, efficient, and
accurate is a vital part for the success of radiomics. Machine learning method is
a powerful tool to achieve this. Feature extraction strongly affects the performance.
Delta-feature is one way of feature extraction methods to reflect the spatial variation
in tumor phenotype, hence it could provide better treatment-specific assessment. </p><p>Purpose:
To compare the performance of using pre-treatment features and delta-features for
assessing the brain radiosurgery treatment response, and to investigate the performance
of different combinations of machine learning methods for feature selection and for
feature classification. </p><p>Materials and Methods: A cohort of 12 patients with
brain treated by radiosurgery was included in this research. The pre-treatment, one-week
post-treatment, and two-month post-treatment T1 and T2 FLAIR MR images were acquired.
61 radiomic features were extracted from the gross tumor volume (GTV) of each image.
The delta-features from pre-treatment to two post-treatment time points were calculated.
With leave-one-out sampling, pre-treatment features and the two sets of delta-features
were separately input into a univariate Cox regression model and a machine learning
model (L1-regularized logistic regression [L1-LR], random forest [RF] or neural network
[NN]) for feature selection. Then a machine learning method (L1-LR, L2-regularized
logistic regression [L2-LR], RF, NN, kernel support vector machine [Kernel-SVM], linear-SVM,
or naïve bayes [NB]) was used to build a classification model to predict overall survival.
The performance of each model combination and feature type was estimated by the area
under receiver operating characteristic (ROC) curve (AUC). </p><p>Results: The AUC
of one-week delta-features was significantly higher than that of pre-treatment features
(p-values < 0.0001) and two-month delta-features (p-value= 0.000). The model combinations
of L1-LR for feature selection and RF for classification as well as RF for feature
selection and NB for classification based on one-week delta-features presented the
highest AUC values (both AUC=0.944). </p><p>Conclusions: This work potentially implied
that the delta-features could be better in predicting treatment response than pre-treatment
features, and the time point of computing the delta-features was a vital factor in
assessment performance. Analyzing delta-features using a suitable machine learning
approach is potentially a powerful tool for assessing treatment response.</p>