Prediction of Stereotactic Radiosurgery Brain Metastasis Lesion Control Using Radiomic Features

Yeung, TY; Rodrigues, George; Lagerwaard, F.; Palacios, Miguel; Bohoudi, O.; Bruynzeel, A.; Mattonen, Sarah A.; Warner, Andrew; Johnson, Christopher Z.; Ward, Aaron D.

doi:10.1016/j.ijrobp.2015.07.023

Cited by 2 publications

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Use of radiomics for the prediction of local control of brain metastases after stereotactic radiosurgery

et al. 2020

View full text Add to dashboard Cite

Background Local response prediction for brain metastases (BM) after stereotactic radiosurgery (SRS) is challenging, particularly for smaller BM, as existing criteria are based solely on unidimensional measurements. This investigation sought to determine whether radiomic features provide additional value to routinely available clinical and dosimetric variables to predict local recurrence following SRS. Methods Analyzed were 408 BM in 87 patients treated with SRS. A total of 440 radiomic features were extracted from the tumor core and the peritumoral regions, using the baseline pretreatment volumetric post-contrast T1 (T1c) and volumetric T2 fluid-attenuated inversion recovery (FLAIR) MRI sequences. Local tumor progression was determined based on Response Assessment in Neuro-Oncology‒BM criteria, with a maximum axial diameter growth of >20% on the follow-up T1c indicating local failure. The top radiomic features were determined based on resampled random forest (RF) feature importance. An RF classifier was trained using each set of features and evaluated using the area under the receiver operating characteristic curve (AUC). Results The addition of any one of the top 10 radiomic features to the set of clinical features resulted in a statistically significant (P < 0.001) increase in the AUC. An optimized combination of radiomic and clinical features resulted in a 19% higher resampled AUC (mean = 0.793; 95% CI = 0.792–0.795) than clinical features alone (0.669, 0.668–0.671). Conclusions The increase in AUC of the RF classifier, after incorporating radiomic features, suggests that quantitative characterization of tumor appearance on pretreatment T1c and FLAIR adds value to known clinical and dosimetric variables for predicting local failure.

show abstract

Use of radiomics for the prediction of local control of brain metastases after stereotactic radiosurgery

et al. 2020

View full text Add to dashboard Cite

show abstract

Combining Radiomics and CNNs to Classify Benign and Malignant GIST

Zhuo¹,

Li²,

Zhou³

2018

Proceedings of the 2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

View full text Add to dashboard Cite

This paper studies the classification of gastrointestinal stromal tumor (GIST) with machine learning and radiomics methods. The highlight of the paper is that combining features extracted from computed tomography (CT) images by radiomics and convolutional neural networks (CNNs) are used in classification task. It's different from the previous radiomics-only methodology including tumor intensity, tumor shape, tumor texture and wavelet features. In the paper, deep neural networks (DNN) U-Net and Res-Net realize high-level features extraction. The classifiers based on random forest (RF) and gradient boosting decision tree (GBDT) are trained with combining features extracted from computed tomography images. The experiments show that the combining features are effective biomarkers to discriminate benign and malignant GIST. The performance (accuracy 81%, precision 78%, recall 83%) of the classifier is validated on 50 instances.

show abstract

Prediction of Stereotactic Radiosurgery Brain Metastasis Lesion Control Using Radiomic Features

Cited by 2 publications

References 0 publications

Use of radiomics for the prediction of local control of brain metastases after stereotactic radiosurgery

Use of radiomics for the prediction of local control of brain metastases after stereotactic radiosurgery

Combining Radiomics and CNNs to Classify Benign and Malignant GIST

Contact Info

Product

Resources

About