2018
DOI: 10.1016/j.ijrobp.2018.05.041
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Distinguishing True Progression From Radionecrosis After Stereotactic Radiation Therapy for Brain Metastases With Machine Learning and Radiomics

Abstract: Purpose: Treatment effect or radiation necrosis after stereotactic radiosurgery (SRS) for brain metastases is a common phenomenon often indistinguishable from true progression. Radiomics is an emerging field that promises to improve on conventional imaging. In this study, we sought to apply a radiomics-based prediction model to the problem of diagnosing treatment effect after SRS. Methods and Materials: We included patients in the Johns Hopkins Health System who were treated with SRS for brain metastases who… Show more

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Cited by 113 publications
(100 citation statements)
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References 31 publications
(42 reference statements)
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“…Peng et al (37) evaluated the usefulness of MRI radiomics for this important question. Sixty-six patients with 82 lesions treated with stereotactic radiosurgery and imaging findings on contrast-enhanced T1 and FLAIR sequences suspicious for tumor recurrence were included in the study.…”
Section: Differentiation Of Treatment-related Changes From Brain Metamentioning
confidence: 99%
“…Peng et al (37) evaluated the usefulness of MRI radiomics for this important question. Sixty-six patients with 82 lesions treated with stereotactic radiosurgery and imaging findings on contrast-enhanced T1 and FLAIR sequences suspicious for tumor recurrence were included in the study.…”
Section: Differentiation Of Treatment-related Changes From Brain Metamentioning
confidence: 99%
“…Radiomics use texture features to define potential quantitative metrics from radiological images [1][2][3][4][5][6][7][8][9][10][11].The texture features extracted are based on several properties inherent to image data, such as, gray-level distribution [12], inter-voxel relationships [13][14][15][16][17], and shape [18]. The goal of radiomics is to provide a quantitative framework for a radiological biopsy of tissue, which could be correlated to the underlying tissue biology.…”
Section: Introductionmentioning
confidence: 99%
“…Radiomic analysis has shown very promising results in this field, especially in lung 92 and brain disease. [93][94][95] As an example of this, in lung SBRT for NSCLC, Mattonen et al 81,92 have demonstrated the ability of quantitative CT analysis to provide early prediction of recurrence versus radiation damage. This analysis was based on higher-density HU consolidation changes and GCLM-derived texture modifications detected as early as 2-5 months after SBRT.…”
Section: Radiomics To Discriminate Between Radiation Damage and Tumoumentioning
confidence: 99%