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01-06-2018 | Oncology

A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images

Authors: Zijian Zhang, Jinzhong Yang, Angela Ho, Wen Jiang, Jennifer Logan, Xin Wang, Paul D. Brown, Susan L. McGovern, Nandita Guha-Thakurta, Sherise D. Ferguson, Xenia Fave, Lifei Zhang, Dennis Mackin, Laurence E. Court, Jing Li

Published in: European Radiology | Issue 6/2018

Abstract

Objectives

To develop a model using radiomic features extracted from MR images to distinguish radiation necrosis from tumour progression in brain metastases after Gamma Knife radiosurgery.

Methods

We retrospectively identified 87 patients with pathologically confirmed necrosis (24 lesions) or progression (73 lesions) and calculated 285 radiomic features from four MR sequences (T1, T1 post-contrast, T2, and fluid-attenuated inversion recovery) obtained at two follow-up time points per lesion per patient. Reproducibility of each feature between the two time points was calculated within each group to identify a subset of features with distinct reproducible values between two groups. Changes in radiomic features from one time point to the next (delta radiomics) were used to build a model to classify necrosis and progression lesions.

Results

A combination of five radiomic features from both T1 post-contrast and T2 MR images were found to be useful in distinguishing necrosis from progression lesions. Delta radiomic features with a RUSBoost ensemble classifier had an overall predictive accuracy of 73.2% and an area under the curve value of 0.73 in leave-one-out cross-validation.

Conclusions

Delta radiomic features extracted from MR images have potential for distinguishing radiation necrosis from tumour progression after radiosurgery for brain metastases.

Key points

• Some radiomic features showed better reproducibility for progressive lesions than necrotic ones

• Delta radiomic features can help to distinguish radiation necrosis from tumour progression

• Delta radiomic features had better predictive value than did traditional radiomic features

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Title: A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images
Authors: Zijian Zhang
Jinzhong Yang
Angela Ho
Wen Jiang
Jennifer Logan
Xin Wang
Paul D. Brown
Susan L. McGovern
Nandita Guha-Thakurta
Sherise D. Ferguson
Xenia Fave
Lifei Zhang
Dennis Mackin
Laurence E. Court
Jing Li
Publication date: 01-06-2018
Publisher: Springer Berlin Heidelberg
Published in: European Radiology / Issue 6/2018
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-017-5154-8

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images

Abstract

Objectives

Methods

Results

Conclusions

Key points

Keynote webinar | Spotlight on sleep in brain health

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key points

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