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

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

来源：European Radiology, 2018, 28(6): 2255-2263.

DOI：10.1007/s00330-017-5154-8

• Delta radiomic features
• MRI
• Radiation necrosis
• Brain metastases
• Gamma Knife radiosurgery

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. @@@ 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. @@@ 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. @@@ Delta radiomic features extracted from MR images have potential for distinguishing radiation necrosis from tumour progression after radiosurgery for brain metastases. @@@ aEuro cent Some radiomic features showed better reproducibility for progressive lesions than necrotic ones @@@ aEuro cent Delta radiomic features can help to distinguish radiation necrosis from tumour progression @@@ aEuro cent Delta radiomic features had better predictive value than did traditional radiomic features.

• 出版日期2018-6
• 单位中南大学