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

Abstract

Objectives To develop a model using radiomic features extracted from MR images to distinguish radiation necrosis from tumor 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 4 MR sequences (T1, T1 post-contrast, T2, and fluid-attenuated inversion recovery) obtained at 2 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 5 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 tumor progression after radiosurgery for brain metastases.

Non-small cell lung cancer brain metastases and the immune system: From brain metastases development to treatment

“…Another interesting new imaging analysis method is radiomics feature analysis. It has already been shown that radiomics can aid in the differentiation between progression and pseudoprogression in BM patients treated with SRT but this technique had not been validated in BM patients treated with ICI [89].…”

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“…Image biomarker explorer is an open‐source tool for radiomic feature calculation made available by the MD Anderson Cancer Center, and it has already been reliably used for radiomic studies by several institutions around the world . Recently, a paper was published that shares guidelines and experiences using this software .…”

“…Image biomarker explorer is an open-source tool for radiomic feature calculation made available by the MD Anderson Cancer Center, and it has already been reliably used for radiomic studies by several institutions around the world. [27][28][29][30][31][32][33] Recently, a paper was published that shares guidelines and experiences using this software. 34 Image biomarker explorer allows the straightforward calculation of eight feature categories: shape, intensity direct, intensity-histogram, intensityhistogram Gaussian fit, gray-level co-occurrence matrix, gray-level run length matrix, neighborhood gray tone difference matrix, and gradient orient histogram.…”

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“…LBP feature extractor is known for its efficiency in utilizing the computation power, but its effectiveness reduces with an increase in noise in the image [43]. Another commonly used method to extract radiomics features is Histogram of Oriented Gradient (HOG) [50] [57] where the number of oriented gradient occurrences in certain image regions are counted to create a histogram. Depending on the application, different regions can be used to capture local shape and edge information from the images, which is further converted into a feature vector using the HOG descriptor.…”