A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities

By El Naqa. 2015

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Deep Reinforcement Learning for Automated Radiation Adaptation in Lung Cancer (2017, methodology): “The image features analysis was performed using customized routines in MATLAB and the features included metabolic tumor volume, intensity statistics, and texture-derived metrics [14, 15].”
Characterization of PET/CT images using texture analysis: the past, the present… any future? (2016, methodology): “This would allow major issues in comparing co-occurrencederived metrics calculated with different spatial sampling to be avoided [55, 74].”
Deep Learning-based Image Segmentation on Multimodal Medical Imaging (2019, methodology): “In this paper, we use the publicly available soft-tissue sarcoma (STS) dataset [37] from the cancer imaging archive [38] for model development and validation.”
Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures (2017, methodology): “Wavelet decomposition of the original image has been employed to extract intensity and textural features from different frequency bands(61) and to obtain fused texture characteristics from two imaging modalities.(68) All intensity-based and textural features were mostly calculated in a volumetric wa”