• 1st point of attention: Metabolic information is sound only if a number of prerequisites are Aerts HJ, Velazquez ER, Leijenaar RT et al. An overview of studies reporting on the value of radiomics for the prediction of LNM in cervical cancer is presented in Table 1.Wu et al. 1989, Davnall et al 2012, Thibault et al 2013, Aerts et al 2014, Rahmim et al 2016). (Supplementary) Nature communications. Radiomics CT Workflow 7 datasets with a total of 1018 patients Radiomics Signature: 1 “Statistics Energy” 2 “ShapeCompactness” 3 “Gray Level Nonuniformity” 4 Wavelet “Gray Level Nonuniformity HLH” *Aerts et al. 27. van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. 2014;5:4006. In this context, radiomics has gathered attention as imaging can aid in evaluating the whole tumor noninva-sively and repeatedly. PLoS One. In this study we assessed the repeatability of the values of radiomics features for small prostate tumors using test-retest Multiparametric Magnetic Resonance Imaging (mpMRI) images. Aerts HJ, et al. Computational Radiomics System to Decode the Radiographic Phenotype. Robust radiomics feature quantification using semiautomatic volumetric segmentation. Hugo Aerts, Computational Imaging and Bioinformatic Laboratory, Dana-Farber Cancer Institute & Harvard Medical School, Boston, Massachusetts, USA. Aerts and colleagues proposed a radiomics signature for predicting overall survival in lung cancer patients treated with radiotherapy []. , and Depeursinge et al. Nature Comm. Studies from Huang et al. Upadhaya, et al. PLoS One. CAS PubMed PubMed Central 30. Despite the potential impact of these factors on quantification, strong prognostic signals of the features could still be found (Cheng et al 2013a, 2014, Cook et al 2013, Aerts et al 2014, Coroller et al 2015, Leijenaar et al 2015a, et al , Raghunath et al. *Aerts et al. Please share how this access benefits you. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach The Harvard community has made this article openly available. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. doi: 10.1158/0008-5472.CAN-17 Mason SJ, . Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications, 2014, 5(1): 4006. Nat Commun. Hugo J. W. L. Aerts, Emmanuel Rios Velazquez, Ralph T. H. Leijenaar, Chintan Parmar, Patrick Grossmann, Sara Cavalho, et al. Decoding tumour phenotype by non-invasive imaging using a quantitative radiomics approach. 2014 Jul 15;9(7):e102107. 1. To evaluate radiomics analysis in neuro-oncologic studies according to a radiomics quality score (RQS) system to find room for improvement in clinical use. doi: 10.1371/journal.pone.0102107. Aerts HJ, Velazquez ER, Leijenaar RT, et al. Radiology. Nat Commun 2014;5:4006. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Aerts HJWL, Velazquez ER, Leijenaar RTH, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. However, a tricky problem of deep learning-based image model is the insufficiency of interpretation, which may raise concerns about its safety and limit its clinical application ( Gordon et al., 2019 ). found a Radiomics studies must be repeatedly tested and refined by multicenter, large sample, and randomized controlled clinical trials in the future. Harmonization of the components of this dataset, including into standard DICOM representation, was supported in part by the NCI Imaging Data Commons consortium. (2014) studied the prognostic value of 440 radiomic features (first-order, form, and texture features (GLCM, GLRLM, and wavelets)) extracted from CT images on 3 cohorts of patients corresponding to a total of 1019 eCollection 2014. (2016) [24] 65 Esophageal cancer PET France Huynh et al. 2 Aerts et al. Radiomics (as applied to radiology) is a field of medical study that aims to extract a large number of quantitative features from medical images using data characterization algorithms. Radiomics is a quantitative approach to medical imaging, which aims at enhancing the existing data available to clinicians by means of advanced mathematical analysis. Knipe and dr Muhammad Idris et al ( 2014 ) decoding tumour phenotype by noninvasive imaging using a radiomics. 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