Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. In this context, radiomics has gathered attention as imaging can aid in evaluating the whole tumor noninva-sively and repeatedly. 1 Radiomics refers to high‐throughput automated characterization of the tumor phenotype by analyzing quantitative features derived from a radiological image. However, inclusion of Aerts et al. Your story matters Citation Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach The Harvard community has made this article openly available. Robust Radiomics feature quantification using semiautomatic volumetric segmentation. Radiomics is a quantitative approach to medical imaging, which aims at enhancing the existing data available to clinicians by means of advanced mathematical analysis. They found that radiomics analysis of heterogeneous thrombi texture was able found a 2014; 5 :4006. doi: 10.1038/ncomms5006. 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 Cancer Res (2017) 77(21):e104–7. From 189 articles, 51 original research articles reporting the diagnostic, prognostic, or predictive utility … Aerts et al. CAS PubMed PubMed Central 30. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. 2014;9(7):e102107. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Aerts et al demonstrated a CT-based radiomics signature, which captured heterogeneity and had significant prognostic value in lung and head-and-neck cancer. 2014 Radiomics CT Signature Performance - Signature performed significantly better compared to volume in all datasets. Nat Commun 2014;5(1):4006. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Parmar C, Rios Velazquez E, Leijenaar R, et al. Radiomics studies of clinical oncology published in literature Study No. PLoS One. Aerts HJ, et al. SPIE Medical Imaging 2016 2. Radiomics 1. Prognosis classification in glioblastoma multiforme using multimodal MRI derived heterogeneity textural features: impact of pre-processing choices. doi: 10.1371/journal.pone.0102107. (Supplementary) Nature communications. Nat Commun 5:4006 Nat Commun 5:4006 CAS Article Google Scholar Hugo Aerts, Computational Imaging and Bioinformatic Laboratory, Dana-Farber Cancer Institute & Harvard Medical School, Boston, Massachusetts, USA. 1. Dr Henry Knipe and Dr Muhammad Idris et al. Aerts at al. Radiomics studies must be repeatedly tested and refined by multicenter, large sample, and randomized controlled clinical trials in the future. Aerts et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Computational radiomics system to decode the Nat Commun 2014;5:4006. Aerts HJWL, Velazquez ER, Leijenaar RTH et al. The issues raised above are drawbacks of precision medicine. Nat Commun. (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 [] data produced two radiomics features that were also significant in the independent testing data and an AUC above 0.7, as discussed at the beginning of the results presented here. [ PubMed ] Upadhaya, et al. Computational Radiomics System to Decode the Radiographic Phenotype. doi: 10.1158/0008-5472.CAN-17 Nature Comm. Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. To evaluate radiomics analysis in neuro-oncologic studies according to a radiomics quality score (RQS) system to find room for improvement in clinical use. • 1st point of attention: Metabolic information is sound only if a number of prerequisites are CAS Article PubMed PubMed Central Google Scholar 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. Mason SJ, . Song et al, Ann Hematol 2012 Esfahani et al, Ann J Nucl Med Mol Imaging 2013 * Only lymphoma-related studies referred to in this talk! This will enable them to … 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. Aerts and colleagues proposed a radiomics signature for predicting overall survival in lung cancer patients treated with radiotherapy []. Hugo J. W. L. Aerts, Emmanuel Rios Velazquez, Ralph T. H. Leijenaar, Chintan Parmar, Patrick Grossmann, Sara Cavalho, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014; 5:4006 [Google Scholar] 2. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Studies from Huang et al. Recent progress in deep learning has generated a series of the image-based model with high accuracy and good performance (Kather et al., 2019; Lu et al., 2020; Skrede et al., 2020). Nat Commun 2014;5:4006. Aerts HJ, Velazquez ER, Leijenaar RT, et al. 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 ). The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping.0(0):191145. Aerts HJ, Velazquez ER, Leijenaar RT et al. , Raghunath et al. described a combination of features (size, shape, texture and wavelets) which could predict outcome in patients with lung cancer. PLoS One. 2014 Jul 15;9(7):e102107. Decoding tumour phenotype by non-invasive imaging using a quantitative radiomics approach. Harmonization of the components of this dataset, including into standard DICOM representation, was supported in part by the NCI Imaging Data Commons consortium. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. The premise of radiomics is that quantitative image features can serve as biomarkers characterizing disease. Radiology. Gilles RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. 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. of patients Cancer type Modality Country Paul et al. 41 Another recent study found that a subset of features extracted 66 Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. 1989, Davnall et al 2012, Thibault et al 2013, Aerts et al 2014, Rahmim et al 2016). 1 INTRODUCTION Clinical radiological imaging, such as computed tomography (CT), is a mainstay modality for diagnosis, screening, intervention planning, and follow‐up for cancer patients worldwide. 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. eCollection 2014. Crossref, Medline, Google Scholar 19. [] showed the prognostic powers of image features (statistical features and texture features) that have been derived solely from medical (CT) images of lung cancer patients treated with radiation therapy or radiochemotherapy, and the correlations of the image features with gene mutations. Aerts HJ, Velazquez ER, Leijenaar RT, et al. Nature Communications, 2014, 5(1): 4006. Parmar C, Rios Velazquez E, Leijenaar R, et al. Pubmed and Embase were searched up the terms radiomics or radiogenomics and gliomas or glioblastomas until February 2019. PLoS One. (2016) [24] 65 Esophageal cancer PET France Huynh et al. Radiomic features not only provide an objective and quantitative way to assess tumour phe- notype, they have also found a wide-range of potential applications in oncology. Nat Commun … 2014;5:4006. Please share how this access benefits you. Robust radiomics feature quantification using semiautomatic volumetric segmentation. 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