aerts et al radiomics

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Nat Commun … Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 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. CAS Article PubMed PubMed Central Google Scholar Prognosis classification in glioblastoma multiforme using multimodal MRI derived heterogeneity textural features: impact of pre-processing choices. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. 2014;9(7):e102107. Nat Commun 2014; 5:4006 [Google Scholar] 2. found a Aerts HJ, Velazquez ER, Leijenaar RT, et al. Radiology. • 1st point of attention: Metabolic information is sound only if a number of prerequisites are Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by non-invasive imaging using a quantitative radiomics approach. doi: 10.1371/journal.pone.0102107. (Supplementary) Nature communications. SPIE Medical Imaging 2016 2. Hugo J. W. L. Aerts, Emmanuel Rios Velazquez, Ralph T. H. Leijenaar, Chintan Parmar, Patrick Grossmann, Sara Cavalho, et al. 2014 Radiomics CT Signature Performance - Signature performed significantly better compared to volume in all datasets. Aerts et al demonstrated a CT-based radiomics signature, which captured heterogeneity and had significant prognostic value in lung and head-and-neck cancer. To evaluate radiomics analysis in neuro-oncologic studies according to a radiomics quality score (RQS) system to find room for improvement in clinical use. 1 Radiomics refers to high‐throughput automated characterization of the tumor phenotype by analyzing quantitative features derived from a radiological image. [ PubMed ] Radiomics 1. 1. 2014;9(7):e102107. Aerts at al. Computational Radiomics System to Decode the Radiographic Phenotype. The issues raised above are drawbacks of precision medicine. 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. Aerts HJ, et al. However, inclusion of Aerts et al. The premise of radiomics is that quantitative image features can serve as biomarkers characterizing disease. Cancer Res (2017) 77(21):e104–7. (2019) evaluated the correlation between LNM and radiomics features from MRI, and reported that apparent diffusion coefficient (ADC) maps generated from diffusion weighted imaging (DWI) showed the best discrimination performance for LNM. 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). The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping.0(0):191145. 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. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Aerts HJ, Velazquez ER, Leijenaar RT et al. 1989, Davnall et al 2012, Thibault et al 2013, Aerts et al 2014, Rahmim et al 2016). (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 41 Another recent study found that a subset of features extracted 66 Nat Commun 2014;5(1):4006. of patients Cancer type Modality Country Paul et al. Upadhaya, et al. Your story matters Citation Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. 2014;5:4006. In a recent study, Qiu et al 17 evaluated the value of radiomics in predicting the efficacy of intravenous alteplase in the treatment of patients with AIS. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Aerts et al. 27. van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. , Raghunath et al. doi: 10.1158/0008-5472.CAN-17 Radiomics studies of clinical oncology published in literature Study No. Harmonization of the components of this dataset, including into standard DICOM representation, was supported in part by the NCI Imaging Data Commons consortium. 2014; 5 :4006. doi: 10.1038/ncomms5006. Aerts HJ, Velazquez ER, Leijenaar RT, et al. Aerts and colleagues proposed a radiomics signature for predicting overall survival in lung cancer patients treated with radiotherapy []. 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 Nat Commun. 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. Nature Comm. Nat Commun 2014;5:4006. 2 Aerts et al. Nat Commun 5:4006 Nat Commun 5:4006 CAS Article Google Scholar Parmar C, Rios Velazquez E, Leijenaar R, et al. 2016;278(2):563-577. van Griethuysen JJM, Fedorov A, Parmar C, et al. Studies from Huang et al. 2014 Jul 15;9(7):e102107. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. PLoS One. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. 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 ). 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. [] 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. Robust radiomics feature quantification using semiautomatic volumetric segmentation. described a combination of features (size, shape, texture and wavelets) which could predict outcome in patients with lung cancer. Gilles RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Please share how this access benefits you. Aerts HJWL, Velazquez ER, Leijenaar RTH, et al. Crossref, Medline, Google Scholar 19. Nat Commun 2014;5:4006. Hugo J. W. L. Aerts, Emmanuel Rios Velazquez, Ralph T. H. Leijenaar, Chintan Parmar, Patrick Grossmann, Sara Cavalho, et al. [] 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. 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! Robust radiomics feature quantification using semiautomatic volumetric segmentation. In this context, radiomics has gathered attention as imaging can aid in evaluating the whole tumor noninva-sively and repeatedly. Nature Communications, 2014, 5(1): 4006. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. They found that radiomics analysis of heterogeneous thrombi texture was able Aerts et al. Aerts HJWL, Velazquez ER, Leijenaar RTH et al. Vallières, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach The Harvard community has made this article openly available. PLoS One. Pubmed and Embase were searched up the terms radiomics or radiogenomics and gliomas or glioblastomas until February 2019. Robust Radiomics feature quantification using semiautomatic volumetric segmentation. Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. 2014;5:4006. (2016) [24] 65 Esophageal cancer PET France Huynh 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. Mason SJ, . Radiomics is a quantitative approach to medical imaging, which aims at enhancing the existing data available to clinicians by means of advanced mathematical analysis. Radiomics studies must be repeatedly tested and refined by multicenter, large sample, and randomized controlled clinical trials in the future. PLoS One. Hugo Aerts, Computational Imaging and Bioinformatic Laboratory, Dana-Farber Cancer Institute & Harvard Medical School, Boston, Massachusetts, USA. , and Depeursinge et al. Dr Henry Knipe and Dr Muhammad Idris et al. eCollection 2014. Computational radiomics system to decode the From 189 articles, 51 original research articles reporting the diagnostic, prognostic, or predictive utility … Parmar C, Rios Velazquez E, Leijenaar R, et al. *Aerts et al. CAS PubMed PubMed Central 30. This will enable them to … 24 ] 65 Esophageal cancer PET France Huynh et al found a However, inclusion Aerts. Community has made this article openly available Gilles RJ, Kinahan PE, H.... 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