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