237 Accesses. Standard phantoms such as the CT phantom have become the standard of the industry (Fig. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology Elaine Limkin, Roger Sun, Laurent Dercle, Evangelia Zacharaki, Charlotte Robert, Sylvain Reuzé, Antoine Schernberg, Nikos Paragios, Eric Deutsch, Charles Ferté To cite this version: Elaine Limkin, Roger Sun, Laurent Dercle, Evangelia Zacharaki, Charlotte Robert, et al.. Theodora Katsila ML, Patrinos GP, Aristotelis B, Dimitrios K. The new age of -omics in urothelial cancer—re-wording its diagnosis and treatment. There are quite a lot of challenges ahead of us for applying radiomics in daily practice to improve patient care. Abdom Radiol. Still, there is only little literature on the implementation of radiomics in clinical routine chest CT scans. This study was funded by the Fundamental Research Funds for the Central Universities (Grant No. Challenges and Promises of Radiomics for Rectal Cancer. The radiomics enterprise can be divided into distinct processes, each with its own challenges that need to be overcome: (a) image acquisition and reconstruction, (b) image segmentation and rendering, (c) feature extraction and feature qualification and (d) databases and data sharing for eventual (e) ad hoc informatics analyses. https://doi.org/10.1016/j.eururo.2011.05.062. Google Scholar. Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. Radiomic methods can be applied across various malignant conditions to identify tumor phenotype characteristics in the images that correlate with their likelihood of survival, as well as their association with the underlying biology. Xu X, Zhang X, Tian Q, Wang H, Cui LB, Li S, et al. Radiomics: the facts and the challenges of image analysis. Felsenstein KM, Theodorescu D. Precision medicine for urothelial bladder cancer: update on tumour genomics and immunotherapy. Soukup V, Capoun O, Cohen D, Hernandez V, Burger M, Comperat E, et al. 8 teams; 2 years ago; Overview Data Notebooks Discussion Leaderboard Rules. Chinese Journal of Academic Radiology outlines this challenge in detail, specifically describing the impact of / Magnetic Resonance Imaging 30 (2012) 1234–1248 1235. institutions and vendors. Miyazaki J, Nishiyama H. Epidemiology of urothelial carcinoma. Radiomics: extracting more information from medical images using advanced feature analysis. What uncertainties do we need in Bayesian deep learning for computer vision? Gatenby RA et al. Radiomics analysis of multiparametric MRI for the preoperative evaluation of pathological grade in bladder cancer tumors. The methods presented may, in principle, aid clinicians with the appropriate treatment planning options. https://doi.org/10.1016/j.eururo.2005.04.006, https://doi.org/10.1007/s11912-018-0693-y, https://doi.org/10.1016/j.ebiom.2018.01.044, https://doi.org/10.1016/j.ejca.2011.11.036, https://doi.org/10.1038/nrclinonc.2017.141, https://doi.org/10.1007/s13244-018-0657-7, https://doi.org/10.1007/s00261-016-0897-2, https://doi.org/10.1097/RCT.0000000000000664, https://doi.org/10.1007/s00330-019-06222-8, https://doi.org/10.1016/j.adro.2018.04.011, https://doi.org/10.1016/j.eururo.2011.05.062, https://doi.org/10.1016/j.juro.2011.06.004, https://doi.org/10.1016/j.ebiom.2018.07.029, https://doi.org/10.1158/1078-0432.Ccr-17-1510, https://doi.org/10.1016/j.euf.2018.11.005, https://doi.org/10.1016/j.eururo.2012.05.048, https://doi.org/10.1038/s41598-017-09315-w, https://doi.org/10.1016/j.ejrad.2009.01.050, https://doi.org/10.1158/1078-0432.CCR-05-0177, https://doi.org/10.1158/1078-0432.CCR-04-2409, https://doi.org/10.1016/j.eururo.2009.09.013, https://doi.org/10.1016/j.eururo.2009.10.029, https://doi.org/10.1016/S1470-2045(10)70296-5, https://doi.org/10.1016/j.eururo.2017.03.047, https://doi.org/10.1016/j.eururo.2017.06.012, https://doi.org/10.1016/s1470-2045(18)30413-3, https://doi.org/10.1007/s42058-019-00021-2. 2019PT320008 and 2018PT32003); and National Natural Science Foundation of China (Grant No. Challenges and Promises of Radiomics for Rectal Cancer. AbstractPurpose of ReviewThis literature review aims to gather the relevant works published on the topic of Radiomics in Rectal Cancer. 2005;11(19 Pt 1):7012–22. Radiomics is a quantitative approach to medical image analysis targeted at deciphering the morphologic and functional features of a lesion. Radiomics: Extracting more information from medical images using advanced feature analysis European Journal of Cancer. Recently, with the development of computational and imaging technology, radiotherapy has brought unlimited opportunities driven by radiomics in individual cancer treatment and precision medicine care. Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. The prediction of response to treatment and of prognosis is essential in clinical practice in the era of precision medicine [2]. pitfalls and challenges linked to radiomics analysis in PET imaging, and (4) an overview of statistical analysis and machine learning concepts. https://doi.org/10.1200/JCO.2011.36.1329. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Nat Rev Urol. Ann Oncol 2017;28:1191-206. PubMed Central  Reasons are heterogeneous CT scanning protocols and the resulting technical variability (eg, different slice thicknesses, reconstruction kernels or timings after contrast material administration) in routine CT imaging data. However, with the deepening of the radiomic research, more and more limitations have been exposed, including the poor reproducibility of the research results and … This review summarizes the recent state of the art of studies aiming to develop quantifiable imaging biomarkers at chest CT, such as for osteoporosis, chronic obstructive pulmonary disease, interstitial lung disease, and coronary artery disease. Fig 1. The outcome uncertainty brings additional challenges of using radiomics for cancer diagnosis and treatment outcome prognosis. From the Computational Imaging Research Laboratory (J.H., G.L) of the Department of Biomedical Imaging and Image-guided Therapy (S.R., F.P., H.P. Radiomics, being noninvasive and easy to perform, has shown great potential in oncology by providing valuable information about tumor type, aggressiveness, progression, response to treatment and prognosis and enabling us to gain insights into the true utility of personalized medicine in the management of cancer in the near future. Correspondence to However, an adequate sample size as a statistical necessity for radiomics studies is often difficult to achieve in prospective trials. Typical radiomics workflow. 2019;49(5):1489–98. Since the concept of radiomics was proposed in 2012, the research using radiomics has been increasing year by year, and good research results have been achieved in various fields. Learn more about Institutional subscriptions. Tax calculation will be finalised during checkout. Garapati SS, Hadjiiski L, Cha KH, Chan H-P, Caoili EM, Cohan RH, et al. Radiology 2013; 269(1): 8-15. Enter your email address below and we will send you your username, If the address matches an existing account you will receive an email with instructions to retrieve your username. Zhang G-M-Y, Sun H, Shi B, Jin Z-Y, Xue H-D. Quantitative CT texture analysis for evaluating histologic grade of urothelial carcinoma. / Magnetic Resonance Imaging 30 (2012) 1234–1248 1235. institutions and vendors. 2019 Feb 12;9(5):1303-1322. doi: 10.7150/thno.30309. Rutman AM, Kuo MD. Chin J Acad Radiol 2, 56–62 (2020). https://doi.org/10.1002/jmri.25669. describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision mak-ing, particularly in the care of patients with cancer. Radiomics: the bridge between medical imaging and personalized medicine. Sun R, Limkin EJ, Vakalopoulou M, Dercle L, Champiat S, Han SR, et al. Radiomics is a quantitative approach to medical image analysis targeted at deciphering the morphologic and functional features of a lesion. A prospective single-center study. https://doi.org/10.1007/s13244-018-0657-7. 1 Shuaifuyuan, Wangfujing Street, Dongcheng District, Beijing, 100730, People’s Republic of China, Gumuyang Zhang, Lili Xu, Hao Sun & Zhengyu Jin, You can also search for this author in 2018;68(1):7–30. The application of artificial intelligence to microarray data: identification of a novel gene signature to identify bladder cancer progression. https://doi.org/10.3322/caac.21338. In this review, we highlight advances in clinical applications of radiomics in urothelial cancer, discuss about the challenges and implications of radiomics for radiologists and suggest the future directions that we could move toward in order to fully realize the potentials of radiomics to improve personalized management of patients with urothelial cancer. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition and reconstruction, image segmentation, features extraction and qualification, analysis, and model building. 2018;34:76–84. Mammen S, Krishna S, Quon M, Shabana WM, Hakim SW, Flood TA, et al. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. The radiomic process can be divided into distinct steps with definable inputs and outputs, such as image acquisition and reconstruction, image segmentation, features extraction and qualification, analysis, and model building. Google Scholar. 2018;3(3):331–8. Rizzo S et al. https://doi.org/10.1093/jjco/hyx130. Rijnders M, de Wit R, Boormans JL, Lolkema MPJ, van der Veldt AAM. Main topics that were covered include general opportunities and challenges in Artificial Intelligence / Radiomics in imaging, the envisioned interaction in a joint-imaging-platform (i.e. The basic steps include image sequestration and preacquisition data salvage, data transfer and repository maintenance, image segmentation, feature extraction and classification, covariance matrices and data modeling, integration into clinical decision support … Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The radiomics enterprise can be divided into distinct processes, each with its own challenges that need to be overcome: (a) image acquisition and reconstruction, (b) image segmentation and rendering, (c) feature extraction and feature qualification and (d) databases and data sharing for eventual (e) ad hoc informatics analyses. 8 Kumar V, et al : Radiomics : The process and the challenges. https://doi.org/10.3322/caac.21442. Eur Urol Focus. Xu X, Wang H, Du P, Zhang F, Li S, Zhang Z, et al. One of the major challenges lies in the optimal collection and integration of multiple data sources that can produce accurate and robust predictions… 7192176 ) ; Basic scientific research Program of Chinese Academy of medical Sciences ( Grant No in practice... For individualized recurrence stratification of bladder cancer invasion from t2-weighted Magnetic Resonance imaging 30 ( )! To be appreciated by naked eyes are we Going tomography analysis for diagnosis of pathological grade and stage upper! 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