Adv Radiat Oncol. Faiq A. Shaikh, MD, University of Pittsburgh Medical Center; Omer Awan, MD; Christopher Deible, MD, PhD; Brian Kolowitz, MBA, DSc; Kenneth Hendrata, MBA . 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. At the data collection stage, imaging data is combined with clinical and histopathological data. https://doi.org/10.1016/j.eururo.2012.05.048. Insights Imaging 2018; 9(6): 915-24. eCollection 2019. Rijnders M, de Wit R, Boormans JL, Lolkema MPJ, van der Veldt AAM. outlines this challenge in detail, specifically describing the impact of V. Kumar et al. Eur Radiol Exp. Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Blaveri E, Brewer JL, Roydasgupta R, Fridlyand J, DeVries S, Koppie T, et al. Eur Radiol. Am J Roentgenol. Neri E, Del Re M, Paiar F, Erba P, Cocuzza P, Regge D, et al. By exploiting imaging data from clinical routine, a much larger amount of data could be used than in clinical trials. 2010;57(3):398–406. 2018. https://doi.org/10.1016/j.euf.2018.11.005. Research on this topic has focused on finding predictors of rectal cancer staging and chemoradiation treatment response from medical images. Radiomics analysis of multiparametric MRI for the preoperative evaluation of pathological grade in bladder cancer tumors. Radiomics: the facts and the challenges of image analysis. Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, et al. Wu S, Zheng J, Li Y, Wu Z, Shi S, Huang M, et al. 2019;212(5):1060–9. https://doi.org/10.2214/ajr.18.20718. 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 … Bladder cancer treatment response assessment in CT using radiomics with deep-learning. Overview. Part of Springer Nature. 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. Eur Urol Focus. This literature … In recent years, we have witnessed the progress of radiomics in methodologies and clinical applications. Metrics details. Med Oncol. 2018;3(3):331–8. Test-retest data for radiomics feature stability analysis: generalizable or study-specific? 2012;48(4):441–6. Google Scholar. Med Oncol. https://doi.org/10.1111/iju.13376. 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. https://doi.org/10.1016/j.eururo.2009.09.013. 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.. PubMed Central  https://doi.org/10.1016/j.adro.2018.04.011. Quantitative imaging in cancer evolution and ecology. https://doi.org/10.1002/jmri.26327. Radiomics - research challenges identified by EURAMED Prof. Christoph Hoeschen EURAMED Past-President, member of ExB, head of scientific committee . 3 FUTURE CHALLENGES OF RADIOMICS IN RADIOTHERAPY. In this article, we discuss two main sets of challenges faced in the field of radiomics. 2009;70(2):232–41. The methods presented may, in principle, aid clinicians with the appropriate treatment planning options. Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No. This review describes challenges and solutions of a radiomics analysis of chronic obstructive pulmonary disease, osteoporosis, sarcopenia, interstitial lung disease, and coronary artery calcifications at nononcologic routine chest CT. Powles T, Smith K, Stenzl A, Bedke J. Development and validation of an MRI-based radiomics signature for the preoperative prediction of lymph node metastasis in bladder cancer. Additionally, we comment on the future challenges of radiomic research. Wang H, Hu D, Yao H, Chen M, Li S, Chen H, et al. Meeks JJ, Bellmunt J, Bochner BH, Clarke NW, Daneshmand S, Galsky MD, et al. A general workflow of radiomics is depicted in Figure 2. 2011;60(3):572–7. 2018;9:1474. https://doi.org/10.3389/fimmu.2018.01474. 8 teams; 2 years ago; Overview Data Notebooks Discussion Leaderboard Rules. Clin Cancer Res. Methodology. José Maria Moreira 1, Inês Santiago 2, João Santinha 1, Nuno Figueiredo 3, Kostas Marias 4, Mário Figueiredo 5, Leonardo Vanneschi 6 & Nickolas Papanikolaou 1 Current Colorectal Cancer Reports volume 15, pages 175 – 180 (2019)Cite this article. 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. Radiomics: extracting more information from medical images using advanced feature analysis. Radiomics and liquid biopsy in oncology: The holons of systems medicine. Cancer statistics in China, 2015. Radiomics and liquid biopsy in oncology: the holons of systems medicine. 7. 6. Eur J Cancer. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges Zhenyu Liu 1, 5*, Shuo Wang1,5*, 1, Di Dong1, 5*, 3Jingwei Wei 5*, Cheng Fang *, Xuezhi Zhou1, 4, Kai Sun 4, Longfei Li1, 6, Bo Li3 , Meiyun Wang2 , Jie Tian1, 4, 7 1. 2018;68(1):7–30. Physicians and physicists should indeed be aware of the large risks of biases gener-ated by the lack of standardization in the acquisition pro-cess, reconstruction of images, postprocessing, or statistical learning. Cancer statistics, 2018. There are quite a lot of challenges ahead of us for applying radiomics in daily practice to improve patient care. Typical radiomics workflow. 2019. https://doi.org/10.1002/jmri.26749. Radiomics: Current Challenges in Clinical Validation . Many challenges remain in the field of radiomics, not least, the need for consensus, reproducibility, standardization, and prospective validation in clinical trials (17, 67) . CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China 2. Xu X, Wang H, Du P, Zhang F, Li S, Zhang Z, et al. Radiology 2013; 269(1): 8-15. Herein, we review recent developments in radiomics, its applications to lung cancer treatments, and the challenges associated with radiomics as a tool for precision diagnostics and theranostics. Birkhahn M, Mitra AP, Williams AJ, Lam G, Ye W, Datar RH, et al. Google Scholar. Asian Pac J Cancer Prev. Clin Cancer Res. 2016;66(2):115–32. Each of these individual processes poses unique challenges. 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. https://doi.org/10.1016/j.ejrad.2009.01.050. J Magn Reson Imaging. 2016;17(1):381–6. Accepted for publication Dec 14, 2017. Supplemental material is available for this article. Article  27 August 2020 | Radiology: Cardiothoracic Imaging, Vol. Google Scholar. 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. 2018;42(2):204–10. Google Scholar. The challenges of radiomics for functional imaging are similar to the challenges of contrast-enhanced anatomical imaging radiomics, where the variability in the injected radiopharmaceutical activity, the time between injection and image acquisition, and acquisition time per bed position have profound implications on the reproducibility of radiomics features . Numerous logistical, computational and clinical challenges remain to unlocking the full potential of the radiomics approach. Nat Rev Urol. Sci Rep. 2017. https://doi.org/10.1038/s41598-017-09315-w. Vickers AJ. Description Evaluation Prizes. Author information: (1)Department of Radiology, IEO, European Institute of Oncology, IRCCS, Milan, IT, Italy. Challenges and Prospects for Radiomics. J Natl Compr Cancer Netw. © 2021 Springer Nature Switzerland AG. Radiomics and Radiogenomics seeks to cover the fundamental principles, technical basis, and clinical applications of radiomics and radiogenomics, with a focus on oncology. https://doi.org/10.1200/JCO.2011.36.1329. Buder-Bakhaya K, Hassel JC. New discoveries and technologies have begun to change paradigms of urothelial cancer therapy in recent years. Background . https://doi.org/10.6004/jnccn.2017.0156. 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. 2018;34:76–84. https://doi.org/10.1038/nrurol.2017.179. 2018;48(1):3–6. Spiess PE, Agarwal N, Bangs R, Boorjian SA, Buyyounouski MK, Clark PE, et al. Its potential has been revealed in helping clinical experts to uncover cancer characteristics that fail to be appreciated by naked eyes. Purpose of Review. Join Competition. 7192176); Basic Scientific Research Program of Chinese Academy of Medical Sciences (Grant Nos. A systematic review of neoadjuvant and adjuvant chemotherapy for muscle-invasive bladder cancer. https://doi.org/10.1093/jjco/hyx130. Abstract. This is a preview of subscription content, access via your institution. Despite the promising results, radiomics faces multiple challenges . 2018;2(1):36. Imaging Biz Web Site, Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures, Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study, Variability in CT lung-nodule quantification: Effects of dose reduction and reconstruction methods on density and texture based features, Effect of tube current on computed tomography radiomic features. https://doi.org/10.1158/1078-0432.CCR-04-2409. With rapid development in this area, radiomics has already been applied in urothelial cancer to predict pathological grade, clinical stage, lymph node metastasis and treatment response demonstrating promising results. 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. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. This review explains solutions to overcome heterogeneity in routine data such as the use of imaging repositories, the standardization of radiomic features, algorithmic approaches to improve feature stability, test-retest studies, and the evolution of deep learning for modeling radiomics features. Immune checkpoint inhibition in metastatic urothelial cancer. Garapati SS, Hadjiiski L, Cha KH, Chan H-P, Caoili EM, Cohan RH, et al. Eur Urol. Forghani et al. 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.. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. V. Kumar et al. Radiomics: extracting more information from medical images using advanced feature analysis. Radiomics is a quantitative approach to medical image analysis targeted at deciphering the morphologic and functional features of a lesion. Wu S, Zheng J, Li Y, Yu H, Shi S, Xie W, et al. Eur J Cancer 2012;48:441-6. 2017;46(5):1281–8. The process and challenges in radiomics. https://doi.org/10.1007/s13244-018-0657-7. Multicenter CT phantoms public dataset for radiomics reproducibility tests, Radiomics: the bridge between medical imaging and personalized medicine, Robust radiomics feature quantification using semiautomatic volumetric segmentation, Radiomics of lung nodules: a multi-institutional study of robustness and agreement of quantitative imaging features, Image biomarker standardisation initiative, Computational radiomics system to decode the radiographic phenotype, Radiomic machine-learning classifiers for prognostic biomarkers of head and neck cancer, Unsupervised domain adaptation in brain lesion segmentation with adversarial networks, Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach, Deep learning-based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses, Regression concept vectors for bidirectional explanations in histopathology. 2019PT320008 and 2018PT32003); and National Natural Science Foundation of China (Grant No. Magn Reson Imaging 30 : 1234-1248, 2012 9 Traverso A, et al : Repeatability and Reproducibility of Radiomic Features : A Systematic Review. Eur Urol. https://doi.org/10.1016/j.eururo.2017.06.012. CAS  Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. 237 Accesses. Bladder cancer stage and outcome by array-based comparative genomic hybridization. Hao Sun or Zhengyu Jin. 2005;11(11):4044–55. Tax calculation will be finalised during checkout. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach Nature Communications. 2011;12(2):137–43. / Magnetic Resonance Imaging 30 (2012) 1234–1248 1235. institutions and vendors. Many challenges remain in the field of radiomics, not least, the need for consensus, reproducibility, standardization, and prospective validation in clinical trials 17, 67. Jpn J Clin Oncol. 2017;15(10):1240–67. 237 Accesses. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. https://doi.org/10.1016/j.eururo.2005.04.006. EBioMedicine. Use of quantitative T2-weighted and apparent diffusion coefficient texture features of bladder cancer and extravesical fat for local tumor staging after transurethral resection. Radiomics: Challenges and Opportunities Parnian Afshary, Student Member, IEEE, Arash Mohammadiy, Senior Member, IEEE, Konstantinos N. Plataniotisz, Fellow, IEEE, Anastasia Oikonomou , and Habib Benali>, Member, IEEE yConcordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, Canada zDepartment of Electrical and Computer Engineering, University of … Bladder cancer outcome and subtype classification by gene expression. Quantitative identification of nonmuscle-invasive and muscle-invasive bladder carcinomas: a multiparametric MRI radiomics analysis. European Alliance for Medical Radiation Protection Research www.euramed.eu Vision •To lead the European research activities in medical radiation protection and to assume an umbrella function for the harmonisation of practice to advance … More studies correlating radiomic features with disease outcomes and molecular attributes are also needed … EBioMedicine. Curr Oncol Rep. 2018;20(6):48. https://doi.org/10.1007/s11912-018-0693-y. Lancet Oncol. Rizzo S(1), Botta F(2), Raimondi S(3), Origgi D(2), Fanciullo C(4), Morganti AG(5), Bellomi M(6). The outcome uncertainty brings additional challenges of using radiomics for cancer diagnosis and treatment outcome prognosis. 8 Kumar V, et al : Radiomics : The process and the challenges. Cha KH, Hadjiiski L, Chan H-P, Weizer AZ, Alva A, Cohan RH, et al. 2011;186(4):1261–8. / Magnetic Resonance Imaging 30 (2012) 1234–1248 1235. institutions and vendors. Miyazaki J, Nishiyama H. Epidemiology of urothelial carcinoma. Eur Urol. CAS  2017;72(3):411–23. Ann Oncol 2017;28:1191-206. https://doi.org/10.1007/s42058-019-00021-2, DOI: https://doi.org/10.1007/s42058-019-00021-2, Over 10 million scientific documents at your fingertips, Not logged in … 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. 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 Predicting recurrence and progression of noninvasive papillary bladder cancer at initial presentation based on quantitative gene expression profiles.
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