The prediction of response to treatment and of prognosis is essential in clinical practice in the era of precision medicine [2]. 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. 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 … Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. The aim of radiomics is aiding clinical decision-making and outcome prediction for more personalized medicine. J Comput Assist Tomogr. Radiomics and liquid biopsy in oncology: The holons of systems medicine. 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. 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. 2018;48(1):3–6. 2019;49(5):1489–98. 2018;28:43–50. 2020;37(4):1-18. Soukup V, Capoun O, Cohen D, Hernandez V, Burger M, Comperat E, et al. 2). 2005;11(19 Pt 1):7012–22. 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. 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 Standard phantoms such as the CT phantom have become the standard of the industry (Fig. A 20-gene model for molecular nodal staging of bladder cancer: development and prospective assessment. Join Competition. Neri E et al. In this article, we discuss two main sets of challenges faced in the field of radiomics. 237 Accesses. Eur Urol. The phantom is based on the American Association of Physicists in Medicine (Task Group Report-1) and has several sections to evaluate imaging performance. What uncertainties do we need in Bayesian deep learning for computer vision? 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. https://doi.org/10.1158/1078-0432.CCR-05-0177. https://doi.org/10.1016/j.ebiom.2018.01.044. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. Despite the promising results, radiomics faces multiple challenges . Typical radiomics workflow. Radiomics: the bridge between medical imaging and personalized medicine. The outcome uncertainty brings additional challenges of using radiomics for cancer diagnosis and treatment outcome prognosis. Article  Radiomics and liquid biopsy in oncology: the holons of systems medicine. Curr Oncol Rep. 2018;20(6):48. https://doi.org/10.1007/s11912-018-0693-y. Prediction models: revolutionary in principle, but do they do more good than harm? Adv Radiat Oncol. 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. Eur Radiol. 2016;66(2):115–32. Research on this topic has focused on finding predictors of rectal cancer staging and chemoradiation treatment response from medical images. https://doi.org/10.1016/j.ejca.2011.11.036. If the address matches an existing account you will receive an email with instructions to reset your password. 2011;29(22):2951–2. 2, No. PubMed Central  Google Scholar. Artificial intelligence and radiomics in nuclear medicine: potentials and challenges Cumali Aktolun1 # Springer-Verlag GmbH Germany, part of Springer Nature 2019, corrected publication 2019 Abstract Artificial intelligence involves a wide range of smart techniques that are applicable to medical services including nuclear medicine. https://doi.org/10.1158/1078-0432.CCR-04-2409. PubMed  2020;37(4):1-18. 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.. Risk stratification tools and prognostic models in non-muscle-invasive bladder cancer: a Critical Assessment from the European Association of Urology Non-muscle-invasive Bladder Cancer Guidelines Panel. © 2021 Springer Nature Switzerland AG. Each of these individual processes poses unique challenges. Bladder cancer outcome and subtype classification by gene expression. Eur J Cancer. For example, … 2019;212(5):1060–9. 3332018022); Beijing Municipal Natural Science Foundation (Grant No. Radiomics has the potential to personalize patient treatment by using medical images that are already being acquired in clinical practice. https://doi.org/10.1002/mp.12510. Many challenges remain in the field of radiomics, not least, the need for consensus, reproducibility, standardization, and prospective validation in clinical trials 17, 67. 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. Asian Pac J Cancer Prev. 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. Koshkin VS, Grivas P. Emerging role of immunotherapy in advanced urothelial carcinoma. A systematic review of neoadjuvant and adjuvant chemotherapy for muscle-invasive bladder cancer. Learn more about Institutional subscriptions. Test-retest data for radiomics feature stability analysis: generalizable or study-specific? 2018;9:1474. https://doi.org/10.3389/fimmu.2018.01474. 8. Catto JWF, Abbod MF, Wild PJ, Linkens DA, Pilarsky C, Rehman I, et al. Limkin EJ, Sun R, Dercle L, et al. 2019PT320008 and 2018PT32003); and National Natural Science Foundation of China (Grant No. Overview. 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. Biomarkers for Clinical Benefit of Immune Checkpoint Inhibitor Treatment-A Review From the Melanoma Perspective and Beyond. https://doi.org/10.1038/nrurol.2017.179. 2016;17(1):381–6. Current status of Radiomics for cancer management: Challenges versus opportunities for clinical practice 1 | INTRODUCTION Radiomics, the high‐throughput extraction and analysis of features from medical images, is a promising field for characterizing tumor phenotype and normal tissue injury post‐radiotherapy. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, 100190, China 2. Part of Springer Nature. https://doi.org/10.1016/j.eururo.2017.06.012. Nat Rev Urol. Fig 1. Typical radiomics workflow. In recent years, we have witnessed the progress of radiomics in methodologies and clinical applications. https://doi.org/10.1200/JCO.2011.36.1329. Theodora Katsila ML, Patrinos GP, Aristotelis B, Dimitrios K. The new age of -omics in urothelial cancer—re-wording its diagnosis and treatment. 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. Nat Commun. Purpose of Review. Correspondence to 2016;42(2):561–8. Systematic review of immune checkpoint inhibition in urological cancers. PubMed  Mammen S, Krishna S, Quon M, Shabana WM, Hakim SW, Flood TA, et al. Does the extent of lymphadenectomy in radical cystectomy for bladder cancer influence disease-free survival? Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging. Google Scholar. 2018;19(9):1180–91. This article reviews the advances in the application of radiomics in lung cancer, head and neck cancer, and other cancer sites. A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors. Miyazaki J, Nishiyama H. Epidemiology of urothelial carcinoma. 2012;48(4):441–6. 2005;48(2):202–5. https://doi.org/10.1016/j.ejrad.2009.01.050. 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. 8 teams; 2 years ago; Overview Data Notebooks Discussion Leaderboard Rules. An important challenge is the collection and acquisition of (large amounts of) suitable imaging data, which is difficult due to evolving technology, lack of standardization protocols and differences in cohorts and protocols between institutes. EBioMedicine. Robert J. Gillies, PhD Paul E. Kinahan, PhD Hedvig Hricak, MD, PhD, Dr(hc) radiomics: Images Are More than Pictures, They Are Data 1 This copy is for personal use only. 2017;44(11):5814–23. Participation in radiomics challenges necessitates a good-faith commitment on the part of contestants to follow through with the challenge, even in the face of unsatisfactory model performance. https://doi.org/10.1016/j.eururo.2009.10.029. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. https://doi.org/10.1007/s00261-016-0897-2. https://doi.org/10.1007/s13244-018-0657-7. 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. J Urol. Cancer statistics in China, 2015. Use of quantitative T2-weighted and apparent diffusion coefficient texture features of bladder cancer and extravesical fat for local tumor staging after transurethral resection. Fig 1. Jpn J Clin Oncol. 81901742). Withdrawals are antithetical to the mission of radiomics challenges as a learning tool for both challenge contestants and organizers to advance the field. 2019. https://doi.org/10.1007/s00330-019-06222-8. Mahdavifar N, Ghoncheh M, Pakzad R, Momenimovahed Z, Salehiniya H. Epidemiology, incidence and mortality of bladder cancer and their relationship with the development index in the world. Its potential has been revealed in helping clinical experts to uncover cancer characteristics that fail to be appreciated by naked eyes. Cha KH, Hadjiiski L, Chan H-P, Weizer AZ, Alva A, Cohan RH, et al. https://doi.org/10.1016/j.eururo.2017.03.047. 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. 2005;11(11):4044–55. Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. 2017;46(5):1281–8. https://doi.org/10.1016/S1470-2045(10)70296-5. Many challenges remain in the field of radiomics, not least, the need for consensus, reproducibility, standardization, and prospective validation in clinical trials 17, 67. It has the potential to uncover disease characteristics that are difficult to identify by human vision alone. Hao Sun or Zhengyu Jin. 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 … Abol-Enein H, Tilki D, Mosbah A, El-Baz M, Shokeir A, Nabeeh A, et al. Powles T, Smith K, Stenzl A, Bedke J. Radiomics is a quantitative approach to medical image analysis targeted at deciphering the morphologic and functional features of a lesion. 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. Standard phantoms such as the CT phantom have become the standard of the industry (Fig. In recent years, we have witnessed the progress of radiomics in methodologies and clinical applications. Eur Urol. PubMed Google Scholar. / Magnetic Resonance Imaging 30 (2012) 1234–1248 1235. institutions and vendors. Eur J Radiol. Zehnder P, Studer UE, Skinner EC, Dorin RP, Cai J, Roth B, et al. 2010;57(3):398–406. 4, © 2021 Radiological Society of North America, Radiomics: images are more than pictures, they are data, Computed tomography (CT) exams. One of the major challenges lies in the optimal collection and integration of multiple data sources that can produce accurate and robust predictions… Nishiyama H. Asia consensus statement on NCCN clinical practice guideline for bladder cancer. An exploratory radiomics approach to quantifying pulmonary function in CT images, Radiomics nomogram analyses for differentiating pneumonia and acute paraquat lung injury, HIV-infected patients with opportunistic pulmonary infections misdiagnosed as lung cancers: the clinicoradiologic features and initial application of CT radiomics, Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children, Radiomic measures from chest high-resolution computed tomography associated with lung function in sarcoidosis, The utility of quantitative CT radiomics features for improved prediction of radiation pneumonitis, Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development, Radiomics-based assessment of radiation-induced lung injury after stereotactic body radiotherapy, Radiomics-based differentiation of lung disease models generated by polluted air based on x-ray computed tomography data, Serial automated quantitative CT analysis in idiopathic pulmonary fibrosis: functional correlations and comparison with changes in visual CT scores, Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis, Quantitative computed tomography imaging of interstitial lung diseases, Quantitative stratification of diffuse parenchymal lung diseases, Selection of glucocorticoid-sensitive patients in interstitial lung disease secondary to connective tissue diseases population by radiomics, Chest CT texture analysis for response assessment in systemic sclerosis, Differences in texture analysis parameters between active alveolitis and lung fibrosis in chest ct of patients with systemic sclerosis: a feasibility study, Incidental findings on chest CT imaging are associated with increased COPD exacerbations and mortality, Computer-aided classification of interstitial lung diseases via MDCT: 3D adaptive multiple feature method (3D AMFM), Global burden of COPD: risk factors, prevalence, and future trends, Landmark papers in respiratory medicine: Automatic quantification of emphysema and airways disease on computed tomography, Quantitative computed tomography in COPD: possibilities and limitations, Texture-based analysis of COPD: a data-driven approach, Unsupervised discovery of emphysema subtypes in a large clinical cohort, Unsupervised discovery of spatially-informed lung texture patterns for pulmonary emphysema: the MESA COPD study, The prevalence of vertebral deformity in European men and women: the European Vertebral Osteoporosis study, Ten-year risk of osteoporotic fracture and the effect of risk factors on screening strategies, NIH consensus development panel on osteoporosis prevention, diagnosis, and therapy, March 7-29, 2000: highlights of the conference, The effect of intravertebral heterogeneity in microstructure on vertebral strength and failure patterns, Inter-observer and inter-examination variability of manual vertebral bone attenuation measurements on computed tomography, Opportunistic osteoporosis screening in multi-detector CT images via local classification of textures, Vertebral body insufficiency fractures: detection of vertebrae at risk on standard CT images using texture analysis and machine learning, Feasibility of opportunistic osteoporosis screening in routine contrast-enhanced multi detector computed tomography (MDCT) using texture analysis, Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study, Diagnosing sarcopenia on thoracic computed tomography: quantitative assessment of skeletal muscle mass in patients undergoing transcatheter aortic valve replacement, Multicentre evaluation of multidisciplinary team meeting agreement on diagnosis in diffuse parenchymal lung disease: a case-cohort study, Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study, Relationship and prognostic value of modified coronary artery calcium score, FEV1, and emphysema in lung cancer screening population: the MILD trial, Estimation of cardiovascular risk on routine chest CT: Ordinal coronary artery calcium scoring as an accurate predictor of Agatston score ranges, Automatic coronary calcium scoring in low-dose chest computed tomography, Automated coronary artery calcification scoring in non-gated chest CT: agreement and reliability, Radiomics versus visual and histogram-based assessment to identify atheromatous lesions at coronary CT angiography: an ex vivo study, Measuring computed tomography scanner variability of radiomics features, Standardization of features extracted from CT images of texture phantoms, Oncology society rolls out big-data initiative, tells why radiology should care. Rijnders M, de Wit R, Boormans JL, Lolkema MPJ, van der Veldt AAM. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges Theranostics. Supplemental material is available for this article. 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.ebiom.2018.07.029. Recent radiomics publications. The mere presence of noninvasive nature of medical images and possibility of high spatial and temporal resolution provide major benefits over using simplistic metrics that would overlook the wealth of … 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. (2)Medical Physics, European Institute of Oncology, Milan, Italy. https://doi.org/10.1002/jmri.25669. Nat Rev Clin Oncol. Clin Cancer Res. ), Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria; and Department of Information Systems, University of Applied Sciences of Western Switzerland, Sierre, Switzerland (H.M.). Pesapane F et al. Advanced feature analysis, Clarke NW, Daneshmand S, Krishna S Zheng... 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