Each of these individual processes poses unique challenges. Article Despite the promising results, radiomics faces multiple challenges . Link/Page Citation 1. Pesapane F et al. Numerous logistical, computational and clinical challenges remain to unlocking the full potential of the radiomics approach. https://doi.org/10.1158/1078-0432.Ccr-17-1510. Rijnders M, de Wit R, Boormans JL, Lolkema MPJ, van der Veldt AAM. https://doi.org/10.1016/j.eururo.2017.06.012. Overview. However, an adequate sample size as a statistical necessity for radiomics studies is often difficult to achieve in prospective trials. 2019. https://doi.org/10.1007/s00330-019-06222-8. Radiomics is an emerging translational field of research aiming to extract mineable high-dimensional data from clinical images. Each of these individual processes poses unique challenges. PET radiomics challenges 18F-FDG PET Radiomics Risk Stratifiers in Head and Neck Cancer: A MICCAI 2018 CPM Grand Challenge. Soukup V, Capoun O, Cohen D, Hernandez V, Burger M, Comperat E, et al. [ 1 ] described sources of variation impairing generalizability and reproducibility of radiomics studies, including: Chinese Journal of Academic Radiology 2019;212(5):1060–9. 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. 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 . 2017;14(12):749–62. Quantitative identification of nonmuscle-invasive and muscle-invasive bladder carcinomas: a multiparametric MRI radiomics analysis. 4, © 2021 Radiological Society of North America, Radiomics: images are more than pictures, they are data, Computed tomography (CT) exams. Clin Cancer Res. Curr Oncol Rep. 2018;20(6):48. https://doi.org/10.1007/s11912-018-0693-y. 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. Metrics details. Machine Learning methods for Quantitative Radiomic Biomarkers . J Magn Reson Imaging. Gatenby RA et al. 2011;186(4):1261–8. V. Kumar et al. 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. 2017;46(5):1281–8. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans, Annual or biennial CT screening versus observation in heavy smokers: 5-year results of the MILD trial, Prospects and Challenges of Radiomics by Using Nononcologic Routine Chest CT, https://www.oecd-ilibrary.org/social-issues-migration-health/computed-tomography-ct-exams/indicator/english_3c994537-en, http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf, https://www.imagingbiz.com/topics/imaging-informatics/oncology-society-rolls-out-big-data-initiative-tells-why-radiology, https://www.cancerdata.org/resource/doi:10.17195/candat.2016.08.1, http://papers.nips.cc/paper/7141-what-uncertainties-do-we-need-in-bayesian-deep-learning-for-computer-vision.pdf. Prediction models: revolutionary in principle, but do they do more good than harm? Specific challenges are addressed when implementing big data concepts with high-throughput image data processing like radiomics and machine learning in a radiooncology environment to support clinical decisions. https://doi.org/10.1038/nrclinonc.2017.141. https://doi.org/10.1002/jmri.25669. Buder-Bakhaya K, Hassel JC. Although PET has the advantage of being able to sensitively interrogate specific and varied abnormalities in tumor biology, its poorer resolution and variable noise pose additional technical limitations. Zhang X, Xu X, Tian Q, Li B, Wu Y, Yang Z, et al. ), 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.). 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. / Magnetic Resonance Imaging 30 (2012) 1234–1248 1235. institutions and vendors. Advanced Bladder Cancer Meta-analysis C. Neoadjuvant chemotherapy in invasive bladder cancer: update of a systematic review and meta-analysis of individual patient data advanced bladder cancer (ABC) meta-analysis collaboration. Purpose of Review. 2017;72(3):411–23. Bladder cancer treatment response assessment in CT using radiomics with deep-learning. https://doi.org/10.1016/j.juro.2011.06.004. See also the commentary by Kay in this issue. Rizzo S et al. Eur J Cancer 2012;48:441-6. Eur Radiol Exp. PubMed Google Scholar. Koshkin VS, Grivas P. Emerging role of immunotherapy in advanced urothelial carcinoma. 2018;68(1):7–30. eCollection 2019. 2018;15(2):92–111. J Urol. Author information: (1)Department of Radiology, IEO, European Institute of Oncology, IRCCS, Milan, IT, Italy. https://doi.org/10.1007/s00261-016-0897-2. https://doi.org/10.1016/j.ebiom.2018.07.029. A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors. Smith SC, Baras AS, Dancik G, Ru YB, Ding KF, Moskaluk CA, et al. J Clin Oncol. A Radiomics nomogram for the preoperative prediction of lymph node metastasis in bladder cancer. Eur Urol Focus. Nevertheless, Quantitative imaging in cancer evolution and ecology. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges Theranostics. This study was funded by the Fundamental Research Funds for the Central Universities (Grant No. Eur Urol. 2018;19(9):1180–91. Organisation for Economic Cooperation and Development Web site, From handcrafted to deep-learning-based cancer radiomics: challenges and opportunities, Idiopathic pulmonary fibrosis: data-driven textural analysis of extent of fibrosis at baseline and 15-month follow-up, Methods and challenges in quantitative imaging biomarker development, ImageNet classification with deep convolutional neural networks, Identification of invasive and radionuclide imaging markers of coronary plaque vulnerability using radiomic analysis of coronary computed tomography angiography, Radiomic features are superior to conventional quantitative computed tomographic metrics to identify coronary plaques with napkin-ring sign, A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography. For example, … Typical radiomics workflow. Faiq A. Shaikh, MD, University of Pittsburgh Medical Center; Omer Awan, MD; Christopher Deible, MD, PhD; Brian Kolowitz, MBA, DSc; Kenneth Hendrata, MBA . The prediction of response to treatment and of prognosis is essential in clinical practice in the era of precision medicine [2]. Xu X, Zhang X, Tian Q, Wang H, Cui LB, Li S, et al. Introduction Breast cancer is the most commonly diagnosed cancer and the second leading cause of death for cancer among women worldwide [1]. Adv Radiat Oncol. Temporal changes of texture features extracted from pulmonary nodules on dynamic contrast-enhanced chest computed tomography: how influential is the scan delay? 2020;37(4):1-18. 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. https://doi.org/10.1200/JCO.2011.36.1329. 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. The automatic extraction and quantification of imaging features may help in diagnosis, prognosis of, or treatment decision in cardiovascular, pulmonary, and metabolic diseases. Magn Reson Imaging 30 : 1234-1248, 2012 9 Traverso A, et al : Repeatability and Reproducibility of Radiomic Features : A Systematic Review. 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. One of the novel techniques which emerged in the imaging community is radiomics, which refers to the high-throughput extraction of quantitative image features from medical images. 2019. https://doi.org/10.1002/jmri.26749. What uncertainties do we need in Bayesian deep learning for computer vision? 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. 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. Cancer statistics in China, 2015. Siegel RL, Miller KD, Jemal A. Radiomics: extracting more information from medical images using advanced feature analysis. https://doi.org/10.1016/j.eururo.2017.03.047. 2011;29(22):2951–2. Spiess PE, Agarwal N, Bangs R, Boorjian SA, Buyyounouski MK, Clark PE, et al. Tax calculation will be finalised during checkout. Current applications and challenges of radiomics in urothelial cancer. 2018;3(3):331–8. Radiomics and liquid biopsy in oncology: the holons of systems medicine. Radiomics in Chest CT: Where Are We Going? 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. Int J Urol. https://doi.org/10.1093/jjco/hyx130. The process and challenges in radiomics. Radiomics-guided therapy for bladder cancer: using an optimal biomarker approach to determine extent of bladder cancer invasion from t2-weighted magnetic resonance images. 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.. 2017;15(10):1240–67. Its potential has been revealed in helping clinical experts to uncover cancer characteristics that fail to be appreciated by naked eyes. Pathological grade and stage in upper tract urothelial cell carcinoma along with advances in era... ( 1 ):7012–22, Deist TM, Peerlings J, Li B et... Wm, Hakim SW, Flood TA, et al validation of MRI-based! ( 4 ) an overview of statistical analysis and machine learning concepts imaging is!, Huang M, Mitra AP, Williams AJ, Lam G, Ye W, Zheng,... Sciences ( Grant Nos Katsila ML, Patrinos GP, Aristotelis B, Z... 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