The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

From OncoRay–National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden–Rossendorf, Fetscherstr 74, PF 41, 01307 Dresden, Germany (A.Z., S. Leger, E.G.C.T., C.R., S. Löck); National Center for Tumor Diseases (NCT), Partner Site Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany (A.Z.); Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden and Helmholtz Association/Helmholtz-Zentrum Dresden–Rossendorf (HZDR), Dresden, Germany (A.Z., S. Leger, E.G.C.T.); German Cancer Consortium (DKTK), Partner Site Dresden, and German Cancer Research Center (DKFZ), Heidelberg, Germany (A.Z., S. Leger, E.G.C.T., C.R., S. Löck); Medical Physics Unit, McGill University, Montréal, Canada (M.V., I.E.N.); Image Response Assessment Team Core Facility, Moffitt Cancer Center, Tampa, Fla (M.A.A.); Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School, Harvard University, Boston, Mass (H.J.W.L.A.); Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland (V.A., A.D., H.M.); Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY (A.A.); Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Md (S.A.); Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, Md (S.A., A.R.); Center for Biomedical image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pa (S.B., C.D., S.M.H., S.P.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (S.B., C.D., S.M.H., S.P.); Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pa (S.B.); Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands (R.J.B., R.B., E.A.G.P.); Radiology and Nuclear Medicine, VU University Medical Centre (VUMC), Amsterdam, the Netherlands (R.B.); Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland (M.B., M.Guckenberger, S.T.L.); Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy (L.B., N.D., R.G., J.L., V.V.); Laboratoire d'Imagerie Translationnelle en Oncologie, Université Paris Saclay, Inserm, Institut Curie, Orsay, France (I.B., C.N., F.O.); Cancer Imaging Dept, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom (G.J.R.C., V.G., M.M.S.); Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Lausanne, Switzerland (A.D.); Laboratory of Medical Information Processing (LaTIM)–team ACTION (image-guided therapeutic action in oncology), INSERM, UMR 1101, IBSAM, UBO, UBL, Brest, France (M.C.D., M.H., T.U.); Department of Radiation Oncology, the Netherlands Cancer Institute (NKI), Amsterdam, the Netherlands (C.V.D.); Department of Radiology, Stanford University School of Medicine, Stanford, Calif (S.E., S.N.); Department of Radiation Oncology, Physics Division, University of Michigan, Ann Arbor, Mich (I.E.N., A.U.K.R.); Surgical Planning Laboratory, Brigham and Women’s Hospital and Harvard Medical School, Harvard University, Boston, Mass (A.Y.F.); Department of Cancer Imaging and Metabolism, Moffitt Cancer Center, Tampa, Fla (R.J.G.); Department of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany (M. Götz, F.I., K.H.M.H., J.S.); The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, the Netherlands (P.L., R.T.H.L.); Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany (F.L., J.S.F., D.T.); Department of Clinical Medicine, University of Bergen, Bergen, Norway (A.L.); Department of Radiation Oncology, University of California, San Francisco, Calif (O.M.); University of Geneva, Geneva, Switzerland (H.M.); Department of Electrical Engineering, Stanford University, Stanford, Calif (S.N.); Department of Medicine (Biomedical Informatics Research), Stanford University School of Medicine, Stanford, Calif (S.N.); Departments of Radiology and Physics, University of British Columbia, Vancouver, Canada (A.R.); Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Mich (A.U.K.R.); Department of Radiation Oncology, University of Groningen, University Medical Center Groningen (UMCG), Groningen, the Netherlands (N.M.S., R.J.H.M.S., L.V.v.D.); School of Engineering, Cardiff University, Cardiff, United Kingdom (E.S., P.W.); Department of Medical Physics, Velindre Cancer Centre, Cardiff, United Kingdom (E.S.); Department of Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany (E.G.C.T., C.R., S. Löck), Helmholtz-Zentrum Dresden–Rossendorf, Institute of Radiooncology–OncoRay, Dresden, Germany (E.G.C.T., C.R.); Department of Nuclear Medicine, CHU Milétrie, Poitiers, France (T.U.); Department of Radiology, the Netherlands Cancer Institute (NKI), Amsterdam, the Netherlands (J.v.G.); GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands (J.v.G.); Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass (J.v.G.); and Department of Radiology, Leiden University Medical Center (LUMC), Leiden, the Netherlands (F.H.P.v.V.).
Address correspondence to A.Z. Fetscherstraße 74, PO Box PF 41, 01307 Dresden, Germany (e-mail: [email protected]).

Published Online:Mar 10 2020https://doi.org/10.1148/radiol.2020191145

See related article

Abstract

The Image Biomarker Standardization Initiative validated consensus-based reference values for 169 radiomics features, thus enabling calibration and verification of radiomics software.

Background

Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use.

Purpose

To standardize a set of 174 radiomic features.

Materials and Methods

Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features.

Results

Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI).

Conclusion

A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software.

Online supplemental material is available for this article.

See also the editorial by Kuhl and Truhn in this issue.

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Article History

Received: May 22 2019
Revision requested: July 3 2019
Revision received: Dec 9 2019
Accepted: Jan 6 2020
Published online: Mar 10 2020
Published in print: May 2020

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  2024Oct19
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  26 February 2024 | Journal of Applied Clinical Medical Physics, Vol. 25, No. 3
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  21 November 2023 | European Journal of Nuclear Medicine and Molecular Imaging, Vol. 51, No. 4
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  2022Nov1 | The British Journal of Radiology, Vol. 95, No. 1139
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  24 October 2022 | Applied Sciences, Vol. 12, No. 21
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  2 November 2022 | International Journal of Molecular Sciences, Vol. 23, No. 21
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  26 October 2022 | Journal of Clinical Medicine, Vol. 11, No. 21
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  7 November 2022 | Journal of Personalized Medicine, Vol. 12, No. 11
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  18 November 2022 | Journal of Personalized Medicine, Vol. 12, No. 11
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  28 October 2022 | Frontiers in Oncology, Vol. 12
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  2022Oct26
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  25 October 2022 | Frontiers in Oncology, Vol. 12
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  13 October 2022 | Frontiers in Medicine, Vol. 9
- Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction
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  13 October 2022 | Frontiers in Neuroscience, Vol. 16
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  10 October 2022 | Frontiers in Neuroscience, Vol. 16
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  6 October 2022 | Frontiers in Oncology, Vol. 12
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  2022Oct3
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  3 October 2022 | Frontiers in Oncology, Vol. 12
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  11 August 2022 | Advanced Science, Vol. 9, No. 28
- Radiomic phenotype of epicardial adipose tissue in the prognosis of atrial fibrillation recurrence after catheter ablation in patients with lone atrial fibrillation
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  16 August 2022 | Journal of Arrhythmia, Vol. 38, No. 5
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  Femke C. R.Staal, Else A.Aalbersberg, Daphnevan der Velden, Erica A.Wilthagen, Margot E. T.Tesselaar, Regina G. H.Beets-Tan, MoniqueMaas
  26 July 2022 | European Radiology, Vol. 32, No. 10
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  28 July 2022 | European Radiology, Vol. 32, No. 10
- Prediction of recurrence-free survival and adjuvant therapy benefit in patients with gastrointestinal stromal tumors based on radiomics features
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  4 September 2022 | La radiologia medica, Vol. 127, No. 10
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  6 July 2022 | International Journal of Computer Assisted Radiology and Surgery, Vol. 17, No. 10
- How Machine Learning is Powering Neuroimaging to Improve Brain Health
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  28 March 2022 | Neuroinformatics, Vol. 20, No. 4
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  Yo-TsenLiu, Cheng-ChiaLee, Chun-FuLin, Hsiu-MeiWu, Wan-YuoGuo, Huai-CheYang, Feng-ChiChang, Kang-DuLiou, Chung-JungLin
  12 July 2022 | Molecular Neurobiology, Vol. 59, No. 10
- Artificial intelligence-based radiomics on computed tomography of lumbar spine in subjects with fragility vertebral fractures
  E.Biamonte, R.Levi, F.Carrone, W.Vena, A.Brunetti, M.Battaglia, F.Garoli, G.Savini, M.Riva, A.Ortolina, M.Tomei, G.Angelotti, M. E.Laino, V.Savevski, M.Mollura, M.Fornari, R.Barbieri, A. G.Lania, M.Grimaldi, L. S.Politi, G.Mazziotti
  25 June 2022 | Journal of Endocrinological Investigation, Vol. 45, No. 10
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  2022Oct1 | Academic Radiology, Vol. 29, No. 10
- Identifying How the Next Generation of Radiologists Will Increase the Value of Imaging and our Impact on Patient Outcomes: The Added Potential of CT Radiomics and AI Analysis
  Christopher M.Straus
  2022Oct1 | Academic Radiology, Vol. 29, No. 10
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  29 August 2022 | Thoracic Cancer, Vol. 13, No. 19
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  22 September 2022 | Cancers, Vol. 14, No. 19
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  3 October 2022 | Cancers, Vol. 14, No. 19
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  5 October 2022 | Cancers, Vol. 14, No. 19
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  22 September 2022 | Diagnostics, Vol. 12, No. 10
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  HyeinAhn, GeumSong, Si-HyongJang, MyoungSon, HyunLee, Moon-SooLee, Ji-HyeLee, Mee-HyeOh, GeumJeong, JongYun, SangLee, JeongLee
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  30 September 2022 | Frontiers in Oncology, Vol. 12
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  29 September 2022 | Frontiers in Oncology, Vol. 12
- Fusing hand-crafted and deep-learning features in a convolutional neural network model to identify prostate cancer in pathology images
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  23 September 2022 | Frontiers in Oncology, Vol. 12
- Computed tomography-based deep-learning prediction of lymph node metastasis risk in locally advanced gastric cancer
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  23 September 2022 | Frontiers in Oncology, Vol. 12
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  13 September 2022 | Frontiers in Oncology, Vol. 12
- Application of texture analysis of CT and MR images to determine the histologic grade of hepatocellular cancer and it’s differential diagnosis: a review
  M. Yu.Shantarevich, G. G.Karmazanovsky
  17 August 2022 | Research and Practical Medicine Journal, Vol. 9, No. 3
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  9 September 2022 | Frontiers in Radiology, Vol. 2
- Influence of Image Processing on Radiomic Features From Magnetic Resonance Imaging
  Barbara D.Wichtmann, Felix N.Harder, KilianWeiss, Stefan O.Schönberg, Ulrike I.Attenberger, HatemAlkadhi, DanielPinto dos Santos, BettinaBaeßler
  7 September 2022 | Investigative Radiology, Vol. 375
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  7 September 2022 | Frontiers in Oncology, Vol. 12
- Diagnostic performance of radiomics in adrenal masses: A systematic review and meta-analysis
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  2 September 2022 | Frontiers in Oncology, Vol. 12
- Forging Connections in Latin America to Advance AI in Radiology
  Felipe Campos Kitamura
  ,
  Felipe Barjud Pereira do Nascimento
  ,
  Guillermo Elizondo-Riojas
  ,
  Hernán Chaves
  ,
  Héctor Henríquez Leighton
  ,
  Emmanuel Salinas-Miranda
  ,
  Thiago Júlio
  ,
  Antônio José da Rocha
  ,
  César Higa Nomura
  ,
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  YangYang, XianlunZou, WeiZhou, GuanjieYuan, DaoyuHu, DongKuang, YaqiShen, QingguoXie, QingpengZhang, XuemeiHu, ZhenLi
  20 January 2022 | Journal of Magnetic Resonance Imaging, Vol. 56, No. 3
- A multi‐modality physical phantom for mimicking tumor heterogeneity patterns in PET/CT and PET/MRI
  AlejandraValladares, ThomasBeyer, LaszloPapp, ElisabethSalomon, IvoRausch
  25 July 2022 | Medical Physics, Vol. 49, No. 9
- Development and validation of a radiomic model for the diagnosis of dopaminergic denervation on [18F]FDOPA PET/CT
  VictorComte, HugoSchmutz, DavidChardin, FannyOrlhac, JacquesDarcourt, OlivierHumbert
  14 May 2022 | European Journal of Nuclear Medicine and Molecular Imaging, Vol. 49, No. 11
- Update on quantitative radiomics of pancreatic tumors
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  22 July 2021 | Abdominal Radiology, Vol. 47, No. 9
- Radiomics: a primer on high-throughput image phenotyping
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  25 August 2021 | Abdominal Radiology, Vol. 47, No. 9
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  JeroenBleker, Thomas C.Kwee, DennisRouw, ChristianRoest, JaapBorstlap, Igle Jande Jong, Rudi A. J. O.Dierckx, HenkjanHuisman, DeryaYakar
  14 April 2022 | European Radiology, Vol. 32, No. 9
- Automated prediction of the neoadjuvant chemotherapy response in osteosarcoma with deep learning and an MRI-based radiomics nomogram
  JingyuZhong, ChengxiuZhang, YangfanHu, JingZhang, YunLiu, LipingSi, YueXing, DefangDing, JiaGeng, QiongJiao, HuizhenZhang, GuangYang, WeiwuYao
  2 April 2022 | European Radiology, Vol. 32, No. 9
- Intratumoral and peritumoral CT-based radiomics strategy reveals distinct subtypes of non-small-cell lung cancer
  XingTang, HaolinHuang, PengDu, LijuanWang, HongYin, XiaopanXu
  17 April 2022 | Journal of Cancer Research and Clinical Oncology, Vol. 148, No. 9
- Diagnostische Radiologie
  SimonLennartz, Heinz-PeterSchlemmer, ThorstenPersigehl
  12 July 2022 | Die Onkologie, Vol. 28, No. 9
- Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression
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  19 July 2022 | Journal of Neuro-Oncology, Vol. 159, No. 3
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  RobertaFusco, VincenzaGranata, GiuliaGrazzini, SilviaPradella, AlessandraBorgheresi, AlessandraBruno, PierpaoloPalumbo, FedericoBruno, RobertaGrassi, AndreaGiovagnoni, RobertoGrassi, VittorioMiele, AntonioBarile
  28 March 2022 | Japanese Journal of Radiology, Vol. 40, No. 9
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  6 July 2022 | Radiological Physics and Technology, Vol. 15, No. 3
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  YoshiroIeko, NoriyukiKadoya, YutoSugai, ShiinaMouri, MarikoUmeda, ShoheiTanaka, TakayukiKanai, KeiIchiji, TakayaYamamoto, HisanoriAriga, KeiichiJingu
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- Introduction to the National Cancer Imaging Translational Accelerator (NCITA): a UK-wide infrastructure for multicentre clinical translation of cancer imaging biomarkers
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- AI and High-Grade Glioma for Diagnosis and Outcome Prediction: Do All Machine Learning Models Perform Equally Well?
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- A Novel Clinical-Radiomics Model Based on Sarcopenia and Radiomics for Predicting the Prognosis of Intrahepatic Cholangiocarcinoma After Radical Hepatectomy
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- Bone Metastases Are Measurable: The Role of Whole-Body MRI and Positron Emission Tomography
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- Radiomics in hepatocellular carcinoma: A state-of-the-art review
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- Generalized methodology for radiomic feature selection and modeling in predicting clinical outcomes
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- Toward Generalizability in the Deployment of Artificial Intelligence in Radiology: Role of Computation Stress Testing to Overcome Underspecification
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- Magnetic Resonance Imaging Radiomics‐Based Machine Learning Prediction of Clinically Significant Prostate Cancer in Equivocal PI‐RADS 3 Lesions
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- Technical note: On the development of an outcome‐driven frequency filter for improving radiomics‐based modeling of human papillomavirus (HPV) in patients with oropharyngeal squamous cell carcinoma
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- Reliability of MRI radiomics features in MR‐guided radiotherapy for prostate cancer: Repeatability, reproducibility, and within‐subject agreement
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- Structural and functional radiomics for lung cancer
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- Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance
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- Performance of automatic machine learning versus radiologists in the evaluation of endometrium on computed tomography
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- Preoperative prediction of postsurgical outcomes in mass-forming intrahepatic cholangiocarcinoma based on clinical, radiologic, and radiomics features
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- Radiomics on routine T1-weighted MRI can delineate Parkinson’s disease from multiple system atrophy and progressive supranuclear palsy
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- Quantitative CT texture analysis in predicting PD-L1 expression in locally advanced or metastatic NSCLC patients
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- Role of correlated noise in textural features extraction
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- Artificial intelligence-based radiomics models in endometrial cancer: A systematic review
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- A machine learning–based radiomic model for predicting urinary infection stone
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- Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images
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- MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy
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- Machine Assist for Pediatric Posterior Fossa Tumor Diagnosis: A Multinational Study
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- Prediction of Genetic Alterations in Oncogenic Signaling Pathways in Squamous Cell Carcinoma of the Head and Neck: Radiogenomic Analysis Based on Computed Tomography Images
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- Radiomics-based Prediction of Re-hemorrhage in Cerebral Cavernous Malformation after Gamma Knife Radiosurgery
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- Comparison of radiomics approaches to predict resistance to 1st line chemotherapy in liver metastatic colorectal cancer
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- Virtual biopsy in prostate cancer: can machine learning distinguish low and high aggressive tumors on MRI?
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- A picture is worth a thousand words: a history of diagnostic imaging for lymphoma
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- Assessment of Renal Cell Carcinoma by Texture Analysis in Clinical Practice: A Six-Site, Six-Platform Analysis of Reliability
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- Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies
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- Comparison of Conventional and Radiomic Features between 18F-FBPA PET/CT and PET/MR
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- Effects of Carbidopa Premedication on 18F-FDOPA PET Imaging of Glioma: A Multiparametric Analysis
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- Development and Validation of an Efficient MRI Radiomics Signature for Improving the Predictive Performance of 1p/19q Co-Deletion in Lower-Grade Gliomas
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- Radiogenomics: Hunting Down Liver Metastasis in Colorectal Cancer Patients
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- Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models
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- Development of a Radiomic-Based Model Predicting Lymph Node Involvement in Prostate Cancer Patients
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- Quantitative Imaging Biomarkers of the Whole Liver Tumor Burden Improve Survival Prediction in Metastatic Pancreatic Cancer
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- Brain MRI Radiomics Analysis of School-Aged Children with Tetralogy of Fallot
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- The Role of the Immune Response in Brain Metastases: Novel Imaging Biomarkers for Immunotherapy
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- A Clinical Semantic and Radiomics Nomogram for Predicting Brain Invasion in WHO Grade II Meningioma Based on Tumor and Tumor-to-Brain Interface Features
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- Multi-level multi-modality (PET and CT) fusion radiomics: prognostic modeling for non-small cell lung carcinoma
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- MRI-Based Machine Learning in Differentiation Between Benign and Malignant Breast Lesions
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- Accurate Tumor Delineation vs. Rough Volume of Interest Analysis for 18F-FDG PET/CT Radiomics-Based Prognostic Modeling inNon-Small Cell Lung Cancer
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- CT-Based Peritumoral and Intratumoral Radiomics as Pretreatment Predictors of Atypical Responses to Immune Checkpoint Inhibitor Across Tumor Types: A Preliminary Multicenter Study
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- Survival Prognostic Modeling Using PET/CT Image Radiomics: The Quest for Optimal Approaches
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- On the Removal of Inter/Intra Observer Variability of PET Radiomics Features
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- Habitat Imaging-Based 18F-FDG PET/CT Radiomics for the Preoperative Discrimination of Non-small Cell Lung Cancer and Benign Inflammatory Diseases
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- Radiomics in Oncology: A Practical Guide
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- [18F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation
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- Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer
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- A deep look into radiomics
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- Toward High-Throughput Artificial Intelligence-Based Segmentation in Oncological PET Imaging
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- Robustness of radiomic features of benign breast lesions and hormone receptor positive/HER2-negative cancers across DCE-MR magnet strengths
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- Radiomics integration into a picture archiving and communication system
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- Value of PET imaging for radiation therapy
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- Improved 18-FDG PET/CT diagnosis of multiple myeloma diffuse disease by radiomics analysis
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- Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs
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- A Fully Automatic Artificial Intelligence System Able to Detect and Characterize Prostate Cancer Using Multiparametric MRI: Multicenter and Multi-Scanner Validation
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- Artificial Intelligence in Thyroid Field—A Comprehensive Review
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- Prediction of Early Distant Recurrence in Upfront Resectable Pancreatic Adenocarcinoma: A Multidisciplinary, Machine Learning-Based Approach
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- Predicting Short-Term Survival after Gross Total or Near Total Resection in Glioblastomas by Machine Learning-Based Radiomic Analysis of Preoperative MRI
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- Predicting Tumor Budding Status in Cervical Cancer Using MRI Radiomics: Linking Imaging Biomarkers to Histologic Characteristics
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- Added Value of Tomoelastography for Characterization of Pancreatic Neuroendocrine Tumor Aggressiveness Based on Stiffness
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- Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature
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- Impact of radiogenomics in esophageal cancer on clinical outcomes: A pilot study
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- Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories
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Accompanying This Article
- The Long Route to Standardized Radiomics: Unraveling the Knot from the End
  Mar 10 2020Radiology

Vol. 295, No. 2

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Abbreviations

Abbreviations:
IBSI	Image Biomarker Standardization Initiative
ROI	region of interest

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