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For: Rai R, Holloway LC, Brink C, Field M, Christiansen RL, Sun Y, Barton MB, Liney GP. Multicenter evaluation of MRI-based radiomic features: A phantom study. Med Phys 2020;47:3054-63. [PMID: 32277703 DOI: 10.1002/mp.14173] [Cited by in Crossref: 24] [Cited by in F6Publishing: 29] [Article Influence: 8.0] [Reference Citation Analysis]
Number Citing Articles
1 Wennmann M, Bauer F, Klein A, Chmelik J, Grözinger M, Rotkopf LT, Neher P, Gnirs R, Kurz FT, Nonnenmacher T, Sauer S, Weinhold N, Goldschmidt H, Kleesiek J, Bonekamp D, Weber TF, Delorme S, Maier-Hein K, Schlemmer HP, Götz M. In Vivo Repeatability and Multiscanner Reproducibility of MRI Radiomics Features in Patients With Monoclonal Plasma Cell Disorders: A Prospective Bi-institutional Study. Invest Radiol 2023;58:253-64. [PMID: 36165988 DOI: 10.1097/RLI.0000000000000927] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
2 Bos P, Martens RM, de Graaf P, Jasperse B, van Griethuysen JJM, Boellaard R, Leemans CR, Beets-Tan RGH, van de Wiel MA, van den Brekel MWM, Castelijns JA. External validation of an MR-based radiomic model predictive of locoregional control in oropharyngeal cancer. Eur Radiol 2023;33:2850-60. [PMID: 36460924 DOI: 10.1007/s00330-022-09255-8] [Reference Citation Analysis]
3 Wagner MW, Namdar K, Napoleone M, Hainc N, Amirabadi A, Fonseca A, Laughlin S, Shroff MM, Bouffet E, Hawkins C, Khalvati F, Bartels U, Ertl-Wagner BB. Radiomic Features Based on MRI Predict Progression-Free Survival in Pediatric Diffuse Midline Glioma/Diffuse Intrinsic Pontine Glioma. Can Assoc Radiol J 2023;74:119-26. [PMID: 35768942 DOI: 10.1177/08465371221109921] [Reference Citation Analysis]
4 Hadjiiski L, Cha K, Chan HP, Drukker K, Morra L, Näppi JJ, Sahiner B, Yoshida H, Chen Q, Deserno TM, Greenspan H, Huisman H, Huo Z, Mazurchuk R, Petrick N, Regge D, Samala R, Summers RM, Suzuki K, Tourassi G, Vergara D, Armato SG 3rd. AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging. Med Phys 2023;50:e1-e24. [PMID: 36565447 DOI: 10.1002/mp.16188] [Reference Citation Analysis]
5 Saba L, Loewe C, Weikert T, Williams MC, Galea N, Budde RPJ, Vliegenthart R, Velthuis BK, Francone M, Bremerich J, Natale L, Nikolaou K, Dacher JN, Peebles C, Caobelli F, Redheuil A, Dewey M, Kreitner KF, Salgado R. State-of-the-art CT and MR imaging and assessment of atherosclerotic carotid artery disease: standardization of scanning protocols and measurements-a consensus document by the European Society of Cardiovascular Radiology (ESCR). Eur Radiol 2023;33:1063-87. [PMID: 36194267 DOI: 10.1007/s00330-022-09024-7] [Reference Citation Analysis]
6 Nie K, Xiao Y. Radiomics in clinical trials: perspectives on standardization. Phys Med Biol 2022;68. [PMID: 36384049 DOI: 10.1088/1361-6560/aca388] [Reference Citation Analysis]
7 Veres G, Kiss J, Vas NF, Kallos-balogh P, Máthé NB, Lassen ML, Berényi E, Balkay L. Phantom Study on the Robustness of MR Radiomics Features: Comparing the Applicability of 3D Printed and Biological Phantoms. Diagnostics 2022;12:2196. [DOI: 10.3390/diagnostics12092196] [Reference Citation Analysis]
8 Rai R, Barton MB, Chlap P, Liney G, Brink C, Vinod S, Heinke M, Trada Y, Holloway LC. Repeatability and reproducibility of magnetic resonance imaging-based radiomic features in rectal cancer. J Med Imag 2022;9. [DOI: 10.1117/1.jmi.9.4.044005] [Reference Citation Analysis]
9 Cui Y, Yin F. Impact of image quality on radiomics applications. Phys Med Biol 2022;67:15TR03. [DOI: 10.1088/1361-6560/ac7fd7] [Reference Citation Analysis]
10 Mitchell D, Buszek S, Tran B, Farhat M, Goldman J, Erickson L, Curl B, Suki D, Ferguson SD, Liu H, Kundu S, Chung C. Managing the effect of magnetic resonance imaging pulse sequence on radiomic feature reproducibility in the study of brain metastases. F1000Res 2022;11:892. [DOI: 10.12688/f1000research.122871.1] [Reference Citation Analysis]
11 Valladares A, Oberoi G, Berg A, Beyer T, Unger E, Rausch I. Additively manufactured, solid object structures for adjustable image contrast in Magnetic Resonance Imaging. Z Med Phys 2022;32:466-76. [PMID: 35597743 DOI: 10.1016/j.zemedi.2022.03.003] [Reference Citation Analysis]
12 Scalco E, Rizzo G, Mastropietro A. The stability of oncologic MRI radiomic features and the potential role of deep learning: a review. Phys Med Biol 2022;67:09TR03. [DOI: 10.1088/1361-6560/ac60b9] [Reference Citation Analysis]
13 Du Y, Zha HL, Wang H, Liu XP, Pan JZ, Du LW, Cai MJ, Zong M, Li CY. Ultrasound-based radiomics nomogram for differentiation of triple-negative breast cancer from fibroadenoma. Br J Radiol 2022;95:20210598. [PMID: 35138938 DOI: 10.1259/bjr.20210598] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
14 Xue C, Yuan J, Zhou Y, Wong OL, Cheung KY, Yu SK. Acquisition repeatability of MRI radiomics features in the head and neck: a dual-3D-sequence multi-scan study. Vis Comput Ind Biomed Art 2022;5:10. [PMID: 35359245 DOI: 10.1186/s42492-022-00106-3] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
15 Chen ZW, Xiao HM, Ye X, Liu K, Rios RS, Zheng KI, Jin Y, Targher G, Byrne CD, Shi J, Yan Z, Chi XL, Zheng MH. A novel radiomics signature based on T2-weighted imaging accurately predicts hepatic inflammation in individuals with biopsy-proven nonalcoholic fatty liver disease: a derivation and independent validation study. Hepatobiliary Surg Nutr 2022;11:212-26. [PMID: 35464279 DOI: 10.21037/hbsn-21-23] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
16 Ren J, Li Y, Yang J, Zhao J, Xiang Y, Xia C, Cao Y, Chen B, Guan H, Qi Y, Tang W, Chen K, He Y, Jin Z, Xue H. MRI-based radiomics analysis improves preoperative diagnostic performance for the depth of stromal invasion in patients with early stage cervical cancer. Insights Imaging 2022;13. [DOI: 10.1186/s13244-022-01156-0] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
17 Ericsson-Szecsenyi R, Zhang G, Redler G, Feygelman V, Rosenberg S, Latifi K, Ceberg C, Moros EG. Robustness Assessment of Images From a 0.35T Scanner of an Integrated MRI-Linac: Characterization of Radiomics Features in Phantom and Patient Data. Technol Cancer Res Treat 2022;21:15330338221099113. [PMID: 35521966 DOI: 10.1177/15330338221099113] [Reference Citation Analysis]
18 Jensen LJ, Kim D, Elgeti T, Steffen IG, Hamm B, Nagel SN. Stability of Liver Radiomics across Different 3D ROI Sizes-An MRI In Vivo Study. Tomography 2021;7:866-76. [PMID: 34941645 DOI: 10.3390/tomography7040073] [Reference Citation Analysis]
19 Xue C, Yuan J, Lo GG, Chang ATY, Poon DMC, Wong OL, Zhou Y, Chu WCW. Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review. Quant Imaging Med Surg 2021;11:4431-60. [PMID: 34603997 DOI: 10.21037/qims-21-86] [Cited by in Crossref: 12] [Cited by in F6Publishing: 14] [Article Influence: 6.0] [Reference Citation Analysis]
20 Mali SA, Ibrahim A, Woodruff HC, Andrearczyk V, Müller H, Primakov S, Salahuddin Z, Chatterjee A, Lambin P. Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods. J Pers Med 2021;11:842. [PMID: 34575619 DOI: 10.3390/jpm11090842] [Cited by in Crossref: 24] [Cited by in F6Publishing: 29] [Article Influence: 12.0] [Reference Citation Analysis]
21 Spohn SKB, Bettermann AS, Bamberg F, Benndorf M, Mix M, Nicolay NH, Fechter T, Hölscher T, Grosu R, Chiti A, Grosu AL, Zamboglou C. Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies. Theranostics 2021;11:8027-42. [PMID: 34335978 DOI: 10.7150/thno.61207] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 5.0] [Reference Citation Analysis]
22 Field M, Hardcastle N, Jameson M, Aherne N, Holloway L. Machine learning applications in radiation oncology. Phys Imaging Radiat Oncol 2021;19:13-24. [PMID: 34307915 DOI: 10.1016/j.phro.2021.05.007] [Cited by in Crossref: 16] [Cited by in F6Publishing: 21] [Article Influence: 8.0] [Reference Citation Analysis]
23 Jensen LJ, Kim D, Elgeti T, Steffen IG, Hamm B, Nagel SN. Stability of Radiomic Features across Different Region of Interest Sizes-A CT and MR Phantom Study. Tomography 2021;7:238-52. [PMID: 34201012 DOI: 10.3390/tomography7020022] [Cited by in Crossref: 9] [Cited by in F6Publishing: 12] [Article Influence: 4.5] [Reference Citation Analysis]
24 Granzier RWY, Ibrahim A, Primakov SP, Samiei S, van Nijnatten TJA, de Boer M, Heuts EM, Hulsmans FJ, Chatterjee A, Lambin P, Lobbes MBI, Woodruff HC, Smidt ML. MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter Study. Cancers (Basel) 2021;13:2447. [PMID: 34070016 DOI: 10.3390/cancers13102447] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 2.5] [Reference Citation Analysis]
25 Yuan J, Xue C, Lo G, Wong OL, Zhou Y, Yu SK, Cheung KY. Quantitative assessment of acquisition imaging parameters on MRI radiomics features: a prospective anthropomorphic phantom study using a 3D-T2W-TSE sequence for MR-guided-radiotherapy. Quant Imaging Med Surg 2021;11:1870-87. [PMID: 33936971 DOI: 10.21037/qims-20-865] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
26 Zheng R, Shi C, Wang C, Shi N, Qiu T, Chen W, Shi Y, Wang H. Imaging-Based Staging of Hepatic Fibrosis in Patients with Hepatitis B: A Dynamic Radiomics Model Based on Gd-EOB-DTPA-Enhanced MRI. Biomolecules 2021;11:307. [PMID: 33670596 DOI: 10.3390/biom11020307] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
27 Verduin M, Primakov S, Compter I, Woodruff HC, van Kuijk SMJ, Ramaekers BLT, te Dorsthorst M, Revenich EGM, ter Laan M, Pegge SAH, Meijer FJA, Beckervordersandforth J, Speel EJ, Kusters B, de Leng WWJ, Anten MM, Broen MPG, Ackermans L, Schijns OEMG, Teernstra O, Hovinga K, Vooijs MA, Tjan-Heijnen VCG, Eekers DBP, Postma AA, Lambin P, Hoeben A. Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma. Cancers (Basel) 2021;13:722. [PMID: 33578746 DOI: 10.3390/cancers13040722] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 6.0] [Reference Citation Analysis]
28 van Houdt PJ, Yang Y, van der Heide UA. Quantitative Magnetic Resonance Imaging for Biological Image-Guided Adaptive Radiotherapy. Front Oncol 2020;10:615643. [PMID: 33585242 DOI: 10.3389/fonc.2020.615643] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 7.5] [Reference Citation Analysis]
29 Alves N, Dias J, Ventura T, Mateus J, Capela M, Khouri L, do Carmo Lopes M. Predicting the Need for Adaptive Radiotherapy in Head and Neck Patients from CT-Based Radiomics and Pre-treatment Data. Computational Science and Its Applications – ICCSA 2021 2021. [DOI: 10.1007/978-3-030-86976-2_29] [Reference Citation Analysis]