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For: Cattell R, Chen S, Huang C. Robustness of radiomic features in magnetic resonance imaging: review and a phantom study. Vis Comput Ind Biomed Art 2019;2:19. [PMID: 32240418 DOI: 10.1186/s42492-019-0025-6] [Cited by in Crossref: 39] [Cited by in F6Publishing: 44] [Article Influence: 9.8] [Reference Citation Analysis]
Number Citing Articles
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6 Sudjai N, Siriwanarangsun P, Lektrakul N, Saiviroonporn P, Maungsomboon S, Phimolsarnti R, Asavamongkolkul A, Chandhanayingyong C. Robustness of Radiomic Features: Two-Dimensional versus Three-Dimensional MRI-Based Feature Reproducibility in Lipomatous Soft-Tissue Tumors. Diagnostics (Basel) 2023;13. [PMID: 36673068 DOI: 10.3390/diagnostics13020258] [Reference Citation Analysis]
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8 DeVries DA, Lagerwaard F, Zindler J, Yeung TPC, Rodrigues G, Hajdok G, Ward AD. Performance sensitivity analysis of brain metastasis stereotactic radiosurgery outcome prediction using MRI radiomics. Sci Rep 2022;12:20975. [PMID: 36471160 DOI: 10.1038/s41598-022-25389-7] [Reference Citation Analysis]
9 Tharmaseelan H, Rotkopf LT, Ayx I, Hertel A, Nörenberg D, Schoenberg SO, Froelich MF. Evaluation of radiomics feature stability in abdominal monoenergetic photon counting CT reconstructions. Sci Rep 2022;12:19594. [PMID: 36379992 DOI: 10.1038/s41598-022-22877-8] [Reference Citation Analysis]
10 Castro MA, Reza S, Chu WT, Bradley D, Lee JH, Crozier I, Sayre PJ, Lee BY, Mani V, Friedrich TC, O'Connor DH, Finch CL, Worwa G, Feuerstein IM, Kuhn JH, Solomon J. Toward the determination of sensitive and reliable whole-lung computed tomography features for robust standard radiomics and delta-radiomics analysis in a nonhuman primate model of coronavirus disease 2019. J Med Imaging (Bellingham) 2022;9:066003. [PMID: 36506838 DOI: 10.1117/1.JMI.9.6.066003] [Reference Citation Analysis]
11 Budai BK, Stollmayer R, Rónaszéki AD, Körmendy B, Zsombor Z, Palotás L, Fejér B, Szendrõi A, Székely E, Maurovich-Horvat P, Kaposi PN. Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols. Front Med (Lausanne) 2022;9:974485. [PMID: 36314024 DOI: 10.3389/fmed.2022.974485] [Reference Citation Analysis]
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13 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]
14 Abunahel BM, Pontre B, Petrov MS. Effect of Gray Value Discretization and Image Filtration on Texture Features of the Pancreas Derived from Magnetic Resonance Imaging at 3T. J Imaging 2022;8:220. [DOI: 10.3390/jimaging8080220] [Reference Citation Analysis]
15 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]
16 Thrussell I, Winfield JM, Orton MR, Miah AB, Zaidi SH, Arthur A, Thway K, Strauss DC, Collins DJ, Koh D, Oelfke U, Huang PH, O’connor JPB, Messiou C, Blackledge MD. Radiomic Features From Diffusion-Weighted MRI of Retroperitoneal Soft-Tissue Sarcomas Are Repeatable and Exhibit Change After Radiotherapy. Front Oncol 2022;12:899180. [DOI: 10.3389/fonc.2022.899180] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
17 Marfisi D, Tessa C, Marzi C, Del Meglio J, Linsalata S, Borgheresi R, Lilli A, Lazzarini R, Salvatori L, Vignali C, Barucci A, Mascalchi M, Casolo G, Diciotti S, Traino AC, Giannelli M. Image resampling and discretization effect on the estimate of myocardial radiomic features from T1 and T2 mapping in hypertrophic cardiomyopathy. Sci Rep 2022;12:10186. [PMID: 35715531 DOI: 10.1038/s41598-022-13937-0] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Zhong J, Zhang C, Hu Y, Zhang J, Liu Y, Si L, Xing Y, Ding D, Geng J, Jiao Q, Zhang H, Yang G, Yao W. Automated prediction of the neoadjuvant chemotherapy response in osteosarcoma with deep learning and an MRI-based radiomics nomogram. Eur Radiol 2022. [PMID: 35364712 DOI: 10.1007/s00330-022-08735-1] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
19 Abunahel BM, Pontre B, Ko J, Petrov MS. Towards developing a robust radiomics signature in diffuse diseases of the pancreas: Accuracy and stability of features derived from T1-weighted magnetic resonance imaging. Journal of Medical Imaging and Radiation Sciences 2022. [DOI: 10.1016/j.jmir.2022.04.002] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
20 Cattell R, Ying J, Lei L, Ding J, Chen S, Serrano Sosa M, Huang C. Preoperative prediction of lymph node metastasis using deep learning-based features. Vis Comput Ind Biomed Art 2022;5:8. [PMID: 35254557 DOI: 10.1186/s42492-022-00104-5] [Reference Citation Analysis]
21 Smolyar I, Smolyar D. Assessing Robustness of Morphological Characteristics of Arbitrary Grayscale Images. Applied Sciences 2022;12:2037. [DOI: 10.3390/app12042037] [Reference Citation Analysis]
22 Zhao Y, Zhao T, Chen S, Zhang X, Serrano Sosa M, Liu J, Mo X, Chen X, Huang M, Li S, Zhang X, Huang C. Fully automated radiomic screening pipeline for osteoporosis and abnormal bone density with a deep learning-based segmentation using a short lumbar mDixon sequence. Quant Imaging Med Surg 2022;12:1198-213. [PMID: 35111616 DOI: 10.21037/qims-21-587] [Reference Citation Analysis]
23 Qin C, Hu W, Wang X, Ma X. Application of Artificial Intelligence in Diagnosis of Craniopharyngioma. Front Neurol 2021;12:752119. [PMID: 35069406 DOI: 10.3389/fneur.2021.752119] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
24 Belenky V, Chitalia R, Kontos D. MRI radiomics and radiogenomics for breast cancer. Advances in Magnetic Resonance Technology and Applications 2022. [DOI: 10.1016/b978-0-12-822729-9.00029-1] [Reference Citation Analysis]
25 Prabhu V, Gillingham N, Babb JS, Mali RD, Rusinek H, Bruno MT, Chandarana H. Repeatability, robustness, and reproducibility of texture features on 3 Tesla liver MRI. Clinical Imaging 2022. [DOI: 10.1016/j.clinimag.2022.01.002] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
26 Kazerouni AS, Dula AN, Jarrett AM, Lorenzo G, Weis JA, Bankson JA, Chekmenev EY, Pineda F, Karczmar GS, Yankeelov TE. Emerging techniques in breast MRI. Advances in Magnetic Resonance Technology and Applications 2022. [DOI: 10.1016/b978-0-12-822729-9.00022-9] [Reference Citation Analysis]
27 Stefano A, Leal A, Richiusa S, Trang P, Comelli A, Benfante V, Cosentino S, Sabini MG, Tuttolomondo A, Altieri R, Certo F, Barbagallo GMV, Ippolito M, Russo G. Robustness of PET Radiomics Features: Impact of Co-Registration with MRI. Applied Sciences 2021;11:10170. [DOI: 10.3390/app112110170] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 6.5] [Reference Citation Analysis]
28 Ak M, Toll SA, Hein KZ, Colen RR, Khatua S. Evolving Role and Translation of Radiomics and Radiogenomics in Adult and Pediatric Neuro-Oncology. AJNR Am J Neuroradiol 2021. [PMID: 34649914 DOI: 10.3174/ajnr.A7297] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 1.5] [Reference Citation Analysis]
29 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]
30 Eck B, Chirra PV, Muchhala A, Hall S, Bera K, Tiwari P, Madabhushi A, Seiberlich N, Viswanath SE. Prospective Evaluation of Repeatability and Robustness of Radiomic Descriptors in Healthy Brain Tissue Regions In Vivo Across Systematic Variations in T2-Weighted Magnetic Resonance Imaging Acquisition Parameters. J Magn Reson Imaging 2021;54:1009-21. [PMID: 33860966 DOI: 10.1002/jmri.27635] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
31 Keenan KE, Delfino JG, Jordanova KV, Poorman ME, Chirra P, Chaudhari AS, Baessler B, Winfield J, Viswanath SE, deSouza NM. Challenges in ensuring the generalizability of image quantitation methods for MRI. Med Phys 2021. [PMID: 34455593 DOI: 10.1002/mp.15195] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
32 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]
33 Lee SE, Jung JY, Nam Y, Lee SY, Park H, Shin SH, Chung YG, Jung CK. Radiomics of diffusion-weighted MRI compared to conventional measurement of apparent diffusion-coefficient for differentiation between benign and malignant soft tissue tumors. Sci Rep 2021;11:15276. [PMID: 34315971 DOI: 10.1038/s41598-021-94826-w] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
34 Sushentsev N, Rundo L, Blyuss O, Gnanapragasam VJ, Sala E, Barrett T. MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance. Sci Rep 2021;11:12917. [PMID: 34155265 DOI: 10.1038/s41598-021-92341-6] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
35 Kozikowski M, Suarez-Ibarrola R, Osiecki R, Bilski K, Gratzke C, Shariat SF, Miernik A, Dobruch J. Role of Radiomics in the Prediction of Muscle-invasive Bladder Cancer: A Systematic Review and Meta-analysis. Eur Urol Focus 2021:S2405-4569(21)00157-7. [PMID: 34099417 DOI: 10.1016/j.euf.2021.05.005] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
36 Friconnet G. Exploring the correlation between semantic descriptors and texture analysis features in brain MRI. Chin J Acad Radiol 2021;4:105-115. [DOI: 10.1007/s42058-021-00064-4] [Reference Citation Analysis]
37 Cheung HMC, Rubin D. Challenges and opportunities for artificial intelligence in oncological imaging. Clin Radiol 2021;76:728-36. [PMID: 33902889 DOI: 10.1016/j.crad.2021.03.009] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
38 Escudero Sanchez L, Rundo L, Gill AB, Hoare M, Mendes Serrao E, Sala E. Robustness of radiomic features in CT images with different slice thickness, comparing liver tumour and muscle. Sci Rep 2021;11:8262. [PMID: 33859265 DOI: 10.1038/s41598-021-87598-w] [Cited by in Crossref: 12] [Cited by in F6Publishing: 14] [Article Influence: 6.0] [Reference Citation Analysis]
39 Vils A, Bogowicz M, Tanadini-Lang S, Vuong D, Saltybaeva N, Kraft J, Wirsching HG, Gramatzki D, Wick W, Rushing E, Reifenberger G, Guckenberger M, Weller M, Andratschke N. Radiomic Analysis to Predict Outcome in Recurrent Glioblastoma Based on Multi-Center MR Imaging From the Prospective DIRECTOR Trial. Front Oncol 2021;11:636672. [PMID: 33937035 DOI: 10.3389/fonc.2021.636672] [Cited by in Crossref: 5] [Cited by in F6Publishing: 7] [Article Influence: 2.5] [Reference Citation Analysis]
40 Jian A, Jang K, Manuguerra M, Liu S, Magnussen J, Di Ieva A. Machine Learning for the Prediction of Molecular Markers in Glioma on Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis. Neurosurgery 2021;89:31-44. [PMID: 33826716 DOI: 10.1093/neuros/nyab103] [Cited by in Crossref: 16] [Cited by in F6Publishing: 18] [Article Influence: 8.0] [Reference Citation Analysis]
41 Jang J, El‐rewaidy H, Ngo LH, Mancio J, Csecs I, Rodriguez J, Pierce P, Goddu B, Neisius U, Manning W, Nezafat R. Sensitivity of Myocardial Radiomic Features to Imaging Parameters in Cardiac MR Imaging. J Magn Reson Imaging 2021;54:787-94. [DOI: 10.1002/jmri.27581] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
42 Le EPV, Rundo L, Tarkin JM, Evans NR, Chowdhury MM, Coughlin PA, Pavey H, Wall C, Zaccagna F, Gallagher FA, Huang Y, Sriranjan R, Le A, Weir-McCall JR, Roberts M, Gilbert FJ, Warburton EA, Schönlieb CB, Sala E, Rudd JHF. Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events. Sci Rep 2021;11:3499. [PMID: 33568735 DOI: 10.1038/s41598-021-82760-w] [Cited by in Crossref: 13] [Cited by in F6Publishing: 14] [Article Influence: 6.5] [Reference Citation Analysis]
43 Wong OL, Yuan J, Zhou Y, Yu SK, Cheung KY. Longitudinal acquisition repeatability of MRI radiomics features: An ACR MRI phantom study on two MRI scanners using a 3D T1W TSE sequence. Med Phys 2021;48:1239-49. [PMID: 33370474 DOI: 10.1002/mp.14686] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
44 Brancato V, Aiello M, Basso L, Monti S, Palumbo L, Di Costanzo G, Salvatore M, Ragozzino A, Cavaliere C. Evaluation of a multiparametric MRI radiomic-based approach for stratification of equivocal PI-RADS 3 and upgraded PI-RADS 4 prostatic lesions. Sci Rep 2021;11:643. [PMID: 33436929 DOI: 10.1038/s41598-020-80749-5] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 4.5] [Reference Citation Analysis]
45 Roy S, Whitehead TD, Quirk JD, Salter A, Ademuyiwa FO, Li S, An H, Shoghi KI. Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging. EBioMedicine 2020;59:102963. [PMID: 32891051 DOI: 10.1016/j.ebiom.2020.102963] [Cited by in Crossref: 27] [Cited by in F6Publishing: 22] [Article Influence: 9.0] [Reference Citation Analysis]
46 van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-"how-to" guide and critical reflection.Insights Imaging. 2020;11:91. [PMID: 32785796 DOI: 10.1186/s13244-020-00887-2] [Cited by in Crossref: 212] [Cited by in F6Publishing: 236] [Article Influence: 70.7] [Reference Citation Analysis]
47 Schieda N, Lim CS, Zabihollahy F, Abreu-Gomez J, Krishna S, Woo S, Melkus G, Ukwatta E, Turkbey B. Quantitative Prostate MRI. J Magn Reson Imaging 2021;53:1632-45. [PMID: 32410356 DOI: 10.1002/jmri.27191] [Cited by in Crossref: 21] [Cited by in F6Publishing: 21] [Article Influence: 7.0] [Reference Citation Analysis]
48 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]
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