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For: Buch K, Kuno H, Qureshi MM, Li B, Sakai O. Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model. J Appl Clin Med Phys 2018;19:253-64. [PMID: 30369010 DOI: 10.1002/acm2.12482] [Cited by in Crossref: 36] [Cited by in F6Publishing: 39] [Article Influence: 7.2] [Reference Citation Analysis]
Number Citing Articles
1 Provenzano D, Melnyk O, Imtiaz D, Mcsweeney B, Nemirovsky D, Wynne M, Whalen M, Rao YJ, Loew M, Haji-momenian S. Machine Learning Algorithm Accuracy Using Single- versus Multi-Institutional Image Data in the Classification of Prostate MRI Lesions. Applied Sciences 2023;13:1088. [DOI: 10.3390/app13021088] [Reference Citation Analysis]
2 Tabari A, Chan SM, Omar OMF, Iqbal SI, Gee MS, Daye D. Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers. Cancers (Basel) 2022;15. [PMID: 36612061 DOI: 10.3390/cancers15010063] [Reference Citation Analysis]
3 Korda AI, Andreou C, Rogg HV, Avram M, Ruef A, Davatzikos C, Koutsouleris N, Borgwardt S. Identification of texture MRI brain abnormalities on first-episode psychosis and clinical high-risk subjects using explainable artificial intelligence. Transl Psychiatry 2022;12:481. [PMID: 36385133 DOI: 10.1038/s41398-022-02242-z] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Wei Q, Chen Z, Tang Y, Chen W, Zhong L, Mao L, Hu S, Wu Y, Deng K, Yang W, Liu X. External validation and comparison of MR-based radiomics models for predicting pathological complete response in locally advanced rectal cancer: a two-centre, multi-vendor study. Eur Radiol 2022. [DOI: 10.1007/s00330-022-09204-5] [Reference Citation Analysis]
5 Bologna M, Tenconi C, Corino VDA, Annunziata G, Orlandi E, Calareso G, Pignoli E, Valdagni R, Mainardi LT, Rancati T. Repeatability and reproducibility of MRI‐radiomic features: A phantom experiment on a 1.5 T scanner. Medical Physics 2022. [DOI: 10.1002/mp.16054] [Reference Citation Analysis]
6 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]
7 Hu K, Deng W, Li N, Cai Q, Yuan Z, Li L, Liu Y. Impact of Parallel Acquisition Technology on the Robustness of Magnetic Resonance Imaging Radiomic Features. J Comput Assist Tomogr 2022. [PMID: 35675690 DOI: 10.1097/RCT.0000000000001344] [Reference Citation Analysis]
8 Ștefan PA, Lupean RA, Lebovici A, Csutak C, Crivii CB, Opincariu I, Caraiani C. Quantitative MRI of Pancreatic Cystic Lesions: A New Diagnostic Approach. Healthcare 2022;10:1039. [DOI: 10.3390/healthcare10061039] [Reference Citation Analysis]
9 Strzelecki M, Piórkowski A, Obuchowicz R. Effect of Matrix Size Reduction on Textural Information in Clinical Magnetic Resonance Imaging. JCM 2022;11:2526. [DOI: 10.3390/jcm11092526] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Li MD, Cheng MQ, Chen LD, Hu HT, Zhang JC, Ruan SM, Huang H, Kuang M, Lu MD, Li W, Wang W. Reproducibility of radiomics features from ultrasound images: influence of image acquisition and processing. Eur Radiol 2022. [PMID: 35314881 DOI: 10.1007/s00330-022-08662-1] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
11 Mărginean L, Ștefan PA, Lebovici A, Opincariu I, Csutak C, Lupean RA, Coroian PA, Suciu BA. CT in the Differentiation of Gliomas from Brain Metastases: The Radiomics Analysis of the Peritumoral Zone. Brain Sciences 2022;12:109. [DOI: 10.3390/brainsci12010109] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 9.0] [Reference Citation Analysis]
12 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]
13 Lee S, Summers RM. Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen. Radiol Clin North Am 2021;59:987-1002. [PMID: 34689882 DOI: 10.1016/j.rcl.2021.07.001] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
14 Singh G, Manjila S, Sakla N, True A, Wardeh AH, Beig N, Vaysberg A, Matthews J, Prasanna P, Spektor V. Radiomics and radiogenomics in gliomas: a contemporary update. Br J Cancer 2021;125:641-57. [PMID: 33958734 DOI: 10.1038/s41416-021-01387-w] [Cited by in Crossref: 51] [Cited by in F6Publishing: 52] [Article Influence: 25.5] [Reference Citation Analysis]
15 Li Y, Ammari S, Balleyguier C, Lassau N, Chouzenoux E. Impact of Preprocessing and Harmonization Methods on the Removal of Scanner Effects in Brain MRI Radiomic Features. Cancers (Basel) 2021;13:3000. [PMID: 34203896 DOI: 10.3390/cancers13123000] [Cited by in Crossref: 9] [Cited by in F6Publishing: 11] [Article Influence: 4.5] [Reference Citation Analysis]
16 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]
17 Sohn CK, Bisdas S. Diagnostic Accuracy of Machine Learning-Based Radiomics in Grading Gliomas: Systematic Review and Meta-Analysis. Contrast Media Mol Imaging 2020;2020:2127062. [PMID: 33746649 DOI: 10.1155/2020/2127062] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
18 Castillo T JM, Starmans MPA, Arif M, Niessen WJ, Klein S, Bangma CH, Schoots IG, Veenland JF. A Multi-Center, Multi-Vendor Study to Evaluate the Generalizability of a Radiomics Model for Classifying Prostate cancer: High Grade vs. Low Grade. Diagnostics (Basel) 2021;11:369. [PMID: 33671533 DOI: 10.3390/diagnostics11020369] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 5.5] [Reference Citation Analysis]
19 Crombé A, Buy X, Han F, Toupin S, Kind M. Assessment of Repeatability, Reproducibility, and Performances of T2 Mapping-Based Radiomics Features: A Comparative Study. J Magn Reson Imaging 2021;54:537-48. [PMID: 33594768 DOI: 10.1002/jmri.27558] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
20 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]
21 Priya S, Agarwal A, Ward C, Locke T, Monga V, Bathla G. Survival prediction in glioblastoma on post-contrast magnetic resonance imaging using filtration based first-order texture analysis: Comparison of multiple machine learning models. Neuroradiol J 2021;34:355-62. [PMID: 33533273 DOI: 10.1177/1971400921990766] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 3.0] [Reference Citation Analysis]
22 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]
23 Jarraya M, Heiss R, Duryea J, Nagel AM, Lynch JA, Guermazi A, Weber MA, Arkudas A, Horch RE, Uder M, Roemer FW. Bone Structure Analysis of the Radius Using Ultrahigh Field (7T) MRI: Relevance of Technical Parameters and Comparison with 3T MRI and Radiography. Diagnostics (Basel) 2021;11:110. [PMID: 33445536 DOI: 10.3390/diagnostics11010110] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
24 Pinho MC, Bera K, Beig N, Tiwari P. MRI Morphometry in Brain Tumors: Challenges and Opportunities in Expert, Radiomic, and Deep-Learning-Based Analyses. Brain Tumors 2021. [DOI: 10.1007/978-1-0716-0856-2_14] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
25 Lee S, Kim KW; Alzheimer’s Disease Neuroimaging Initiative. Associations between texture of T1-weighted magnetic resonance imaging and radiographic pathologies in Alzheimer's disease. Eur J Neurol 2021;28:735-44. [PMID: 33098172 DOI: 10.1111/ene.14609] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
26 Naganawa S, Kim J, Yip SSF, Ota Y, Srinivasan A, Moritani T. Texture analysis of T2-weighted MRI predicts SDH mutation in paraganglioma. Neuroradiology 2021;63:547-54. [PMID: 33215243 DOI: 10.1007/s00234-020-02607-5] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
27 Saint Martin MJ, Orlhac F, Akl P, Khalid F, Nioche C, Buvat I, Malhaire C, Frouin F. A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study. MAGMA 2021;34:355-66. [PMID: 33180226 DOI: 10.1007/s10334-020-00892-y] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
28 Crombé A, Kind M, Fadli D, Le Loarer F, Italiano A, Buy X, Saut O. Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients. Sci Rep 2020;10:15496. [PMID: 32968131 DOI: 10.1038/s41598-020-72535-0] [Cited by in Crossref: 15] [Cited by in F6Publishing: 18] [Article Influence: 5.0] [Reference Citation Analysis]
29 Csutak C, Ștefan PA, Lenghel LM, Moroșanu CO, Lupean RA, Șimonca L, Mihu CM, Lebovici A. Differentiating High-Grade Gliomas from Brain Metastases at Magnetic Resonance: The Role of Texture Analysis of the Peritumoral Zone. Brain Sci 2020;10:E638. [PMID: 32947822 DOI: 10.3390/brainsci10090638] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 4.0] [Reference Citation Analysis]
30 T JMC, Arif M, Niessen WJ, Schoots IG, Veenland JF. Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications. Cancers (Basel) 2020;12:E1606. [PMID: 32560558 DOI: 10.3390/cancers12061606] [Cited by in Crossref: 31] [Cited by in F6Publishing: 34] [Article Influence: 10.3] [Reference Citation Analysis]
31 Staalduinen EK, Bangiyev L. Editorial for “Texture Analysis of High b‐value Diffusion‐Weighted Imaging for Evaluating Consistency of Pituitary Macroadenomas”. J Magn Reson Imaging 2020;51:1514-1515. [DOI: 10.1002/jmri.27130] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
32 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]
33 Scalco E, Belfatto A, Mastropietro A, Rancati T, Avuzzi B, Messina A, Valdagni R, Rizzo G. T2w‐MRI signal normalization affects radiomics features reproducibility. Med Phys 2020;47:1680-91. [DOI: 10.1002/mp.14038] [Cited by in Crossref: 48] [Cited by in F6Publishing: 50] [Article Influence: 16.0] [Reference Citation Analysis]
34 Crombé A, Fadli D, Buy X, Italiano A, Saut O, Kind M. High-Grade Soft-Tissue Sarcomas: Can Optimizing Dynamic Contrast-Enhanced MRI Postprocessing Improve Prognostic Radiomics Models? J Magn Reson Imaging 2020;52:282-97. [PMID: 31922323 DOI: 10.1002/jmri.27040] [Cited by in Crossref: 14] [Cited by in F6Publishing: 16] [Article Influence: 4.7] [Reference Citation Analysis]
35 Moradmand H, Aghamiri SMR, Ghaderi R. Impact of image preprocessing methods on reproducibility of radiomic features in multimodal magnetic resonance imaging in glioblastoma. J Appl Clin Med Phys 2020;21:179-90. [PMID: 31880401 DOI: 10.1002/acm2.12795] [Cited by in Crossref: 58] [Cited by in F6Publishing: 65] [Article Influence: 14.5] [Reference Citation Analysis]
36 Wu RY, Liu AY, Yang J, Williamson TD, Wisdom PG, Bronk L, Gao S, Grosshan DR, Fuller DC, Gunn GB, Ronald Zhu X, Frank SJ. Evaluation of the accuracy of deformable image registration on MRI with a physical phantom. J Appl Clin Med Phys 2020;21:166-73. [PMID: 31808307 DOI: 10.1002/acm2.12789] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 2.0] [Reference Citation Analysis]
37 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]
38 Soni N, Priya S, Bathla G. Texture Analysis in Cerebral Gliomas: A Review of the Literature. AJNR Am J Neuroradiol 2019;40:928-34. [PMID: 31122918 DOI: 10.3174/ajnr.A6075] [Cited by in Crossref: 45] [Cited by in F6Publishing: 47] [Article Influence: 11.3] [Reference Citation Analysis]
39 Crombé A, Saut O, Guigui J, Italiano A, Buy X, Kind M. Influence of temporal parameters of DCE‐MRI on the quantification of heterogeneity in tumor vascularization. J Magn Reson Imaging 2019;50:1773-88. [DOI: 10.1002/jmri.26753] [Cited by in Crossref: 11] [Cited by in F6Publishing: 13] [Article Influence: 2.8] [Reference Citation Analysis]