Similar articles for PMID: 28341714

Year	Number of Results
1996	1
1999	1
2000	1
2001	1
2002	3
2003	3
2005	4
2006	3
2007	4
2008	2
2009	2
2010	1
2011	2
2012	4
2013	4
2014	9
2015	3
2016	8
2017	7
2018	15
2019	16
2020	7
2024	0

1

Quantitative Assessment of Variation in CT Parameters on Texture Features: Pilot Study Using a Nonanatomic Phantom.

Buch K, Li B, Qureshi MM, Kuno H, Anderson SW, Sakai O. Buch K, et al. AJNR Am J Neuroradiol. 2017 May;38(5):981-985. doi: 10.3174/ajnr.A5139. Epub 2017 Mar 24. AJNR Am J Neuroradiol. 2017. PMID: 28341714 Free PMC article.

2

Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model.

Buch K, Kuno H, Qureshi MM, Li B, Sakai O. Buch K, et al. J Appl Clin Med Phys. 2018 Nov;19(6):253-264. doi: 10.1002/acm2.12482. Epub 2018 Oct 27. J Appl Clin Med Phys. 2018. PMID: 30369010 Free PMC article.

3

Time stability of delta-radiomics features and the impact on patient analysis in longitudinal CT images.

Plautz TE, Zheng C, Noid G, Li XA. Plautz TE, et al. Med Phys. 2019 Apr;46(4):1663-1676. doi: 10.1002/mp.13395. Epub 2019 Feb 18. Med Phys. 2019. PMID: 30695103

4

Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels.

Shafiq-Ul-Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y, Abdalah MA, Schabath MB, Goldgof DG, Mackin D, Court LE, Gillies RJ, Moros EG. Shafiq-Ul-Hassan M, et al. Med Phys. 2017 Mar;44(3):1050-1062. doi: 10.1002/mp.12123. Med Phys. 2017. PMID: 28112418 Free PMC article.

5

Delta-radiomics increases multicentre reproducibility: a phantom study.

Nardone V, Reginelli A, Guida C, Belfiore MP, Biondi M, Mormile M, Banci Buonamici F, Di Giorgio E, Spadafora M, Tini P, Grassi R, Pirtoli L, Correale P, Cappabianca S, Grassi R. Nardone V, et al. Med Oncol. 2020 Mar 31;37(5):38. doi: 10.1007/s12032-020-01359-9. Med Oncol. 2020. PMID: 32236847

6

Reliability of CT-based texture features: Phantom study.

Varghese BA, Hwang D, Cen SY, Levy J, Liu D, Lau C, Rivas M, Desai B, Goodenough DJ, Duddalwar VA. Varghese BA, et al. J Appl Clin Med Phys. 2019 Aug;20(8):155-163. doi: 10.1002/acm2.12666. Epub 2019 Jun 20. J Appl Clin Med Phys. 2019. PMID: 31222919 Free PMC article.

7

Variability in CT lung-nodule quantification: Effects of dose reduction and reconstruction methods on density and texture based features.

Lo P, Young S, Kim HJ, Brown MS, McNitt-Gray MF. Lo P, et al. Med Phys. 2016 Aug;43(8):4854. doi: 10.1118/1.4954845. Med Phys. 2016. PMID: 27487903 Free PMC article.

8

Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study.

Larue RTHM, van Timmeren JE, de Jong EEC, Feliciani G, Leijenaar RTH, Schreurs WMJ, Sosef MN, Raat FHPJ, van der Zande FHR, Das M, van Elmpt W, Lambin P. Larue RTHM, et al. Acta Oncol. 2017 Nov;56(11):1544-1553. doi: 10.1080/0284186X.2017.1351624. Epub 2017 Sep 8. Acta Oncol. 2017. PMID: 28885084

9

Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics.

Orlhac F, Frouin F, Nioche C, Ayache N, Buvat I. Orlhac F, et al. Radiology. 2019 Apr;291(1):53-59. doi: 10.1148/radiol.2019182023. Epub 2019 Jan 29. Radiology. 2019. PMID: 30694160

10

Precision of quantitative computed tomography texture analysis using image filtering: A phantom study for scanner variability.

Yasaka K, Akai H, Mackin D, Court L, Moros E, Ohtomo K, Kiryu S. Yasaka K, et al. Medicine (Baltimore). 2017 May;96(21):e6993. doi: 10.1097/MD.0000000000006993. Medicine (Baltimore). 2017. PMID: 28538408 Free PMC article.

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