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For: Cunliffe AR, Armato SG 3rd, Fei XM, Tuohy RE, Al-Hallaq HA. Lung texture in serial thoracic CT scans: registration-based methods to compare anatomically matched regions. Med Phys 2013;40:061906. [PMID: 23718597 DOI: 10.1118/1.4805110] [Cited by in Crossref: 17] [Cited by in F6Publishing: 18] [Article Influence: 2.1] [Reference Citation Analysis]
Number Citing Articles
1 Foy JJ, Shenouda M, Ramahi S, Armato S, Ginat DT. Effect of an iterative reconstruction quantum noise reduction technique on computed tomography radiomic features. J Med Imaging (Bellingham) 2020;7:064007. [PMID: 33409336 DOI: 10.1117/1.JMI.7.6.064007] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
2 Foy JJ, Al-Hallaq HA, Grekoski V, Tran T, Guruvadoo K, Armato Iii SG, Sensakovic WF. Harmonization of radiomic feature variability resulting from differences in CT image acquisition and reconstruction: assessment in a cadaveric liver. Phys Med Biol 2020;65:205008. [PMID: 33063693 DOI: 10.1088/1361-6560/abb172] [Cited by in Crossref: 2] [Cited by in F6Publishing: 7] [Article Influence: 1.0] [Reference Citation Analysis]
3 Shiri I, Hajianfar G, Sohrabi A, Abdollahi H, P Shayesteh S, Geramifar P, Zaidi H, Oveisi M, Rahmim A. Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: Test-retest and image registration analyses. Med Phys 2020;47:4265-80. [PMID: 32615647 DOI: 10.1002/mp.14368] [Cited by in Crossref: 22] [Cited by in F6Publishing: 28] [Article Influence: 11.0] [Reference Citation Analysis]
4 Foy JJ, Armato SG 3rd, Al-Hallaq HA. Effects of variability in radiomics software packages on classifying patients with radiation pneumonitis. J Med Imaging (Bellingham) 2020;7:014504. [PMID: 32118090 DOI: 10.1117/1.JMI.7.1.014504] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
5 Kloth C, Thaiss WM, Beck R, Haap M, Fritz J, Beer M, Horger M. Potential role of CT-textural features for differentiation between viral interstitial pneumonias, pneumocystis jirovecii pneumonia and diffuse alveolar hemorrhage in early stages of disease: a proof of principle. BMC Med Imaging 2019;19:39. [PMID: 31113389 DOI: 10.1186/s12880-019-0338-0] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
6 Nie K, Al-Hallaq H, Li XA, Benedict SH, Sohn JW, Moran JM, Fan Y, Huang M, Knopp MV, Michalski JM, Monroe J, Obcemea C, Tsien CI, Solberg T, Wu J, Xia P, Xiao Y, El Naqa I. NCTN Assessment on Current Applications of Radiomics in Oncology. Int J Radiat Oncol Biol Phys 2019;104:302-15. [PMID: 30711529 DOI: 10.1016/j.ijrobp.2019.01.087] [Cited by in Crossref: 18] [Cited by in F6Publishing: 23] [Article Influence: 6.0] [Reference Citation Analysis]
7 Shi L, He Y, Yuan Z, Benedict S, Valicenti R, Qiu J, Rong Y. Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer. Technol Cancer Res Treat 2018;17:1533033818782788. [PMID: 29940810 DOI: 10.1177/1533033818782788] [Cited by in Crossref: 30] [Cited by in F6Publishing: 42] [Article Influence: 7.5] [Reference Citation Analysis]
8 Foy JJ, Robinson KR, Li H, Giger ML, Al-hallaq H, Armato SG. Variation in algorithm implementation across radiomics software. J Med Imag 2018;5:1. [DOI: 10.1117/1.jmi.5.4.044505] [Cited by in Crossref: 20] [Cited by in F6Publishing: 32] [Article Influence: 5.0] [Reference Citation Analysis]
9 Choi W, Riyahi S, Kligerman SJ, Liu CJ, Mechalakos JG, Lu W. Technical Note: Identification of CT Texture Features Robust to Tumor Size Variations for Normal Lung Texture Analysis. Int J Med Phys Clin Eng Radiat Oncol 2018;7:330-8. [PMID: 31131158 DOI: 10.4236/ijmpcero.2018.73027] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 0.8] [Reference Citation Analysis]
10 Scalco E, Rancati T, Pirovano I, Mastropietro A, Palorini F, Cicchetti A, Messina A, Avuzzi B, Valdagni R, Rizzo G. Texture analysis of T1-w and T2-w MR images allows a quantitative evaluation of radiation-induced changes of internal obturator muscles after radiotherapy for prostate cancer. Med Phys 2018;45:1518-28. [DOI: 10.1002/mp.12798] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 1.3] [Reference Citation Analysis]
11 Zhang Z, Yang J, Ho A, Jiang W, Logan J, Wang X, Brown PD, McGovern SL, Guha-Thakurta N, Ferguson SD, Fave X, Zhang L, Mackin D, Court LE, Li J. A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images. Eur Radiol 2018;28:2255-63. [PMID: 29178031 DOI: 10.1007/s00330-017-5154-8] [Cited by in Crossref: 48] [Cited by in F6Publishing: 67] [Article Influence: 9.6] [Reference Citation Analysis]
12 Phillips I, Ajaz M, Ezhil V, Prakash V, Alobaidli S, McQuaid SJ, South C, Scuffham J, Nisbet A, Evans P. Clinical applications of textural analysis in non-small cell lung cancer. Br J Radiol 2018;91:20170267. [PMID: 28869399 DOI: 10.1259/bjr.20170267] [Cited by in Crossref: 18] [Cited by in F6Publishing: 20] [Article Influence: 3.6] [Reference Citation Analysis]
13 Scalco E, Rizzo G. Texture analysis of medical images for radiotherapy applications. Br J Radiol 2017;90:20160642. [PMID: 27885836 DOI: 10.1259/bjr.20160642] [Cited by in Crossref: 59] [Cited by in F6Publishing: 68] [Article Influence: 9.8] [Reference Citation Analysis]
14 Cunliffe AR, White B, Justusson J, Straus C, Malik R, Al-Hallaq HA, Armato SG 3rd. Comparison of Two Deformable Registration Algorithms in the Presence of Radiologic Change Between Serial Lung CT Scans. J Digit Imaging 2015;28:755-60. [PMID: 25822396 DOI: 10.1007/s10278-015-9789-1] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 1.0] [Reference Citation Analysis]
15 Zhang L, Fried DV, Fave XJ, Hunter LA, Yang J, Court LE. IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys 2015;42:1341-53. [PMID: 25735289 DOI: 10.1118/1.4908210] [Cited by in Crossref: 173] [Cited by in F6Publishing: 192] [Article Influence: 28.8] [Reference Citation Analysis]
16 Yang J, Zhang L, Fave XJ, Fried DV, Stingo FC, Ng CS, Court LE. Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors. Comput Med Imaging Graph 2016;48:1-8. [PMID: 26745258 DOI: 10.1016/j.compmedimag.2015.12.001] [Cited by in Crossref: 27] [Cited by in F6Publishing: 27] [Article Influence: 3.9] [Reference Citation Analysis]
17 Cunliffe A, Armato SG 3rd, Castillo R, Pham N, Guerrero T, Al-Hallaq HA. Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development. Int J Radiat Oncol Biol Phys 2015;91:1048-56. [PMID: 25670540 DOI: 10.1016/j.ijrobp.2014.11.030] [Cited by in Crossref: 109] [Cited by in F6Publishing: 127] [Article Influence: 15.6] [Reference Citation Analysis]
18 Cunliffe AR, Armato SG 3rd, Straus C, Malik R, Al-Hallaq HA. Lung texture in serial thoracic CT scans: correlation with radiologist-defined severity of acute changes following radiation therapy. Phys Med Biol 2014;59:5387-98. [PMID: 25157625 DOI: 10.1088/0031-9155/59/18/5387] [Cited by in Crossref: 20] [Cited by in F6Publishing: 19] [Article Influence: 2.5] [Reference Citation Analysis]