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For: Cunliffe AR, Al-Hallaq HA, Labby ZE, Pelizzari CA, Straus C, Sensakovic WF, Ludwig M, Armato SG. Lung texture in serial thoracic CT scans: assessment of change introduced by image registration. Med Phys 2012;39:4679-90. [PMID: 22894392 DOI: 10.1118/1.4730505] [Cited by in Crossref: 19] [Cited by in F6Publishing: 21] [Article Influence: 1.9] [Reference Citation Analysis]
Number Citing Articles
1 Nichols KJ, DiFilippo FP, Palestro CJ. Computational approaches to detect small lesions in 18 F-FDG PET/CT scans. J Appl Clin Med Phys 2021. [PMID: 34643029 DOI: 10.1002/acm2.13451] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
2 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]
3 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]
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 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]
6 Nichols KJ, Difilippo FP, Palestro CJ. Texture analysis for automated evaluation of Jaszczak phantom SPECT system tests. Med Phys 2019;46:262-72. [DOI: 10.1002/mp.13289] [Cited by in Crossref: 5] [Cited by in F6Publishing: 8] [Article Influence: 1.3] [Reference Citation Analysis]
7 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]
8 Monti S, Pacelli R, Cella L, Palma G. Inter-patient image registration algorithms to disentangle regional dose bioeffects. Sci Rep 2018;8:4915. [PMID: 29559687 DOI: 10.1038/s41598-018-23327-0] [Cited by in Crossref: 13] [Cited by in F6Publishing: 15] [Article Influence: 3.3] [Reference Citation Analysis]
9 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]
10 Brock KK, Mutic S, McNutt TR, Li H, Kessler ML. Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132. Med Phys 2017;44:e43-76. [PMID: 28376237 DOI: 10.1002/mp.12256] [Cited by in Crossref: 279] [Cited by in F6Publishing: 330] [Article Influence: 55.8] [Reference Citation Analysis]
11 Anthony GJ, Cunliffe A, Castillo R, Pham N, Guerrero T, Armato SG 3rd, Al-Hallaq HA. Incorporation of pre-therapy 18 F-FDG uptake data with CT texture features into a radiomics model for radiation pneumonitis diagnosis. Med Phys 2017;44:3686-94. [PMID: 28422299 DOI: 10.1002/mp.12282] [Cited by in Crossref: 25] [Cited by in F6Publishing: 24] [Article Influence: 5.0] [Reference Citation Analysis]
12 Bogowicz M, Riesterer O, Bundschuh RA, Veit-Haibach P, Hüllner M, Studer G, Stieb S, Glatz S, Pruschy M, Guckenberger M, Tanadini-Lang S. Stability of radiomic features in CT perfusion maps. Phys Med Biol 2016;61:8736-49. [PMID: 27893446 DOI: 10.1088/1361-6560/61/24/8736] [Cited by in Crossref: 31] [Cited by in F6Publishing: 38] [Article Influence: 5.2] [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 Palma G, Monti S, D'Avino V, Conson M, Liuzzi R, Pressello MC, Donato V, Deasy JO, Quarantelli M, Pacelli R, Cella L. A Voxel-Based Approach to Explore Local Dose Differences Associated With Radiation-Induced Lung Damage. Int J Radiat Oncol Biol Phys 2016;96:127-33. [PMID: 27511851 DOI: 10.1016/j.ijrobp.2016.04.033] [Cited by in Crossref: 31] [Cited by in F6Publishing: 33] [Article Influence: 5.2] [Reference Citation Analysis]
17 Cunliffe AR, Contee C, Armato SG 3rd, White B, Justusson J, Malik R, Al-Hallaq HA. Effect of deformable registration on the dose calculated in radiation therapy planning CT scans of lung cancer patients. Med Phys 2015;42:391-9. [PMID: 25563279 DOI: 10.1118/1.4903267] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 1.6] [Reference Citation Analysis]
18 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]
19 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]
20 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]
21 Vinogradskiy Y, Diot Q, Kavanagh B, Schefter T, Gaspar L, Miften M. Spatial and dose-response analysis of fibrotic lung changes after stereotactic body radiation therapy: Evaluating fibrotic lung changes after SBRT. Med Phys 2013;40:081712. [DOI: 10.1118/1.4813916] [Cited by in Crossref: 12] [Cited by in F6Publishing: 14] [Article Influence: 1.3] [Reference Citation Analysis]