Publisher Correction: A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations (Scientific Reports (2017) DOI: 10.1038/s41598-017-11554-w);A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations

Scientific Reports

Volumn 7, Issue 1, 2017, Pages

  Ger, Rachel B ^a,b   Mohamed, Abdallah S R ^a,c   Awan, Musaddiq J ^d,e   Ding, Yao ^a   Li, Kimberly ^f,g   Fave, Xenia J ^a,b   Beers, Andrew L ^h   Driscoll, Brandon ⁱ   Elhalawani, Hesham ^a   Hormuth, David A ^j   Houdt, Petra J Van ^k   He, Renjie ^l   Zhou, Shouhao ^a   Mathieu, Kelsey B ^a   Li, Heng ^a,b   Coolens, Catherine ^i,m,n   Chung, Caroline ^a,i   Bankson, James A ^a,b   Huang, Wei ^f   Wang, Jihong ^a,b   Sandulache, Vlad C ^o   Lai, Stephen Y ^a   Howell, Rebecca M ^a,b   Stafford, R Jason ^a,b   Yankeelov, Thomas E ^j   Heide, Uulke A Van Der ^k   Frank, Steven J ^a   Barboriak, Daniel P ^p   Hazle, John D ^a,b   Court, Laurence E ^a,b   Kalpathy Cramer, Jayashree ^h   Fuller, Clifton D ^a,b   more..

^a UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

^b UNIVERSITY OF TEXAS   (United States)

^c ALEXANDRIA UNIVERSITY   (Egypt)

^d CASE WESTERN RESERVE UNIVERSITY   (United States)

^e UNIVERSITY HOSPITALS OF CLEVELAND   (United States)

^f OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

^g Beaverton School District   (United States)

^h MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

ⁱ UNIVERSITY HEALTH NETWORK   (Canada)

^j UNIVERSITY OF TEXAS AT AUSTIN   (United States)

^k NETHERLANDS CANCER INSTITUTE   (Netherlands)

^l United Imaging Healthcare America   (United States)

^m PRINCESS MARGARET HOSPITAL   (Canada)

ⁿ UNIVERSITY OF TORONTO   (Canada)

^o BAYLOR COLLEGE OF MEDICINE   (United States)

^p DUKE UNIVERSITY MEDICAL CENTER   (United States)

more..

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85029332732     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/s41598-018-20639-z     Document Type: Erratum

References (54)

1. Jemal, A. et al. Global cancer statistics. CA: a cancer journal for clinicians 61, 69-90, doi:https://doi. org/10. 3322/caac. 20107 (2011).
2. Howlader, N. et al. SEER Cancer Statistics Review, 1975-2014, National Cancer Institute. Bethesda, MD, https://seer. cancer. gov/ csr/1975-2014/, based on November 2016 SEER data submission, posted to the SEER web site, April 2017.
3. Bernstein, J. M., Bernstein, C. R., West, C. M. & Homer, J. J. Molecular and cellular processes underlying the hallmarks of head and neck cancer. European archives of oto-rhino-laryngology 270, 2585-2593, doi:https://doi. org/10. 1007/s00405-012-2323-x (2013).
4. Horsman, M. R., Mortensen, L. S., Petersen, J. B., Busk, M. & Overgaard, J. Imaging hypoxia to improve radiotherapy outcome. Nature reviews clinical oncology 9, 674-687, doi:https://doi. org/10. 1038/nrclinonc. 2012. 171 (2012).
5. Houweling, A. C. et al. MRI to quantify early radiation-induced changes in the salivary glands. Radiotherapy and oncology 100, 386-389, doi:https://doi. org/10. 1016/j. radonc. 2011. 08. 020 (2011).
6. Juan, C. J. et al. Perfusion characteristics of late radiation injury of parotid glands: quantitative evaluation with dynamic contrastenhanced MRI. European radiology 19, 94-102, doi:https://doi. org/10. 1007/s00330-008-1104-9 (2009).
7. Bernstein, J. M., Homer, J. J. & West, C. M. Dynamic contrast-enhanced magnetic resonance imaging biomarkers in head and neck cancer: potential to guide treatment? A systematic review. Oral oncology 50, 963-970, doi:https://doi. org/10. 1016/j. oraloncology. 2014. 07. 011 (2014).
8. Noij, D. P. et al. Contrast-enhanced perfusion magnetic resonance imaging for head and neck squamous cell carcinoma: a systematic review. Oral oncology 51, 124-138, doi:https://doi. org/10. 1016/j. oraloncology. 2014. 10. 016 (2015).
9. Cheng, C. C. et al. Parotid perfusion in nasopharyngeal carcinoma patients in early-to-intermediate stage after low-dose intensitymodulated radiotherapy: evaluated by fat-saturated dynamic contrast-enhanced magnetic resonance imaging. Magnetic resonance imaging 31, 1278-1284, doi:https://doi. org/10. 1016/j. mri. 2013. 03. 018 (2013).
10. Lee, F. K., King, A. D., Kam, M. K., Ma, B. B. & Yeung, D. K. Radiation injury of the parotid glands during treatment for head and neck cancer: assessment using dynamic contrast-enhanced MR imaging. Radiation research 175, 291-296, doi:https://doi. org/10. 1667/RR2370. 1 (2011).
11. Cooperative, J. Ha. N. R.-M. D. Dynamic contrast-enhanced MRI detects acute radiotherapy-induced alterations in mandibular microvasculature: prospective assessment of imaging biomarkers of normal tissue injury. Scientific reports 6, 29864, doi:https://doi. org/10. 1038/srep29864 (2016).
12. Huang, W. et al. The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge. Tomography 2, 56-66, doi:https://doi. org/10. 18383/j. tom. 2015. 00184 (2016).
13. Yankeelov, T. E. & Gore, J. C. Dynamic Contrast Enhanced Magnetic Resonance Imaging in Oncology: Theory, Data Acquisition, Analysis, and Examples. Current medical imaging reviews 3, 91-107, doi:https://doi. org/10. 2174/157340507780619179 (2009).
14. Yankeelov, T. E., Rooney, W. D., Li, X. & Springer, C. S. Jr. Variation of the relaxographic "shutter-speed" for transcytolemmal water exchange affects the CR bolus-tracking curve shape. Magnetic resonance in medicine 50, 1151-1169, doi:https://doi. org/10. 1002/ mrm. 10624 (2003).
15. Leach, M. et al. Imaging vascular function for early stage clinical trials using dynamic contrast-enhanced magnetic resonance imaging. European radiology 22, 1451-1464 (2012).
16. Schabel, M. C. & Parker, D. L. Uncertainty and bias in contrast concentration measurements using spoiled gradient echo pulse sequences. Physics in medicine and biology 53, 2345-2373, doi:https://doi. org/10. 1088/0031-9155/53/9/010 (2008).
17. Yang, C. et al. Reproducibility assessment of a multiple reference tissue method for quantitative dynamic contrast enhanced-MRI analysis. Magnetic resonance in medicine 61, 851-859, doi:https://doi. org/10. 1002/mrm. 21912 (2009).
18. Heisen, M. et al. The influence of temporal resolution in determining pharmacokinetic parameters from DCE-MRI data. Magnetic resonance in medicine 63, 811-816, doi:https://doi. org/10. 1002/mrm. 22171 (2010).
19. Di Giovanni, P. et al. The accuracy of pharmacokinetic parameter measurement in DCE-MRI of the breast at 3 T. Physics in medicine and biology 55, 121-132, doi:https://doi. org/10. 1088/0031-9155/55/1/008 (2010).
20. Sourbron, S. P. & Buckley, D. L. On the scope and interpretation of the Tofts models for DCE-MRI. Magnetic resonance in medicine 66, 735-745, doi:https://doi. org/10. 1002/mrm. 22861 (2011).
21. Sourbron, S. P. & Buckley, D. L. Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability. Physics in medicine and biology 57, R1-33, doi:https://doi. org/10. 1088/0031-9155/57/2/R1 (2012).
22. Othman, A. E. et al. Comparison of different population-averaged arterial-input-functions in dynamic contrast-enhanced MRI of the prostate: Effects on pharmacokinetic parameters and their diagnostic performance. Magnetic resonance imaging 34, 496-501, doi:https://doi. org/10. 1016/j. mri. 2015. 12. 009 (2016).
23. Tofts, P. S. & Kermode, A. G. Measurement of the blood-brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magnetic resonance in medicine 17, 357-367 (1991).
24. Heye, T. et al. Reproducibility of dynamic contrast-enhanced MR imaging. Part I. Perfusion characteristics in the female pelvis by using multiple computer-aided diagnosis perfusion analysis solutions. Radiology 266, 801-811, doi:https://doi. org/10. 1148/ radiol. 12120278 (2013).
25. Huang, W. et al. Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. Translational oncology 7, 153-166 (2014).
26. Beuzit, L. et al. Dynamic contrast-enhanced MRI: Study of inter-software accuracy and reproducibility using simulated and clinical data. Journal of magnetic resonance imaging 43, 1288-1300, doi:https://doi. org/10. 1002/jmri. 25101 (2016).
27. Cron, G. O. et al. Bias and precision of three different DCE-MRI analysis software packages: a comparison using simulated data. International Society for Magnetic Resonance in Medicine. (Proc 22nd Annual Meeting ISMRM, Milan (abstract 4592) (2014).
28. Tofts, P. S. Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. Journal of magnetic resonance imaging 7, 91-101 (1997).
29. Tofts, P. S. et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. Journal of magnetic resonance imaging 10, 223-232 (1999).
30. Quantitative Imaging Biomarkers Alliance, https://www. rsna. org/QIBA/.
31. Carletta, J. Assessing agreement on classification tasks: the kappa statistic. Computational linguistics 22, 249-254 (1996).
32. Krippendorff, K. Reliability in content analysis. Human communication research 30, 411-433 (2004).
33. Neuendorf, K. A. The content analysis guidebook. (Sage, 2002).
34. Kim, S. et al. Prediction of response to chemoradiation therapy in squamous cell carcinomas of the head and neck using dynamic contrast-enhanced MR imaging. American journal of neuroradiology 31, 262-268, doi:https://doi. org/10. 3174/ajnr. A1817 (2010).
35. Van Cann, E. M. et al. Quantitative dynamic contrast-enhanced MRI for the assessment of mandibular invasion by squamous cell carcinoma. Oral oncology 44, 1147-1154, doi:https://doi. org/10. 1016/j. oraloncology. 2008. 02. 009 (2008).
36. Lee, F. K., King, A. D., Ma, B. B. & Yeung, D. K. Dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) for differential diagnosis in head and neck cancers. European journal of radiology 81, 784-788, doi:https://doi. org/10. 1016/j. ejrad. 2011. 01. 089 (2012).
37. Bisdas, S. et al. An exploratory pilot study into the association between microcirculatory parameters derived by MRI-based pharmacokinetic analysis and glucose utilization estimated by PET-CT imaging in head and neck cancer. European radiology 20, 2358-2366, doi:https://doi. org/10. 1007/s00330-010-1803-x (2010).
38. Tofts, P. S., Berkowitz, B. & Schnall, M. D. Quantitative analysis of dynamic Gd-DTPA enhancement in breast tumors using a permeability model. Magnetic resonance in medicine 33, 564-568 (1995).
39. Ashton, E. Quantitative MR in multi-center clinical trials. Journal of magnetic resonance imaging 31, 279-288, doi:https://doi. org/10. 1002/jmri. 22022 (2010).
40. Schabel, M. C. & Morrell, G. R. Uncertainty in T(1) mapping using the variable flip angle method with two flip angles. Physics in medicine and biology 54, N1-8, doi:https://doi. org/10. 1088/0031-9155/54/1/N01 (2009).
41. Eklund, A., Nichols, T. E. & Knutsson, H. Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. Proceedings of the National Academy of Sciences of the United States of America 113, 7900-7905, doi:https://doi. org/10. 1073/ pnas. 1602413113 (2016).
42. Galbraith, S. M. et al. Reproducibility of dynamic contrast-enhanced MRI in human muscle and tumours: comparison of quantitative and semi-quantitative analysis. NMR in biomedicine 15, 132-142 (2002).
43. Butterworth, E., Jardine, B. E., Raymond, G. M., Neal, M. L. & Bassingthwaighte, J. B. JSim, an open-source modeling system for data analysis. F1000Research 2, 288, doi:https://doi. org/10. 12688/f1000research. 2-288. v1 (2013).
44. Barboriak, D. P. QIBA-v6-Tofts-RevB, https://sites. duke. edu/dblab/qibacontent/.
45. Barboriak, D. P. QIBA-v9-Tofts, https://sites. duke. edu/dblab/qibacontent/.
46. Gelman, A. & Hill, J. Data analysis using regression and multilevel/hierarchical models. 45-46 (Cambridge University Press, 2006).
47. Korporaal, J. G. et al. Phase-based arterial input function measurements in the femoral arteries for quantification of dynamic contrast-enhanced (DCE) MRI and comparison with DCE-CT. Magnetic resonance in medicine 66, 1267-1274, doi:https://doi. org/10. 1002/mrm. 22905 (2011).
48. NordicNeuroLab, http://www. nordicneurolab. com/.
49. Li, X. et al. Dynamic NMR effects in breast cancer dynamic-contrast-enhanced MRI. Proceedings of the National Academy of Sciences of the United States of America 105, 17937-17942, doi:https://doi. org/10. 1073/pnas. 0804224105 (2008).
50. Tudorica, A. et al. Early Prediction and Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy Using Quantitative DCE-MRI. Translational oncology 9, 8-17, doi:https://doi. org/10. 1016/j. tranon. 2015. 11. 016 (2016).
51. Coolens, C. et al. Automated voxel-based analysis of volumetric dynamic contrast-enhanced CT data improves measurement of serial changes in tumor vascular biomarkers. International journal of radiation oncology, biology, physics 91, 48-57, doi:https://doi. org/10. 1016/j. ijrobp. 2014. 09. 028 (2015).
52. Coolens, C., Driscoll, B., Moseley, J., Brock, K. K. & Dawson, L. A. Feasibility of 4D perfusion CT imaging for the assessment of liver treatment response following SBRT and sorafenib. Advances in Radiation Oncology 1, 194-203 (2016).
53. Hormuth, D. A. 2nd, Skinner, J. T., Does, M. D. & Yankeelov, T. E. A comparison of individual and population-derived vascular input functions for quantitative DCE-MRI in rats. Magnetic resonance imaging 32, 397-401, doi:https://doi. org/10. 1016/j. mri. 2013. 12. 019 (2014).
54. Barnes, S. L., Whisenant, J. G., Loveless, M. E. & Yankeelov, T. E. Practical dynamic contrast enhanced MRI in small animal models of cancer: data acquisition, data analysis, and interpretation. Pharmaceutics 4, 442-478, doi:https://doi. org/10. 3390/ pharmaceutics4030442 (2012).