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DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapya)
1. A. Jackson, D. L. Buckley, and G. J. M. Parker, Contrast-Enhanced Magnetic Resonance Imaging in Oncology (Springer, Berlin, 2005).
2. A. Jackson, J. P. B. O’Connor, G. J. M. Parker, and G. C. Jayson, “Imaging tumor vascular heterogeneity and angiogenesis using dynamic contrast-enhanced magnetic resonance imaging,” Clin. Cancer Res. 13, 3449–3459 (2007).
3. T. E. Yankeelov and J. C. Gore, “Dynamic contrast enhanced magnetic resonance imaging in oncology: Theory, data acquisition, analysis, and examples,” Curr. Med. Imaging Rev. 3, 91–107 (2007).
5. C. Hayes, A. R. Padhani, and M. O. Leach, “Assessing changes in tumor vascular function using dynamic contrast-enhanced magnetic resonance imaging,” NMR Biomed. 15, 154–163 (2002).
7. E. Eyal and H. Degani, “Model-based and model-free parametric analysis of breast dynamic-contrast-enhanced MRI,” NMR Biomed. 22, 40–53 (2009).
8. F. Frouin, J. P. Bazin, M. D. Paola, O. Jolivet, and R. D. Paola, “FAMIS: A software package for functional feature extraction from biomedical multidimensional images,” Comput. Med. Imaging Graph. 16, 81–91 (1992).
9. A. M. Zagdanski, R. Sigal, J. Bosq, J. P. Bazin, D. Vanel, and R. D. Paola, “Factor analysis of medical image sequences in MR of head and neck tumors,” Am. J. Neuroradiol. 15, 1359–1368 (1994).
10. S. S. Yoo, C. B. Gil, J. Y. Han, and H. Hee Kim, “Independent component analysis for the examination of dynamic contrast-enhanced breast magnetic resonance imaging data: Preliminary study,” Invest. Radiol. 37, 647–654 (2002).
11. T. Twellmann, A. Saalbach, O. Gerstung, M. O. Leach, and T. W. Nattkemper, “Image fusion for dynamic contrast enhanced magnetic resonance imaging,” Biomed. Eng. Online 3, 35 (2004).
12. E. Eyal, N. B. Bloch, N. M. Rofsky, E. Furman-Haran, E. M. Genega, R. E. Lenkinski, and H. Degani, “Principal component analysis of dynamic contrast enhanced MRI in human prostate cancer,” Invest. Radiol. 45, 174–181 (2010).
13. B. Bloch, E. Eyal, N. M. Rofsky, E. Furman-Haran, H. Degani, E. M. Genega, W. C. Dewolf, G. J. Bubley, and R. E. Lenkinski, “Computer-aided diagnosis of prostate cancer: Clinical utility of integrating model-free and kinetic-based analysis of high spatial resolution dynamic contrast enhanced 3 tesla MRI,” Proc. Intl. Soc. Mag. Reson. Med. 16 (2008).
14. M. J. Bruwer, J. F. MacGregor, and M. D. Noseworthy, “Dynamic contrast-enhanced MRI diagnostics in oncology via principal component analysis,” J. Chemom. 22, 708–716 (2008).
15. E. Eyal, D. Badikhi, E. Furman-Haran, F. Kelcz, K. J. Kirshenbaum, and H. Degani, “Principal component analysis of breast DCE-MRI adjusted with a model-based method,” J. Magn. Reson. Imaging 30, 989–998 (2009).
16. E. Eyal et al., “Combination of model-free and model-based analysis of dynamic contrast enhanced MRI for breast cancer diagnosis,” Proc. SPIE 6916, 69161B–1 (2008).
17. R. Farjam, C. I. Tsien, F. Y. Feng, D. Gomez-Hassan, J. A. Hayman, T. S. Lawrence, and Y. Cao, “Physiological imaging-defined response-driven subvolume of a tumor,” Int. J. Radiat. Oncol., Biol., Phys. 85, 1383–1390 (2013)
19. A. Jackson, D. L. Buckley, and G. J. M. Parker, Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Oncology (Springer-Verlag, Berlin/Heidelberg, 2005), pp. 69–79.
20. H. Spath, One Dimensional Spline Interpolation Algorithms (A K Peters Ltd., Wellesley, MA, 1995), p. 416.
21. Y. Cao, Z. Shen, T. L. Chenevert, and J. R. Ewing, “Estimate of vascular permeability and cerebral blood volume using Gd-DTPA contrast enhancement and dynamic T2*-weighted MRI,” J. Magn. Reson. Imaging 24, 288–296 (2006).
22. M. Law, S. Yang, J. S. Babb, E. A. Knopp, J. G. Golfinos, D. Zagzag, and G. Johnson, “Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with Glioma grade,” Am. J. Neuroradiol. 25, 746–755 (2007).
23. M. Law et al., “Gliomas: Predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR Imaging,” Radiology 247, 490–498 (2008).
25. Y. Cao, C. I. Tsien, V. Nagesh, L. Junck, R. Ten Haken, B. D. Ross, T. L. Chenevert, and T. S. Lawrence, “Survival prediction in high-grade gliomas by perfusion MRI prior to and during early stage of RT,” Int. J. Radiat. Oncol., Biol., Phys. 64, 876–885 (2006).
26. P. Lambin, R. G. Van Stiphout, M. H. Starmans, E. Rios-Velazquez, G. Nalbantov, H. J. Aerts, E. Roelofs, W. Van Elmpt, P. C. Boutros, P. Granone, V. Valentini, A. C. Begg, D. De Ruysscher, and A. Dekker, “Predicting outcomes in radiation oncology—Multifactorial decision support systems,” Nat. Rev. Clin. Oncol. 10, 27–40 (2013).
27. P. E. Huber et al., “Transient enlargement of contrast uptake on MRI after linear accelerator (linac) stereotactic radiosurgery for brain metastases,” Int. J. Radiat. Oncol., Biol., Phys. 49, 1339–1349 (2001).
28. J. Eng, ROC analysis: Web-based calculator for ROC curves (Johns Hopkins University, Baltimore, 2012).
29. E. R. DeLong, D. M. DeLong, and D. L. Clarke-Pearson, “Comparing the area under two or more correlated receiver operating characteristic curve: A nonparametric approach,” Biometrics 44, 837–845 (1988).
30. N. Tuncbilek, M. Kaplan, S. Altaner, I. H. Atakan, N. Süt, O. Inci, and M. K. Demir, “Value of dynamic contrast-enhanced MRI and correlation with tumor angiogenesis in bladder cancer,” AJR, Am. J. Roentgenol. 192, 949–955 (2009).
31. Y. Cao, D. Li, Z. Shen, and D. Normolle, “Sensitivity of quantitative metrics derived from DCE MRI and a pharmacokinetic model to image quality and acquisition parameters,” Acad. Radiol. 17, 468–478 (2010).
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To develop a pharmacokinetic modelfree framework to analyze the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data for assessment of response of brain metastases to radiation therapy.
Twenty patients with 45 analyzable brain metastases had MRI scans prior to whole brain radiation therapy (WBRT) and at the end of the 2-week therapy. The volumetric DCE images covering the whole brain were acquired on a 3T scanner with approximately 5 s temporal resolution and a total scan time of about 3 min. DCE curves from all voxels of the 45 brain metastases were normalized and then temporally aligned. A DCE matrix that is constructed from the aligned DCE curves of all voxels of the 45 lesions obtained prior to WBRT is processed by principal component analysis to generate the principal components (PCs). Then, the projection coefficient maps prior to and at the end of WBRT are created for each lesion. Next, a pattern recognition technique, based upon fuzzy-c-means clustering, is used to delineate the tumor subvolumes relating to the value of the significant projection coefficients. The relationship between changes in different tumor subvolumes and treatment response was evaluated to differentiate responsive from stable and progressive tumors. Performance of the PC-defined tumor subvolume was also evaluated by receiver operating characteristic (ROC) analysis in prediction of nonresponsive lesions and compared with physiological-defined tumor subvolumes.
The projection coefficient maps of the first three PCs contain almost all response-related information in DCE curves of brain metastases. The first projection coefficient, related to the area under DCE curves, is the major component to determine response while the third one has a complimentary role. In ROC analysis, the area under curve of 0.88 ± 0.05 and 0.86 ± 0.06 were achieved for the PC-defined and physiological-defined tumor subvolume in response assessment.
The PC-defined subvolume of a brain metastasis could predict tumor response to therapy similar to the physiological-defined one, while the former is determined more rapidly for clinical decision-making support.
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