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Li X, Kang H, Arlinghaus LR, et al. Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Transl Oncol. 2014;7(1):14-22doi: 10.1593/tlo.13748.
Li, X., Kang, H., Arlinghaus, L. R., Abramson, R. G., Chakravarthy, A. B., Abramson, V. G., Farley, J., Sanders, M., & Yankeelov, T. E. (2014). Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Translational oncology, 7(1), 14-22. https://doi.org/10.1593/tlo.13748
Li, Xia, et al. "Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer." Translational oncology vol. 7,1 (2014): 14-22. doi: https://doi.org/10.1593/tlo.13748
Li X, Kang H, Arlinghaus LR, Abramson RG, Chakravarthy AB, Abramson VG, Farley J, Sanders M, Yankeelov TE. Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. Transl Oncol. 2014 Feb 01;7(1):14-22. doi: 10.1593/tlo.13748. eCollection 2014 Feb. PMID: 24772203; PMCID: PMC3998687.
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Transl Oncol. 2014 Feb 01;7(1):14-22. doi: 10.1593/tlo.13748. eCollection 2014 Feb.

Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer.

Translational oncology

Xia Li, Hakmook Kang, Lori R Arlinghaus, Richard G Abramson, A Bapsi Chakravarthy, Vandana G Abramson, Jaime Farley, Melinda Sanders, Thomas E Yankeelov

Affiliations

  1. Institute of Imaging Science, Vanderbilt University, Nashville, TN.
  2. Department of Biostatistics, Vanderbilt University, Nashville, TN.
  3. Institute of Imaging Science, Vanderbilt University, Nashville, TN ; Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN ; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN.
  4. Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN ; Department of Radiation Oncology, Vanderbilt University, Nashville, TN.
  5. Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN ; Department of Medical Oncology, Vanderbilt University, Nashville, TN.
  6. Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN ; Department of Pathology, Vanderbilt University, Nashville, TN.
  7. Institute of Imaging Science, Vanderbilt University, Nashville, TN ; Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN ; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN ; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN ; Department of Physics, Vanderbilt University, Nashville, TN ; Department of Cancer Biology, Vanderbilt University, Nashville, TN.

PMID: 24772203 PMCID: PMC3998687 DOI: 10.1593/tlo.13748

Abstract

The purpose of this study is to investigate the ability of multivariate analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) parametric maps, obtained early in the course of therapy, to predict which patients will achieve pathologic complete response (pCR) at the time of surgery. Thirty-three patients underwent DCE-MRI (to estimate K (trans), v e, k ep, and v p) and DW-MRI [to estimate the apparent diffusion coefficient (ADC)] at baseline (t 1) and after the first cycle of neoadjuvant chemotherapy (t 2). Four analyses were performed and evaluated using receiver-operating characteristic (ROC) analysis to test their ability to predict pCR. First, a region of interest (ROI) level analysis input the mean K (trans), v e, k ep, v p, and ADC into the logistic model. Second, a voxel-based analysis was performed in which a longitudinal registration algorithm aligned serial parameters to a common space for each patient. The voxels with an increase in k ep, K (trans), and v p or a decrease in ADC or v e were then detected and input into the regression model. In the third analysis, both the ROI and voxel level data were included in the regression model. In the fourth analysis, the ROI and voxel level data were combined with selected clinical data in the regression model. The overfitting-corrected area under the ROC curve (AUC) with 95% confidence intervals (CIs) was then calculated to evaluate the performance of the four analyses. The combination of k ep, ADC ROI, and voxel level data achieved the best AUC (95% CI) of 0.87 (0.77-0.98).

References

  1. Clin Cancer Res. 2011 Jul 15;17(14):4751-60 - PubMed
  2. Magn Reson Imaging. 2006 Sep;24(7):843-7 - PubMed
  3. Inf Process Med Imaging. 2009;21:276-87 - PubMed
  4. Radiology. 2010 Feb;254(2):357-66 - PubMed
  5. J Magn Reson Imaging. 2007 Sep;26(3):615-23 - PubMed
  6. Radiology. 2006 May;239(2):361-74 - PubMed
  7. Magn Reson Imaging. 2009 Nov;27(9):1258-70 - PubMed
  8. Eur J Radiol. 2009 Sep;71(3):498-505 - PubMed
  9. J Magn Reson Imaging. 2009 Jun;29(6):1300-7 - PubMed
  10. Eur J Surg Oncol. 2001 Nov;27(7):617-20 - PubMed
  11. EJNMMI Res. 2012 Nov 16;2(1):62 - PubMed
  12. IEEE Trans Med Imaging. 1997 Apr;16(2):187-98 - PubMed
  13. Clin Cancer Res. 2008 Oct 15;14(20):6580-9 - PubMed
  14. Magn Reson Med. 2014 Apr;71(4):1592-602 - PubMed
  15. Semin Oncol. 2011 Feb;38(1):16-25 - PubMed
  16. Genet Epidemiol. 2013 Nov;37(7):704-14 - PubMed
  17. Radiology. 1994 Jun;191(3):633-8 - PubMed
  18. Phys Med Biol. 2011 Sep 7;56(17):5753-69 - PubMed
  19. Acta Radiol. 2010 Jul;51(6):604-12 - PubMed
  20. Magn Reson Imaging. 2007 Jan;25(1):1-13 - PubMed
  21. Radiology. 2011 Jul;260(1):68-78 - PubMed
  22. IEEE Trans Med Imaging. 2003 Nov;22(11):1470-9 - PubMed
  23. Breast J. 2011 Nov-Dec;17(6):610-9 - PubMed
  24. Acta Oncol. 2010 Apr;49(3):354-60 - PubMed
  25. Breast Cancer Res Treat. 2003 Mar;78(1):51-8 - PubMed
  26. Acad Radiol. 2007 May;14(5):561-73 - PubMed
  27. Radiology. 1982 Apr;143(1):29-36 - PubMed
  28. Eur Radiol. 2003 Jan;13(1):80-7 - PubMed
  29. AJR Am J Roentgenol. 1998 Oct;171(4):941-9 - PubMed
  30. Med Phys. 2010 Jun;37(6):2541-52 - PubMed
  31. Cancer. 1996 Jul 1;78(1):91-100 - PubMed
  32. NMR Biomed. 2009 Jan;22(1):104-13 - PubMed
  33. Breast Cancer Res Treat. 2004 Jan;83(1):67-76 - PubMed
  34. Magn Reson Imaging. 2000 Jul;18(6):689-95 - PubMed

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