Abstract
A comprehensive framework for predicting response to therapy on the basis of heterogeneity in dceMRI parameter maps is presented. A motion-correction method for dceMRI sequences is extended to incorporate uncertainties in the pharmacokinetic parameter maps using a variational Bayes framework. Simple measures of heterogeneity (with and without uncertainty) in parameter maps for colorectal cancer tumours imaged before therapy are computed, and tested for their ability to distinguish between responders and non-responders to therapy. The statistical analysis demonstrates the importance of using the spatial distribution of parameters, and their uncertainties, when computing heterogeneity measures and using them to predict response on the basis of the pre-therapy scan. The results also demonstrate the benefits of using the ratio of K trans with the bolus arrival time as a biomarker.
Chapter PDF
Similar content being viewed by others
Keywords
- Arterial Input Function
- Heterogeneity Measure
- Bolus Arrival Time
- Vascular Input Function
- Breast Cancer Histopathology
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Zahra, M.A., Hollingsworth, K.G., Sala, E., Lomas, D.J., Tan, L.T.: Dynamic contrast-enhanced MRI as a predictor of tumour response to radiotherapy. The Lancet Oncology 8(1), 63–74 (2007)
He, D., Ma, D., Jin, E.: Dynamic MRI-derived parameters for hot and cold spots: correlation with breast cancer histopathology. Journal of the Balkan Union of Oncology 17(1), 57–64
Loncaster, J.A., Carrington, B.M., Sykes, J.R., Jones, A.P., Todd, S.M., Cooper, R., Buckley, D.L., Davidson, S.E., Logue, J.P., Hunter, R.D., West, C.M.: Prediction of radiotherapy outcome using dynamic contrast enhanced MRI of carcinoma of the cervix. Int. J. Radiat. Oncol. Biol. Phys. 54(3), 759–767 (2002)
Bhushan, M., Schnabel, J.A., Risser, L., Heinrich, M.P., Brady, J.M., Jenkinson, M.: Motion correction and parameter estimation in dceMRI sequences: application to colorectal cancer. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 476–483. Springer, Heidelberg (2011)
Buonaccorsi, G.A., Roberts, C., Cheung, S., Watson, Y., Davies, K., Jackson, A., Jayson, G.C., Parker, G.J.M.: Tracer kinetic model-driven registration for dynamic contrast enhanced {MRI} time series. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 91–98. Springer, Heidelberg (2005)
Yang, X., Knopp, M.V.: Quantifying tumor vascular heterogeneity with dynamic contrast-enhanced magnetic resonance imaging: a review. Journal of Biomed. and Biotech., 732–848 (2011)
O’Connor, J.P.B., Rose, C.J., Jackson, A., Watson, Y., Cheung, S., Maders, F., Whitcher, B.J., Roberts, C., Buonaccorsi, G.A., Thompson, G., Clamp, A.R., Jayson, G.C., Parker, G.J.M.: DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following Bevacizumab and FOLFOX-6. Br. J. Cancer 105, 139–145 (2011)
McPhee, K.C.: Delayed Bolus Arrival Time with High Molecular Weight Contrast Agent, an Indicator of Necrosis. Proc. Intl. Soc. Mag. Reson. Med. 20 (2012)
Garpebring, A., Brynolfsson, P., Yu, J., Wirestam, R., Johansson, A., Asklund, T., Karlsson, M.: Uncertainty estimation in dynamic contrast-enhanced MRI. Magn. Reson. in Med. 2, 1–11 (2012)
Tofts, P.S., Brix, G., Buckley, D.L., Evelhoch, J.L., Henderson, E., Knopp, M.V., Larsson, H.B., Lee, T.Y., Mayr, N.A., Parker, G.J., Port, R.E., Taylor, J., Weisskoff, R.M.: Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J. Magn. Reson. Imaging. 10(3), 223–232 (1999)
Orton, M.R., D’Arcy, J.A., Walker-Samuel, S., Hawkes, D.J., Atkinson, D., Collins, D.J., Leach, M.O.: Computationally efficient vascular input function models for quantitative kinetic modelling using DCE-MRI. Physics in Medicine and Biology 53, 1225–1239 (2008)
Chappell, M., Groves, A., Whitcher, B., Woolrich, M.: Variational Bayesian Inference for a Nonlinear Forward Model. IEEE Transactions on Signal Processing 57, 223–236 (2009)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Symmetric log-domain diffeomorphic registration: A demons-based approach. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 754–761. Springer, Heidelberg (2008)
Bateman, A.C., Jaynes, E., Bateman, A.: Rectal cancer staging post neoadjuvant therapy–how should the changes be assessed? Histopathology 54, 713–721 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bhushan, M. et al. (2013). The Impact of Heterogeneity and Uncertainty on Prediction of Response to Therapy Using Dynamic MRI Data. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol 8149. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40811-3_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-40811-3_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40810-6
Online ISBN: 978-3-642-40811-3
eBook Packages: Computer ScienceComputer Science (R0)