The impact of heterogeneity and uncertainty on prediction of response to therapy using dynamic MRI data

Manav Bhushan; Julia A. Schnabel; Michael Chappell; Fergus Gleeson; Mark Anderson; Jamie Franklin; Sir Michael Brady; Mark Jenkinson

doi:10.1007/978-3-642-40811-3_40

The impact of heterogeneity and uncertainty on prediction of response to therapy using dynamic MRI data

Manav Bhushan, Julia A. Schnabel, Michael Chappell, Fergus Gleeson, Mark Anderson, Jamie Franklin, Sir Michael Brady, Mark Jenkinson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

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.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Pages	316-323
Number of pages	8
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-40811-3_40
Publication status	Published or Issued - 2013
Externally published	Yes
Event	16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan Duration: 22 Sept 2013 → 26 Sept 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	8149 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Country/Territory	Japan
City	Nagoya
Period	22/09/13 → 26/09/13

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

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10.1007/978-3-642-40811-3_40

Cite this

Bhushan, M., Schnabel, J. A., Chappell, M., Gleeson, F., Anderson, M., Franklin, J., Brady, S. M., & Jenkinson, M. (2013). The impact of heterogeneity and uncertainty on prediction of response to therapy using dynamic MRI data. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings (PART 1 ed., pp. 316-323). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8149 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-40811-3_40

Bhushan, Manav ; Schnabel, Julia A. ; Chappell, Michael et al. / The impact of heterogeneity and uncertainty on prediction of response to therapy using dynamic MRI data. Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 1. ed. 2013. pp. 316-323 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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title = "The impact of heterogeneity and uncertainty on prediction of response to therapy using dynamic MRI data",

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 Ktrans with the bolus arrival time as a biomarker.",

author = "Manav Bhushan and Schnabel, {Julia A.} and Michael Chappell and Fergus Gleeson and Mark Anderson and Jamie Franklin and Brady, {Sir Michael} and Mark Jenkinson",

year = "2013",

doi = "10.1007/978-3-642-40811-3_40",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Bhushan, M, Schnabel, JA, Chappell, M, Gleeson, F, Anderson, M, Franklin, J, Brady, SM & Jenkinson, M 2013, The impact of heterogeneity and uncertainty on prediction of response to therapy using dynamic MRI data. in Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 8149 LNCS, pp. 316-323, 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013, Nagoya, Japan, 22/09/13. https://doi.org/10.1007/978-3-642-40811-3_40

The impact of heterogeneity and uncertainty on prediction of response to therapy using dynamic MRI data. / Bhushan, Manav; Schnabel, Julia A.; Chappell, Michael et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 1. ed. 2013. p. 316-323 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8149 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Bhushan, Manav

AU - Schnabel, Julia A.

AU - Chappell, Michael

AU - Gleeson, Fergus

AU - Anderson, Mark

AU - Franklin, Jamie

AU - Brady, Sir Michael

AU - Jenkinson, Mark

PY - 2013

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AB - 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 Ktrans with the bolus arrival time as a biomarker.

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Bhushan M, Schnabel JA, Chappell M, Gleeson F, Anderson M, Franklin J et al. The impact of heterogeneity and uncertainty on prediction of response to therapy using dynamic MRI data. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings. PART 1 ed. 2013. p. 316-323. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-40811-3_40

The impact of heterogeneity and uncertainty on prediction of response to therapy using dynamic MRI data

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