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

University of Oxford
University of Oxford Medical Sciences Division
Churchill Hospital

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

3 Scopus citations

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
State	Published - 2013
Externally published	Yes
Event	16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan Duration: 22 Sep 2013 → 26 Sep 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

Conference

Conference	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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

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",

language = "English",

isbn = "9783642408106",

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

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booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings",

edition = "PART 1",

note = "16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 ; Conference date: 22-09-2013 Through 26-09-2013",

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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

TY - GEN

T1 - The impact of heterogeneity and uncertainty on prediction of response to therapy using dynamic MRI data

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

Y1 - 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.

UR - https://www.scopus.com/pages/publications/84885744735

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M3 - Conference contribution

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AN - SCOPUS:84885744735

SN - 9783642408106

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Y2 - 22 September 2013 through 26 September 2013

ER -

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

Abstract

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Conference

UN SDGs

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