Deep 3D Dose Analysis for Prediction of Outcomes After Liver Stereotactic Body Radiation Therapy

Bulat Ibragimov; Diego A. S. Toesca; Yixuan Yuan; Albert C. Koong; Daniel T. Chang; Lei Xing

doi:10.1007/978-3-030-00934-2_76

Deep 3D Dose Analysis for Prediction of Outcomes After Liver Stereotactic Body Radiation Therapy

Bulat Ibragimov^*, Diego A. S. Toesca, Yixuan Yuan, Albert C. Koong, Daniel T. Chang, Lei Xing

^*Corresponding author for this work

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

3 Citations (Scopus)

Abstract

Accurate and precise dose delivery is the key factor for radiation therapy (RT) success. Currently, RT planning is based on optimization of oversimplified dose-volume metrics that consider all human organs to be homogeneous. The limitations of such an approach result in suboptimal treatments with poor outcomes: short survival, early cancer recurrence and radiation-induced toxicities of healthy organs. This paper pioneers the concept of deep 3D dose analysis for outcome prediction after liver stereotactic body RT (SBRT). The presented work develops tools for unification of dose plans into the same anatomy space, classifies dose plan using convolutional neural networks with transfer learning form anatomy images, and assembles the first volumetric liver atlas of the critical-to-spare liver regions. The concept is validated on prediction of post-SBRT survival and local cancer progression using a clinical database of primary and metastatic liver SBRTs. The risks of negative SBRT outcomes are quantitatively estimated for individual liver segments.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
Subtitle of host publication	21st International Conference, 2018, Proceedings
Editors	Alejandro F. Frangi, Julia A. Schnabel, Christos Davatzikos, Carlos Alberola-López
Publisher	Springer, Cham
Pages	684-692
Volume	2
ISBN (Electronic)	978-3-030-00934-2
ISBN (Print)	978-3-030-00933-5
DOIs	https://doi.org/10.1007/978-3-030-00934-2_76
Publication status	Published - Sept 2018
Event	21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018) - Granada, Spain Duration: 16 Sept 2018 → 20 Sept 2018 https://www.miccai2018.org/en/

Publication series

Name	Lecture Notes in Computer Science (including subseries Image Processing, Computer Vision, Pattern Recognition, and Graphics)
Publisher	Springer, Cham
Volume	LNCS 11071
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018)
Place	Spain
City	Granada
Period	16/09/18 → 20/09/18
Internet address	https://www.miccai2018.org/en/

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

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Ibragimov, B., Toesca, D. A. S., Yuan, Y., Koong, A. C., Chang, D. T., & Xing, L. (2018). Deep 3D Dose Analysis for Prediction of Outcomes After Liver Stereotactic Body Radiation Therapy. In A. F. Frangi, J. A. Schnabel, C. Davatzikos, & C. Alberola-López (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, 2018, Proceedings (Vol. 2, pp. 684-692). (Lecture Notes in Computer Science (including subseries Image Processing, Computer Vision, Pattern Recognition, and Graphics); Vol. LNCS 11071). Springer, Cham. https://doi.org/10.1007/978-3-030-00934-2_76

Ibragimov, Bulat ; Toesca, Diego A. S. ; Yuan, Yixuan et al. / Deep 3D Dose Analysis for Prediction of Outcomes After Liver Stereotactic Body Radiation Therapy. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, 2018, Proceedings. editor / Alejandro F. Frangi ; Julia A. Schnabel ; Christos Davatzikos ; Carlos Alberola-López. Vol. 2 Springer, Cham, 2018. pp. 684-692 (Lecture Notes in Computer Science (including subseries Image Processing, Computer Vision, Pattern Recognition, and Graphics)).

@inproceedings{a9116c0497e948969f295f670b02c6f0,

title = "Deep 3D Dose Analysis for Prediction of Outcomes After Liver Stereotactic Body Radiation Therapy",

abstract = "Accurate and precise dose delivery is the key factor for radiation therapy (RT) success. Currently, RT planning is based on optimization of oversimplified dose-volume metrics that consider all human organs to be homogeneous. The limitations of such an approach result in suboptimal treatments with poor outcomes: short survival, early cancer recurrence and radiation-induced toxicities of healthy organs. This paper pioneers the concept of deep 3D dose analysis for outcome prediction after liver stereotactic body RT (SBRT). The presented work develops tools for unification of dose plans into the same anatomy space, classifies dose plan using convolutional neural networks with transfer learning form anatomy images, and assembles the first volumetric liver atlas of the critical-to-spare liver regions. The concept is validated on prediction of post-SBRT survival and local cancer progression using a clinical database of primary and metastatic liver SBRTs. The risks of negative SBRT outcomes are quantitatively estimated for individual liver segments.",

author = "Bulat Ibragimov and Toesca, {Diego A. S.} and Yixuan Yuan and Koong, {Albert C.} and Chang, {Daniel T.} and Lei Xing",

year = "2018",

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series = "Lecture Notes in Computer Science (including subseries Image Processing, Computer Vision, Pattern Recognition, and Graphics)",

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editor = "Frangi, {Alejandro F.} and Schnabel, {Julia A.} and Christos Davatzikos and Carlos Alberola-L{\'o}pez",

booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2018",

note = "21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018) ; Conference date: 16-09-2018 Through 20-09-2018",

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Ibragimov, B, Toesca, DAS, Yuan, Y, Koong, AC, Chang, DT & Xing, L 2018, Deep 3D Dose Analysis for Prediction of Outcomes After Liver Stereotactic Body Radiation Therapy. in AF Frangi, JA Schnabel, C Davatzikos & C Alberola-López (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, 2018, Proceedings. vol. 2, Lecture Notes in Computer Science (including subseries Image Processing, Computer Vision, Pattern Recognition, and Graphics), vol. LNCS 11071, Springer, Cham, pp. 684-692, 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018), Granada, Spain, 16/09/18. https://doi.org/10.1007/978-3-030-00934-2_76

Deep 3D Dose Analysis for Prediction of Outcomes After Liver Stereotactic Body Radiation Therapy. / Ibragimov, Bulat; Toesca, Diego A. S.; Yuan, Yixuan et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, 2018, Proceedings. ed. / Alejandro F. Frangi; Julia A. Schnabel; Christos Davatzikos; Carlos Alberola-López. Vol. 2 Springer, Cham, 2018. p. 684-692 (Lecture Notes in Computer Science (including subseries Image Processing, Computer Vision, Pattern Recognition, and Graphics); Vol. LNCS 11071).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Deep 3D Dose Analysis for Prediction of Outcomes After Liver Stereotactic Body Radiation Therapy

AU - Ibragimov, Bulat

AU - Toesca, Diego A. S.

AU - Yuan, Yixuan

AU - Koong, Albert C.

AU - Chang, Daniel T.

AU - Xing, Lei

PY - 2018/9

Y1 - 2018/9

N2 - Accurate and precise dose delivery is the key factor for radiation therapy (RT) success. Currently, RT planning is based on optimization of oversimplified dose-volume metrics that consider all human organs to be homogeneous. The limitations of such an approach result in suboptimal treatments with poor outcomes: short survival, early cancer recurrence and radiation-induced toxicities of healthy organs. This paper pioneers the concept of deep 3D dose analysis for outcome prediction after liver stereotactic body RT (SBRT). The presented work develops tools for unification of dose plans into the same anatomy space, classifies dose plan using convolutional neural networks with transfer learning form anatomy images, and assembles the first volumetric liver atlas of the critical-to-spare liver regions. The concept is validated on prediction of post-SBRT survival and local cancer progression using a clinical database of primary and metastatic liver SBRTs. The risks of negative SBRT outcomes are quantitatively estimated for individual liver segments.

AB - Accurate and precise dose delivery is the key factor for radiation therapy (RT) success. Currently, RT planning is based on optimization of oversimplified dose-volume metrics that consider all human organs to be homogeneous. The limitations of such an approach result in suboptimal treatments with poor outcomes: short survival, early cancer recurrence and radiation-induced toxicities of healthy organs. This paper pioneers the concept of deep 3D dose analysis for outcome prediction after liver stereotactic body RT (SBRT). The presented work develops tools for unification of dose plans into the same anatomy space, classifies dose plan using convolutional neural networks with transfer learning form anatomy images, and assembles the first volumetric liver atlas of the critical-to-spare liver regions. The concept is validated on prediction of post-SBRT survival and local cancer progression using a clinical database of primary and metastatic liver SBRTs. The risks of negative SBRT outcomes are quantitatively estimated for individual liver segments.

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DO - 10.1007/978-3-030-00934-2_76

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-3-030-00933-5

VL - 2

T3 - Lecture Notes in Computer Science (including subseries Image Processing, Computer Vision, Pattern Recognition, and Graphics)

SP - 684

EP - 692

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

A2 - Frangi, Alejandro F.

A2 - Schnabel, Julia A.

A2 - Davatzikos, Christos

A2 - Alberola-López, Carlos

PB - Springer, Cham

T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018)

Y2 - 16 September 2018 through 20 September 2018

ER -

Ibragimov B, Toesca DAS, Yuan Y, Koong AC, Chang DT, Xing L. Deep 3D Dose Analysis for Prediction of Outcomes After Liver Stereotactic Body Radiation Therapy. In Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, 2018, Proceedings. Vol. 2. Springer, Cham. 2018. p. 684-692. (Lecture Notes in Computer Science (including subseries Image Processing, Computer Vision, Pattern Recognition, and Graphics)). doi: 10.1007/978-3-030-00934-2_76

Deep 3D Dose Analysis for Prediction of Outcomes After Liver Stereotactic Body Radiation Therapy

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