Breast conserving surgery outcome prediction: A patient-specific, integrated multi-modal imaging and mechano-biological modelling framework

Björn Eiben; Rene Lacher; Vasileios Vavourakis; John H. Hipwell; Danail Stoyanov; Norman R. Williams; Jörg Sabczynski; Thomas Bülow; Dominik Kutra; Kirsten Meetz; Stewart Young; Hans Barschdorf; Hélder P. Oliveira; Jaime S. Cardoso; João P. Monteiro; Hooshiar Zolfagharnasab; Ralph Sinkus; Pedro Gouveia; Gerrit Jan Liefers; Barbara Molenkamp; Cornelis J H van de Velde; David J. Hawkes; Maria João Cardoso; Mohammed Keshtgar

doi:10.1007/978-3-319-41546-8_35

Breast conserving surgery outcome prediction: A patient-specific, integrated multi-modal imaging and mechano-biological modelling framework

Björn Eiben^*, Rene Lacher, Vasileios Vavourakis, John H. Hipwell, Danail Stoyanov, Norman R. Williams, Jörg Sabczynski, Thomas Bülow, Dominik Kutra, Kirsten Meetz, Stewart Young, Hans Barschdorf, Hélder P. Oliveira, Jaime S. Cardoso, João P. Monteiro, Hooshiar Zolfagharnasab, Ralph Sinkus, Pedro Gouveia, Gerrit Jan Liefers, Barbara MolenkampCornelis J H van de Velde, David J. Hawkes, Maria João Cardoso, Mohammed Keshtgar

^*Corresponding author for this work

Biomedical Engineering Department

Research output: Chapter in Book/Report/Conference proceeding › Chapter

4 Citations (Scopus)

Abstract

Patient-specific surgical predictions of Breast Conserving Therapy, through mechano-biological simulations, could inform the shared decision making process between clinicians and patients by enabling the impact of different surgical options to be visualised. We present an overview of our processing workflow that integrates MR images and three dimensional optical surface scans into a personalised model. Utilising an interactively generated surgical plan, a multi-scale open source finite element solver is employed to simulate breast deformity based on interrelated physiological and biomechanical processes that occur post surgery. Our outcome predictions, based on the presurgical imaging, were validated by comparing the simulated outcome with follow-up surface scans of four patients acquired 6 to 12 months post-surgery. A mean absolute surface distance of 3.3mm between the follow-up scan and the simulation was obtained.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer‐Verlag Berlin Heidelberg
Pages	274-281
Number of pages	8
Volume	9699
ISBN (Print)	9783319415451
DOIs	https://doi.org/10.1007/978-3-319-41546-8_35
Publication status	Published - 17 Jun 2016
Event	13th International Workshop on Breast Imaging, IWDM 2016 - Malmo, Sweden Duration: 19 Jun 2016 → 22 Jun 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9699
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	13th International Workshop on Breast Imaging, IWDM 2016
Country/Territory	Sweden
City	Malmo
Period	19/06/2016 → 22/06/2016

Keywords

Breast imaging
Finite element
Image registration
Mathematical modelling
Oncoplastic breast surgery
Surface reconstruction
Surgical planning

Access to Document

10.1007/978-3-319-41546-8_35

Cite this

Eiben, B., Lacher, R., Vavourakis, V., Hipwell, J. H., Stoyanov, D., Williams, N. R., Sabczynski, J., Bülow, T., Kutra, D., Meetz, K., Young, S., Barschdorf, H., Oliveira, H. P., Cardoso, J. S., Monteiro, J. P., Zolfagharnasab, H., Sinkus, R., Gouveia, P., Liefers, G. J., ... Keshtgar, M. (2016). Breast conserving surgery outcome prediction: A patient-specific, integrated multi-modal imaging and mechano-biological modelling framework. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9699, pp. 274-281). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9699). Springer‐Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-319-41546-8_35

Eiben, Björn ; Lacher, Rene ; Vavourakis, Vasileios et al. / Breast conserving surgery outcome prediction : A patient-specific, integrated multi-modal imaging and mechano-biological modelling framework. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9699 Springer‐Verlag Berlin Heidelberg, 2016. pp. 274-281 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Breast conserving surgery outcome prediction: A patient-specific, integrated multi-modal imaging and mechano-biological modelling framework",

abstract = "Patient-specific surgical predictions of Breast Conserving Therapy, through mechano-biological simulations, could inform the shared decision making process between clinicians and patients by enabling the impact of different surgical options to be visualised. We present an overview of our processing workflow that integrates MR images and three dimensional optical surface scans into a personalised model. Utilising an interactively generated surgical plan, a multi-scale open source finite element solver is employed to simulate breast deformity based on interrelated physiological and biomechanical processes that occur post surgery. Our outcome predictions, based on the presurgical imaging, were validated by comparing the simulated outcome with follow-up surface scans of four patients acquired 6 to 12 months post-surgery. A mean absolute surface distance of 3.3mm between the follow-up scan and the simulation was obtained.",

keywords = "Breast imaging, Finite element, Image registration, Mathematical modelling, Oncoplastic breast surgery, Surface reconstruction, Surgical planning",

author = "Bj{\"o}rn Eiben and Rene Lacher and Vasileios Vavourakis and Hipwell, {John H.} and Danail Stoyanov and Williams, {Norman R.} and J{\"o}rg Sabczynski and Thomas B{\"u}low and Dominik Kutra and Kirsten Meetz and Stewart Young and Hans Barschdorf and Oliveira, {H{\'e}lder P.} and Cardoso, {Jaime S.} and Monteiro, {Jo{\~a}o P.} and Hooshiar Zolfagharnasab and Ralph Sinkus and Pedro Gouveia and Liefers, {Gerrit Jan} and Barbara Molenkamp and {van de Velde}, {Cornelis J H} and Hawkes, {David J.} and Cardoso, {Maria Jo{\~a}o} and Mohammed Keshtgar",

year = "2016",

month = jun,

day = "17",

doi = "10.1007/978-3-319-41546-8_35",

language = "English",

isbn = "9783319415451",

volume = "9699",

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

publisher = "Springer‐Verlag Berlin Heidelberg",

pages = "274--281",

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

note = "13th International Workshop on Breast Imaging, IWDM 2016 ; Conference date: 19-06-2016 Through 22-06-2016",

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Eiben, B, Lacher, R, Vavourakis, V, Hipwell, JH, Stoyanov, D, Williams, NR, Sabczynski, J, Bülow, T, Kutra, D, Meetz, K, Young, S, Barschdorf, H, Oliveira, HP, Cardoso, JS, Monteiro, JP, Zolfagharnasab, H, Sinkus, R, Gouveia, P, Liefers, GJ, Molenkamp, B, van de Velde, CJH, Hawkes, DJ, Cardoso, MJ & Keshtgar, M 2016, Breast conserving surgery outcome prediction: A patient-specific, integrated multi-modal imaging and mechano-biological modelling framework. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9699, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9699, Springer‐Verlag Berlin Heidelberg, pp. 274-281, 13th International Workshop on Breast Imaging, IWDM 2016, Malmo, Sweden, 19/06/2016. https://doi.org/10.1007/978-3-319-41546-8_35

Breast conserving surgery outcome prediction: A patient-specific, integrated multi-modal imaging and mechano-biological modelling framework. / Eiben, Björn; Lacher, Rene; Vavourakis, Vasileios et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9699 Springer‐Verlag Berlin Heidelberg, 2016. p. 274-281 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9699).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - Breast conserving surgery outcome prediction

T2 - 13th International Workshop on Breast Imaging, IWDM 2016

AU - Eiben, Björn

AU - Lacher, Rene

AU - Vavourakis, Vasileios

AU - Hipwell, John H.

AU - Stoyanov, Danail

AU - Williams, Norman R.

AU - Sabczynski, Jörg

AU - Bülow, Thomas

AU - Kutra, Dominik

AU - Meetz, Kirsten

AU - Young, Stewart

AU - Barschdorf, Hans

AU - Oliveira, Hélder P.

AU - Cardoso, Jaime S.

AU - Monteiro, João P.

AU - Zolfagharnasab, Hooshiar

AU - Sinkus, Ralph

AU - Gouveia, Pedro

AU - Liefers, Gerrit Jan

AU - Molenkamp, Barbara

AU - van de Velde, Cornelis J H

AU - Hawkes, David J.

AU - Cardoso, Maria João

AU - Keshtgar, Mohammed

PY - 2016/6/17

Y1 - 2016/6/17

N2 - Patient-specific surgical predictions of Breast Conserving Therapy, through mechano-biological simulations, could inform the shared decision making process between clinicians and patients by enabling the impact of different surgical options to be visualised. We present an overview of our processing workflow that integrates MR images and three dimensional optical surface scans into a personalised model. Utilising an interactively generated surgical plan, a multi-scale open source finite element solver is employed to simulate breast deformity based on interrelated physiological and biomechanical processes that occur post surgery. Our outcome predictions, based on the presurgical imaging, were validated by comparing the simulated outcome with follow-up surface scans of four patients acquired 6 to 12 months post-surgery. A mean absolute surface distance of 3.3mm between the follow-up scan and the simulation was obtained.

AB - Patient-specific surgical predictions of Breast Conserving Therapy, through mechano-biological simulations, could inform the shared decision making process between clinicians and patients by enabling the impact of different surgical options to be visualised. We present an overview of our processing workflow that integrates MR images and three dimensional optical surface scans into a personalised model. Utilising an interactively generated surgical plan, a multi-scale open source finite element solver is employed to simulate breast deformity based on interrelated physiological and biomechanical processes that occur post surgery. Our outcome predictions, based on the presurgical imaging, were validated by comparing the simulated outcome with follow-up surface scans of four patients acquired 6 to 12 months post-surgery. A mean absolute surface distance of 3.3mm between the follow-up scan and the simulation was obtained.

KW - Breast imaging

KW - Finite element

KW - Image registration

KW - Mathematical modelling

KW - Oncoplastic breast surgery

KW - Surface reconstruction

KW - Surgical planning

UR - http://www.scopus.com/inward/record.url?scp=84977528743&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-41546-8_35

DO - 10.1007/978-3-319-41546-8_35

M3 - Chapter

AN - SCOPUS:84977528743

SN - 9783319415451

VL - 9699

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

SP - 274

EP - 281

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

PB - Springer‐Verlag Berlin Heidelberg

Y2 - 19 June 2016 through 22 June 2016

ER -

Eiben B, Lacher R, Vavourakis V, Hipwell JH, Stoyanov D, Williams NR et al. Breast conserving surgery outcome prediction: A patient-specific, integrated multi-modal imaging and mechano-biological modelling framework. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9699. Springer‐Verlag Berlin Heidelberg. 2016. p. 274-281. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-41546-8_35

Breast conserving surgery outcome prediction: A patient-specific, integrated multi-modal imaging and mechano-biological modelling framework

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Keywords

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