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
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9699)

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 pre-surgical 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 3.3 mm between the follow-up scan and the simulation was obtained.

Keywords

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

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Björn Eiben
    • 1
  • Rene Lacher
    • 1
  • Vasileios Vavourakis
    • 1
  • John H. Hipwell
    • 1
  • Danail Stoyanov
    • 1
  • Norman R. Williams
    • 2
  • Jörg Sabczynski
    • 3
  • Thomas Bülow
    • 3
  • Dominik Kutra
    • 3
  • Kirsten Meetz
    • 3
  • Stewart Young
    • 3
  • Hans Barschdorf
    • 3
  • Hélder P. Oliveira
    • 4
  • Jaime S. Cardoso
    • 4
  • João P. Monteiro
    • 4
  • Hooshiar Zolfagharnasab
    • 4
  • Ralph Sinkus
    • 5
  • Pedro Gouveia
    • 6
  • Gerrit-Jan Liefers
    • 7
  • Barbara Molenkamp
    • 7
  • Cornelis J. H. van de Velde
    • 7
  • David J. Hawkes
    • 1
  • Maria João Cardoso
    • 6
  • Mohammed Keshtgar
    • 8
  1. 1.Centre for Medical Image ComputingUniversity College LondonLondonUK
  2. 2.Surgical and Interventional Trials UnitUniversity College LondonLondonUK
  3. 3.Philips Technologie GmbH Innovative TechnologiesHamburgGermany
  4. 4.INESC TECPortoPortugal
  5. 5.Imaging Sciences and Biomedical EngineeringKing’s College LondonLondonUK
  6. 6.Champalimaud FoundationLisbonPortugal
  7. 7.Leiden University Medical CenterLeidenNetherlands
  8. 8.Royal Free HospitalLondonUK

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