Evidence map›Paper›PMID 35123136›Full record

ArticleComputers in biology and medicine2022

Automatic framework for patient-specific modelling of tumour resection-induced brain shift.

Yue Yu, Saima Safdar, George Bourantas, Benjamin Zwick, Grand Joldes, Tina Kapur, Sarah Frisken, Ron Kikinis, Arya Nabavi, Alexandra Golby and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Yue YuIntelligent Systems for Medicine Laboratory, The University of Western Australia, Perth 6009, Australia. Electronic address: yue.yu@research.uwa.edu.au.
Saima SafdarIntelligent Systems for Medicine Laboratory, The University of Western Australia, Perth 6009, Australia.
George BourantasIntelligent Systems for Medicine Laboratory, The University of Western Australia, Perth 6009, Australia.
Benjamin ZwickIntelligent Systems for Medicine Laboratory, The University of Western Australia, Perth 6009, Australia.
Grand JoldesIntelligent Systems for Medicine Laboratory, The University of Western Australia, Perth 6009, Australia.
Tina KapurBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Sarah FriskenBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Ron KikinisBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Arya NabaviDepartment of Neurosurgery, KRH Klinikum Nordstadt, Hannover, Germany.
Alexandra GolbyBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Adam WittekIntelligent Systems for Medicine Laboratory, The University of Western Australia, Perth 6009, Australia.
Karol MillerIntelligent Systems for Medicine Laboratory, The University of Western Australia, Perth 6009, Australia; Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Funding

TRD 3 - Enabling Technologies for Intraprocedural GuidanceP41EB028741 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI Nobuhiko Hata, CLARE M TEMPANY · 2021 to 2026
$10.7M
Continuous Compensation of Brain Shift during NeurosurgeryR01EB027134 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI FRISKEN, SARAH · 2018 to 2021
$1.8M
NIBIB NIH HHS P41 EB028741NIBIB NIH HHS R01 EB027134
6 · The paper itself

Abstract

Our motivation is to enable non-biomechanical engineering specialists to use sophisticated biomechanical models in the clinic to predict tumour resection-induced brain shift, and subsequently know the location of the residual tumour and its boundary. To achieve this goal, we developed a framework for automatically generating and solving patient-specific biomechanical models of the brain. This framework automatically determines patient-specific brain geometry from MRI data, generates patient-specific computational grid, assigns material properties, defines boundary conditions, applies external loads to the anatomical structures, and solves differential equations of nonlinear elasticity using Meshless Total Lagrangian Explicit Dynamics (MTLED) algorithm. We demonstrated the effectiveness and appropriateness of our framework on real clinical cases of tumour resection-induced brain shift.

Indexed as

Biomechanical modelBrain shiftMeshless methodsModellingPatient-specific modellingTumour resection

Identifiers

PMID35123136
PMCPMC9389918

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.