Evidence map›Paper›PMID 35908096›Full record

ArticleBiomechanics and modeling in mechanobiology2022

Coupling solid and fluid stresses with brain tumour growth and white matter tract deformations in a neuroimaging-informed model.

Giulio Lucci, Abramo Agosti, Pasquale Ciarletta, Chiara Giverso

Open access · hybridAbstract read
In one paragraph

Article in Biomechanics and modeling in mechanobiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.8field-weighted citation impact, top 14% of its field
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

5 citing papers in PubMed, 14 citations in OpenAlex.

  1. Dynamic image-informed selection of biomechanical tumor growth models.Biomechanics and modeling in mechanobiology · 2026
    Article
  2. Review
  3. Article
  4. Article
  5. Mechanical models and measurement methods of solid stress in tumors.Applied microbiology and biotechnology · 2024
    Review
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

4 authors at 3 institutions in 1 country.

Giulio LucciDepartment of Mathematical Sciences "G.L. Lagrange", Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Turin, Italy.
Abramo AgostiDepartment of Mathematics, University of Pavia, Via Ferrata 5, 27100, Pavia, Italy.
Pasquale CiarlettaMOX-Politecnico di Milano, Piazza Leonardo da Vinci 23, 20133, Milan, Italy.
Chiara GiversoDepartment of Mathematical Sciences "G.L. Lagrange", Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Turin, Italy. chiara.giverso@polito.it.ORCID http://orcid.org/0000-0003-2756-5506
Politecnico di Torino · ITPolitecnico di Milano · ITUniversity of Pavia · IT

Funding

Istituto Nazionale di Alta Matematica "Francesco Severi" Progetto Giovani 2020Ministero dell'Istruzione, dell'Università e della Ricerca E11G18000350001Ministero dell'Istruzione, dell'Università e della Ricerca PRIN2017 n. 2017KL4EF3Regione Lombardia POR FES 2014-2020
6 · The paper itself

Abstract

Brain tumours are among the deadliest types of cancer, since they display a strong ability to invade the surrounding tissues and an extensive resistance to common therapeutic treatments. It is therefore important to reproduce the heterogeneity of brain microstructure through mathematical and computational models, that can provide powerful instruments to investigate cancer progression. However, only a few models include a proper mechanical and constitutive description of brain tissue, which instead may be relevant to predict the progression of the pathology and to analyse the reorganization of healthy tissues occurring during tumour growth and, possibly, after surgical resection. Motivated by the need to enrich the description of brain cancer growth through mechanics, in this paper we present a mathematical multiphase model that explicitly includes brain hyperelasticity. We find that our mechanical description allows to evaluate the impact of the growing tumour mass on the surrounding healthy tissue, quantifying the displacements, deformations, and stresses induced by its proliferation. At the same time, the knowledge of the mechanical variables may be used to model the stress-induced inhibition of growth, as well as to properly modify the preferential directions of white matter tracts as a consequence of deformations caused by the tumour. Finally, the simulations of our model are implemented in a personalized framework, which allows to incorporate the realistic brain geometry, the patient-specific diffusion and permeability tensors reconstructed from imaging data and to modify them as a consequence of the mechanical deformation due to cancer growth.

Indexed as

Brain NeoplasmsWhite MatterBrainElasticityFinite Element AnalysisHumansModels, BiologicalNeuroimagingStress, MechanicalBrain tumour growthCancer modellingContinuum MechanicsFinite element methodMixture theoryNonlinear elasticity

Identifiers

PMID35908096
PMCPMC9626445
OpenAlexW4288843720

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.