Evidence map›Paper›PMID 38236341›Full record

ArticleAnnals of biomedical engineering2024

Non-Fourier Bioheat Transfer Analysis in Brain Tissue During Interstitial Laser Ablation: Analysis of Multiple Influential Factors.

Sundeep Singh, Leonardo Bianchi, Sanzhar Korganbayev, Pouya Namakshenas, Roderick Melnik, Paola Saccomandi

Open access · hybridAbstract read
In one paragraph

Article in Annals of biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.2field-weighted citation impact, top 13% 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

1 citing paper in PubMed, 12 citations in OpenAlex.

  1. 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

6 authors at 3 institutions in 2 countries.

Sundeep SinghFaculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE, C1A 4P3, Canada.
Leonardo BianchiDepartment of Mechanical Engineering, Politecnico di Milano, 20156, Milan, Italy.
Sanzhar KorganbayevDepartment of Mechanical Engineering, Politecnico di Milano, 20156, Milan, Italy.
Pouya NamakshenasDepartment of Mechanical Engineering, Politecnico di Milano, 20156, Milan, Italy.
Roderick MelnikMS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, ON, Canada.
Paola SaccomandiDepartment of Mechanical Engineering, Politecnico di Milano, 20156, Milan, Italy. paola.saccomandi@polimi.it.ORCID http://orcid.org/0000-0003-4236-8033
Politecnico di Milano · ITUniversity of Prince Edward Island · CAWilfrid Laurier University · CA

Funding

Fondazione Cariplo 2017-2075H2020 European Research Council 759159
6 · The paper itself

Abstract

This work presents the dual-phase lag-based non-Fourier bioheat transfer model of brain tissue subjected to interstitial laser ablation. The finite element method has been utilized to predict the brain tissue's temperature distributions and ablation volumes. A sensitivity analysis has been conducted to quantify the effect of variations in the input laser power, treatment time, laser fiber diameter, laser wavelength, and non-Fourier phase lags. Notably, in this work, the temperature-dependent thermal properties of brain tissue have been considered. The developed model has been validated by comparing the temperature obtained from the numerical and ex vivo brain tissue during interstitial laser ablation. The ex vivo brain model has been further extended to in vivo settings by incorporating the blood perfusion effects. The results of the systematic analysis highlight the importance of considering temperature-dependent thermal properties of the brain tissue, non-Fourier behavior, and microvascular perfusion effects in the computational models for accurate predictions of the treatment outcomes during interstitial laser ablation, thereby minimizing the damage to surrounding healthy tissue. The developed model and parametric analysis reported in this study would assist in a more accurate and precise prediction of the temperature distribution, thus allowing to optimize the thermal dosage during laser therapy in the brain.

Indexed as

Hyperthermia, InducedLaser TherapyHot TemperatureLasersModels, BiologicalPerfusionTemperatureBioheat transferBrainLaser ablationMathematical modelingNon-Fourier heat transferThermal therapy

Identifiers

PMID38236341
PMCPMC11252202
OpenAlexW4390986480

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.