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Record W2980544438 · doi:10.1109/tbme.2019.2947292

A Novel Method to Increase Tumor Ablation Zones With RFA by Injecting the Cationic Polymer Solution to Tissues: <i>In Vivo</i> and Computational Studies

2019· article· en· W2980544438 on OpenAlexaff
Zheng Fang, Mike Moser, Edwin Zhang, Wenjun Zhang, Bing Zhang

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2019
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsUniversity of TorontoUniversity of Saskatchewan
FundersHigher Education Discipline Innovation ProjectNational Natural Science Foundation of ChinaNatural Science Foundation of Shanghai
KeywordsIn vivoCationic polymerizationAblationPolymerBiomedical engineeringAblation zoneMaterials scienceChemistryRadiofrequency ablationNuclear chemistryPolymer chemistryMedicineOrganic chemistryInternal medicineBiology

Abstract

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Objective: This study aims to examine, for the first time, the introduction of cationic polymer solutions to improve radiofrequency ablation (RFA) in terms of a potentially enlarged ablation zone. Methods: By using in vivo and computational RFA studies, two cationic polymers, Chitooligosaccharides (COS) and carboxymethyl chitosan (CMC), diluted in deionized water, were injected into tissues separately surrounding the RF bipolar electrode prior to power application. A total of 9 rabbits were used to 1) measure the increase in electrical conductivity of tissues injected with the cationic polymer solutions, and 2) explore the enhancement of the ablation performance in RFA trials. A computer model of RFA comprising a model of the solution diffusion with an RF thermal ablation model was also built, validated by the in vivo experiment, to quantitatively study the effect of cationic polymer solutions on ablation performances. Results: Compared to the control group, the electrical conductivity of rabbit liver tissues was increased by 42.20% (0.282 ± 0.006 vs. 0.401 ± 0.048 S/m, P = 0.001) and 43.97% (0.282 ± 0.006 vs. 0.406 ± 0.042 S/m, P = 0.001) by injecting the COS and CMC solution at the concentration of 100 mg/mL into the tissues, denoted COSDW100 and CMCDW100, respectively. Consequently, the in vivo experiments show that the ablation zone was enlarged by 95% (47.6 ± 6.3 vs. 92.6 ± 11.5 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , P <; 0.001) and 87% (47.6± 6.3 vs. 88.8 ± 9.6 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , P <; 0.001) by COSDW100 and CMCDW100, respectively. The computer simulation shows that the ablation zone was enlarged by 71% (51.9 vs. 88.7 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and 63% (51.9 vs. 84.7 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) by COSDW100 and CMCDW100, respectively. Conclusion: The injection of the cationic solution can greatly improve the performance of RFA treatment in terms of enlarging the ablation zone, which is due to the increase in the electrical conductivity of liver tissues surrounding the RF electrode. Significance: This study contributes to the improvement of RFA in the treatment of large tumors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.748
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.266
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2019
Admission routes1
Has abstractyes

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