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Record W4393165110 · doi:10.1088/1361-6560/ad3795

Development of non-invasive flexible directional microwave ablation for central lung cancer: a simulation study

2024· article· en· W4393165110 on OpenAlexaff
Zheng Fang, Wu Chen, Lin Cao, Tao Wang, Xiaowu Hong, Mike Moser, Wenjun Zhang, Bing Zhang

Bibliographic record

VenuePhysics in Medicine and Biology · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGyrotron and Vacuum Electronics Research
Canadian institutionsUniversity of Saskatchewan
FundersNational Natural Science Foundation of China
KeywordsMicrowave ablationAntenna (radio)Lung cancerAblationMedicineStage (stratigraphy)Biomedical engineeringComputer scienceTelecommunicationsInternal medicineBiology

Abstract

fetched live from OpenAlex

Transbronchial microwave ablation (MWA) with flexible antennas has gradually become an attractive alternative to percutaneous MWA for lung cancer due to its characteristic of non-invasiveness. However, flexible antennas for the precision ablation of lung tumors that are adjacent to critical bronchial structures are still not available. In this study, a non-invasive flexible directional (FD) antenna for early stage central lung tumors surrounding the bronchia was proposed. A comprehensive numerical MWA model with the FD antenna was developed in a real human-sized left lung model. The structure of the antenna and the treatment protocol were optimized by a generic algorithm for the precision ablation of two cases of early stage central lung cancer (i.e. spherical-like and ellipsoidal tumors). The electromagnetic efficiency of the optimized antenna was also improved by implementing an optimizedπ-matching network for impedance matching. The results indicate that the electromagnetic energy of MWA can be restricted to a particular area for precision ablation of specific lung tumors using the FD antenna. This study contributes to the field of lung cancer management with MWA.

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: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.159
GPT teacher head0.466
Teacher spread0.307 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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