Edge-element based finite element analysis of microwave hyperthermia treatments for superficial tumours on the chest wall
Bibliographic record
Abstract
Several three-dimensional hyperthermia treatment planning systems for deep regional hyperthermia have been successfully utilized for improving the performance of applicators such as the BSD Sigma 60. Treatment planning systems for superficial heating in contrast have been less utilized. This paper presents a study of the applicability of the finite element method that has been developed for modelling hyperthermia treatments of recurrent chest wall cancer using a patient geometry. The patient model was created by reconstructing the tissue geometry of a patient using a series of axial CT scans. Tetrahedral grids were generated from this geometry for use in finite element simulations of the SAR profile using edge-elements and in finite element simulations of the steady-state temperature profile using scalar elements. The predicted temperature profile was well correlated with thermometry readings taken after 30 min of heating during a hyperthermia treatment. The model predicted the presence of hot-spots in regions that were not monitored. Simulations also showed that the hot-spots can be manipulated by rotating the applicator by 90 degrees. This study demonstrates the ability of the model to provide detailed and accurate heating profiles in a patient specific model for superficial microwave hyperthermia of the chest wall.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".