Temperature Distribution of Radiofrequency Hyperthermia in a Capacitance System in Breast Equivalent Tumor Ablation: A Simulation Study
Bibliographic record
Abstract
Introduction: For decades, hyperthermia had been widely used for tumor ablation by increasing the temperature of cancerous tissues. For clinical treatment, a capacitance system was developed around the world. In this study, a capacitance system of radiofrequency (RF) hyperthermia was simulated to achieve the temperature distribution map of the entire breast equivalent phantom. Therefore, the efficiency of this method in the treatment of breast cancer was investigated in the current study. Material and Methods: In this study, an RF system with a frequency of 13.56 MHz was simulated by Comsol Multiphysics software (Version 5.3). The geometry of the breast cancerous tissue was modeled by the consideration of three different tissues, including the fat, gland, and tumor tissues. The two electrodes of the system were modeled as two disks with a radius of 15 cm. The calculations of the RF wave and bioheat equation were accomplished by numerical simulation and finite element method. Results: The temperature plots were obtained in 5 min. The temperature distribution map was entirely achieved and the results were compared with experimental findings to check the accuracy of the RF device and precision of the thermometer. Conclusion: The obtained results showed that the temperature of the whole tumor region increased uniformly (3-4˚C). Moreover, the temperature of the whole healthy tissues (i.e., the gland and fat tissues) did not increase (1.9-2.1˚C). Consequently, in the capacitive hyperthermia system, the tumor reached extreme heat; however, the healthy tissues were completely protected from damages.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 source (direct Gemma or distilled Codex), 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".