Use of microwave ablation for thermal treatment of solid tumors with different shapes and sizes—A computational approach
Bibliographic record
Abstract
Microwave Ablation (MWA) is one of the most recent developments in the field of thermal therapy. This approach is an effective method for thermal tumor ablation by increasing the temperature above the normal physiological threshold to kill cancer cells with minimum side effects to surrounding organs due to rapid heat dispersive tissues. In the present study, the effects of the shape and size of the tumor on MWA are investigated. To obtain the temperature gradient, coupled bio-heat and electromagnetic equations are solved using a three-dimensional finite element method (FEM). To extract cellular response at different temperatures and times, the three-state mathematical model was employed to achieve the ablation zone size. Results show that treatment of larger tumors is more difficult than that of smaller ones. By doubling the diameter of the tumor, the percentage of dead cancer cells is reduced by 20%. For a spherical tumor smaller than 2 cm, applying 50 W input power compared to 25 W has no significant effects on treatment efficiency and only increases the risk of damage to adjacent tissues. However, for tumors larger than 2 cm, it can increase the ablation zone up to 21%. In the spherical and oblate tumors, the mean percentage of dead cells at 6 GHz is nearly 30% higher than that at 2.45GHz, but for prolate tumors, treatment efficacy is reduced by 10% at a higher frequency. Moreover, the distance between two slots in the coaxial double slot antenna is modified based on the best treatment of prolate tumors. The findings of this study can be used to choose the optimum frequency and the best antenna design according to the shape and size of the tumor.
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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 | 0.000 |
| 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 | 0.000 | 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 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".