3D bioheat transfer mapping reveals nanomagnetic particles effectiveness in radiofrequency hyperthermia breast cancer treatment comparing to experimental study
Bibliographic record
Abstract
Radiofrequency (RF) hyperthermia has been widely used for tumor ablation since magnetic-fluid-hyperthermia (MFH) can be utilized for increasing temperature in tumor-region as a complementary-method for hyperthermia. In this study, the effectiveness of using the magnetite-nanoparticles (Fe 3 O 4 ) in RF hyperthermia for breast cancer (BC) treatment by determining 3D-temperature-distribution using bioheat-transfer-mapping was evaluated. A breast-phantom with a tumor region was placed in an RF-device with 13.56 MHz frequency in different states (with and without-nanomagnetite). Parallelly, the calculations of the RF-wave and bioheat-equation were accomplished by numerical-simulation and finite-element-method (FEM) in COMSOL-software. The temperature differences were experimentally measured at different points of the phantom with a precision of 0.1 °C, with temperature of 3.6 °C and 6.1 °C in without and with nanomagnetic conditions in tumor area, respectively, and also for normal area with temperature of 1.8 °C and 1.9 °C in non-presence and presence states of 0.05 gr magnetite for both conditions, respectively. Moreover, the difference between the simulation and the experimental results was 0.54–1.1 %. The conformity between temperature measurement in experimental and simulation studies in tumor and normal areas showed the effectiveness of the application of MNPs for RF hyperthermia in tissue equivalent breast phantom. Finally, the positive effect of 0.05 gr of MNPs on BC treatment was confirmed.
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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 | 0.000 |
| 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) | 0.002 | 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".