Electrode Design for Internal Electric Field Delivery to Brain Tumors: Considering Electrical Power and Dynamic Field Shaping
Bibliographic record
Abstract
OBJECTIVE: Brain cancer treatment using low intensity electrotherapy techniques is gaining interest. Localized electric field delivery via an implanted array of electrodes, termed Intratumoral Modulation Therapy (IMT), was found efficacious against brain cancers preclinically. With prior IMT studies supporting the transition towards patient application, we consider optimizing the design of electrodes, such that power consumption is minimized while retaining tumor field coverage and field shaping capability. METHODS: Cylindrical multi-contact electrodes were modelled with variable radius, spacing between contacts and contact length, and applied to spherical tumors ranging from 20-40 mm in diameter. Stimulation programming was optimized and the overall power analyzed for each design such that target coverage was maintained. To investigate the field shaping potential, designs were further optimized on 11 glioma patient MR images with irregular shaped tumors. RESULTS: The IMT electrode parameters found to minimize power consumption were maximal electrode radius (0.8 mm) and minimal contact spacing (1 mm). Analysis of treatment plans on patient images found 4 mm contact length to minimize complexity (total number of contacts) while maintaining field shaping capability. CONCLUSION: In this study, electrodes were designed specifically for IMT that minimized power consumption while maintaining field coverage and shaping. This design was robust in its applicability to patient samples. SIGNIFICANCE: Due to the complexity of dynamic IMT electric field delivery, the established planning system and the custom IMT hardware designed in this study are necessary precursors to human applications. With this work we are one step closer to treating patients with brain cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".