A Feasibility Study for Microwave Breast Cancer Detection Using Contrast-Agent-Loaded Bacterial Microbots
Bibliographic record
Abstract
We propose a new approach to microwave breast tumor sensing and diagnosis based on the use of biocompatible flagellated magnetotactic bacteria (MTB) adapted to operate in human microvasculature. It has been verified experimentally by Martel et al. that externally generated magnetic gradients could be applied to guide the MTB along preplanned routes inside the human body, and a nanoload could be attached to these bacterial microbots. Motivated by these useful properties, we suggest loading a nanoscale microwave contrast agent such as carbon nanotubes (CNTs) or ferroelectric nanoparticles (FNPs) onto the MTB in order to modify the dielectric properties of tissues near the agent-loaded bacteria. Subsequently, we propose a novel differential microwave imaging (DMI) technique to track simultaneously multiple swarms of MTB microbots injected into the breast. We also present innovative strategies to detect and localize a breast tissue malignancy and estimate its size via this DMI-trackable bacterial microrobotic system. Finally, we use an anatomically realistic numerical breast phantom as a platform to demonstrate the feasibility of this tumor diagnostic method.
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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 | 0.001 | 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".