An Early Clinical Study of Time-Domain Microwave Radar for Breast Health Monitoring
Bibliographic record
Abstract
This study reports on monthly scans of healthy patient volunteers with the clinical prototype of a microwave imaging system. The system uses time-domain measurements, and incorporates a multistatic radar approach to imaging. It operates in the 2-4 GHz range and contains 16 wideband sensors embedded in a hemispherical dielectric radome. The system has been previously tested on tissue phantoms in controlled experiments. With this system prototype, we scanned 13 patients (26 breasts) over an eight-month period, collecting a total of 342 breast scans. The goal of the study described in this paper was to investigate how the system measurements are impacted by multiple factors that are unavoidable in monthly monitoring of human subjects. These factors include both biological variability (e.g., tissue variations due to hormonal changes or weight gain) and measurement variability (e.g., inconsistencies in patient positioning, system noise). For each patient breast, we process the results of the monthly scans to assess the variability in both the raw measured signals and in the generated images. The significance of this study is that it quantifies how much variability should be anticipated when conducting microwave breast imaging of a healthy patient over a longer period. This is an important step toward establishing the feasibility of the microwave radar imaging system for frequent monitoring of breast health.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".