Assessment of Breast Composition With a Transmission-Based Microwave Imaging System
Bibliographic record
Abstract
Breast density is a key risk factor for breast cancer, but it is typically unknown before a first mammogram. Microwave imaging, proposed for cancer detection and monitoring, offers potential for measuring the composition of the breast. OBJECTIVE: Assess the potential of microwave imaging as a method for estimating breast composition via correlation with mammogram metrics. METHODS: Transmission based microwave imaging was applied to a cohort of 110 participants with prior mammograms. Several techniques were developed to estimate breast composition from microwave images, including average permittivity calculation, image thresholding and segmentation, and estimation of the fraction of glandular tissue in each pixel. These measures were compared to breast density category and percent density available from mammograms. RESULTS: Average permittivity from microwave images correlated strongly with mammogram-based metrics. For the average permittivity, statistical analysis using one-way ANOVA revealed significant group differences across the various breast density categories. Thresholding and segmentation involved more detailed analysis of the images, and showed potential as alternative approaches to differentiating between breast composition categories. CONCLUSIONS: This study represents the largest cohort of healthy participants in which microwave breast images were compared with breast composition data available from clinical imaging. The cohort is well balanced across all categories. It highlights microwave imaging as a safe, portable, and affordable tool for non-invasive breast composition assessment and early cancer risk detection. SIGNIFICANCE: The correlation between microwave imaging and mammogram-based breast density metrics highlights the potential for microwave imaging as a novel method for assessment of breast composition.
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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 | 0.000 |
| 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 | 0.000 |
| 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".