Reproducibility of Microwave Breast Imaging: Analysis of Regular Scans of a Group of Volunteers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Microwave imaging has been proposed for breast cancer detection and treatment monitoring. The introduction of new approaches or next-generation prototype systems requires characterization of expected variability when scanning participants over clinically relevant timeframes. OBJECTIVE: The objective of this study is to quantify the reliability and variability in scans of a group of 35 volunteers collected at multiple time points with a next-generation microwave imaging system. METHODS: Multiple scans of the same volunteer are collected both during one visit and over multiple visits. Test-retest reliability and variability in measurements are investigated for the average permittivity of breast tissues. RESULTS: The average permittivity for each volunteer exhibited similarity during and between sessions, with some variation noted for smaller breast sizes. The average properties of right and left breasts were also similar. Reliability was demonstrated with the intra-class correlation coefficient (ICC) values statistically greater than 0.9 both within and between sessions. Variability of the measurement was typically less than one unit and coefficient of variation less than 5% (within sessions) or 6% (between sessions). CONCLUSION: The microwave imaging system exhibits excellent reliability when scanning volunteers multiple times during one session and between sessions. This study represents the largest group of participants scanned at multiple time points reported to date. SIGNIFICANCE: The excellent reliability demonstrated in this study suggests that microwave breast imaging has strong potential for capturing changes over time, such as treatment or therapy related effects, along with detecting changes in breast tissues.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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 it