Reproducibility of Microwave Breast Imaging: Analysis of Regular Scans of a Group of Volunteers
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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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".