Feasibility of tumor detection with a transmission‐based microwave imaging system
Bibliographic record
Abstract
BACKGROUND: Microwave imaging has been proposed for breast cancer detection, relying on differences between the microwave frequency properties of healthy and cancerous tissues. Specifically, localized increases in permittivity and/or conductivity may be identified in images of the breast created with microwave tomography. In radar-based images, responses may be noted where property changes occur. Our team has developed a microwave imaging approach that creates maps of permittivity by analyzing the characteristics of signals transmitted through the tissues, scanning the breast in views similar to mammography. PURPOSE: This study aims to assess the feasibility of tumor detection with a transmission-based microwave imaging system. Specifically, both breasts of a control group and a group of women with a cancer diagnosis are scanned in both cranial-caudal (CC) and medial-lateral oblique (MLO) orientations to facilitate comparison with mammography. METHODS: The microwave scanner consists of planar transmit and receive arrays that are placed in contact with the breast. The arrays are placed horizontally to collect the CC view, then tilted to an angle of 45 degrees to collect the MLO view. Signals with frequency content from 0.1 to 8 GHz are transmitted through the tissue, and the characteristics of the detected signals are used to estimate the microwave frequency properties (permittivity). Estimates at each sensor pair are mapped to the imaging plane. The average microwave frequency properties are calculated for the breast region identified in each image. Images are also segmented using k-means and thresholding to further explore tumor detection. Statistical analysis is applied, specifically analysis of variance (ANOVA) to determine differences between views and groups. RESULTS: 20 healthy participants and 14 cancer patients are scanned. Right and left breasts are compared for each patient. Consistency is noted when comparing the CC scans of the breasts of healthy participants; similar observations are made for the MLO scans. Similarity between CC and MLO views of the same breast is also noted and confirmed via ANOVA testing. For patients with confirmed cancer, greater differences are noted between the breasts in at least one view when observing images and average properties. The ratio of the average permittivity of the cancerous breast to the contralateral breast is significantly different than the ratio observed in healthy volunteers when considering CC views. Greater ratios are also observed for patients with higher breast densities. CONCLUSION: This study demonstrates the potential for a transmission-based microwave scanner to detect tumors.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".