Microwave imaging for monitoring breast cancer treatment: A pilot study
Bibliographic record
Abstract
Abstract Background Microwave imaging has been proposed for medical applications, creating maps related to water content of tissues. Breast imaging has emerged as a key application because the signals can be coupled directly into the breast and experience limited attenuation in fatty tissues. While the literature contains reports of tumor detection with microwave approaches, there is limited exploration of treatment monitoring. Purpose This study aims to detect treatment‐related changes in breast tissue with a low‐resolution microwave scanner. Methods Microwave scans of 15 patients undergoing treatment for early‐stage breast cancer are collected at up to 4 time points: after surgery (baseline), 6 weeks after accelerated partial breast radiation, as well as 1 and 2 years post‐treatment. Both the treated and untreated breast are scanned at each time point. The microwave scanner consists of planar transmit and receive arrays and uses signals from 0.1 to 10 GHz. The average microwave frequency properties (permittivity) are calculated for each scan to enable quantitative comparison. Baseline and 6‐week results are analyzed with a two‐way ANOVA with blocking. Results Consistent properties are observed for the untreated breast over time, similar to a previous study. Comparison of the scans of the treated and untreated breast suggests increased properties related to treatment, particularly at baseline and 6‐weeks following radiotherapy. Analysis of the average properties of the scans with ANOVA indicates statistically significant differences () in the treated and untreated breast at these time points. Conclusions Microwave imaging has the potential to track treatment‐related changes in breast tissues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".