Differential diagnosis of breast lesions with RF time-series signal based on ultrasonic radio-frequency flow
Bibliographic record
Abstract
Objective To evaluate the values of RF time-series signal based on ultrasonic radio-frequency flow in the differentiation of benign and malignant breast lesions. Methods A commercially available clinical ultrasound scanner, Sonix TOUCH (Ultrasonix Medical Corporation, Richmond, Canada) with a L14–5 linear ultrasound transducer was used to simultaneously collect B-mode images and RF data from breast lesions. The ultrasound probe displaying the maximal plane of the breast lesion was kept in the same position for 10 seconds. The ultrasound RF data from region of interest was imported into software developed by our lab for ultrasound spectral analysis and 9 spectral parameters including SMR fractal dimension, Higuchi fractal dimension, Slope, Intercept, Mid-band fit, S1, S2, S3, S4 were calculated. 137 patients with 137 breast lesions confirmed by pathological or follow-up findings were included in the study. Of the 137 breast lesions, 86 malignant and 30 benign lesions were confirmed by ultrasound guided core needle biopsy or surgical excision, and the rest 21 lesions were presumed benign as no significant change was found after at least 2 years of follow-up. Results There are significantly difference in spectral parameters including SMR fractal dimension, Higuchi fractal dimension, Slope, Intercept, Mid-band fit, S1, S2, S3 and S4 between the malignant and benign breast lesions (0.75±0.77 vs 0.82±0.10, t=-4.722, 1.31±0.07 vs 1.42±0.10, t=-7.476, -0.24±0.04 vs -0.26±0.06, t=1.986, 0.19±0.03 vs 0.21±0.048, t=-3.391, 0.067±0.011 vs 0.08±0.019, t=-5.319, 3.22±0.54 vs 3.60±0.83, t=-3.298, 0.53±0.12 vs 0.73±0.23, t=-6.467, 0.31±0.06 vs 0.45±0.13, t=-9.207, 0.24±0.05 vs 0.38±0.12, t=-9.367, all P<0.05). Conclusion RF time-series signal based on ultrasonic radio-frequency flow could provide a new imaging method with a simple, low-cost noninvasive technique for the differential diagnosis of the benign and malignant breast lesions. Key words: Breast neoplasms; Ultrasonography; Diagnosis, differential
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.011 | 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".