P6C-7 Ultrasound RF Time Series for Detection of Prostate Cancer: Feature Selection and Frame Rate Analysis
Bibliographic record
Abstract
This paper provides the recent results of in-vitro clinical studies to evaluate the performance of a tissue typing method, based on ultrasound RF time series, for detection of prostate cancer. In our approach, we continuously record RF echo signals backscattered from tissue, while the imaging probe and the tissue are stationary in position. The continuously recorded frames generate a time series of echo values for each spatial sample of RF signals. We extract the fractal dimension, and six spectral features from the RF time series and use them with neural networks for tissue typing. We analyze the performance of this method in detecting prostate cancer in 16 patients and demonstrate that a subset of five parameters selected from the proposed features is optimal for the diagnosis. We also study the performance of the extracted features at various frame rates and show that 22 frames per second is sufficient for efficient cancer detection with an area under ROC curve of 0.89.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".