Ultrasound Velocity Imaging and Lateral Stereotactic BIOPSY: NEW TOOLS FOR BREAST Cancer Detection
Bibliographic record
Abstract
Breast cancer is the most common cancer among Canadian women. There is strong evidence that early detection and diagnosis of breast cancer improves both the survival rate and quality of life for these women. In this thesis, two technical developments aimed towards improving the sensitivity of breast cancer detection are presented and evaluated. First, the feasibility of detecting occult solid breast lesions using ultrasound velocity differences between malignant lesions and surrounding tissue is investigated. In this technique, the breast is placed on a flat backplate and imaged using 3D ultrasound. The difference in ultrasound velocity through solid lesions versus surrounding breast tissue produces a shift in the apparent elevation of the backplate in the resulting image. We provide a mathematical model to predict backplate elevation as a function of lesion thickness, and validate this model experimentally using agar phantoms. Our results suggest that this technique may be useful as an additional screening tool for detection of otherwise occult solid breast lesions. Second, a new needle guidance device for lateral stereotactic breast biopsy (SBB) is presented, which addresses the limitations of commercially available needle guidance hardware. Specifically, the new device provides: 1) an adjustable rigid needle support to minimize needle deflection within the breast, and; 2) an additional degree of rotational freedom in the needle trajectory, allowing the sampling multiple targets through a single skin incision. This device was compared to a commercial lateral guidance device in a series of SBB phantom experiments. Needle placement error using each device was measured for deep and superficial needle insertions in agar phantoms. The biopsy success rate for each device was estimated by performing biopsy procedures in certified SBB phantoms. In these experiments, SBB with the new lateral guidance device provided significant improvements in both needle placement error and biopsy accuracy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".