Sci‐Fri PM Imaging‐06: Registered Digital Stereotactic Mammography and 3D‐Ultrasound for Breast Biopsy Guidance
Bibliographic record
Abstract
Large core needle biopsy is a common procedure used to obtain histological samples when a suspicious lesion is detected in diagnostic breast images. The procedure is typically performed using image guidance, with ultrasound (US) and stereotactic mammography (SM) being the most common modalities used. Each of these imaging methods, however, has limitations that impact their clinical utility. For example, some breast structures are not visible in ultrasound. Mammography provides better visualization of features such as microcalcifications, but does not support real‐time imaging. A prototype device combining the advantages of digital SM and 3D‐US with computer‐aided needle guidance was developed at our centre. The objective of this work was to determine the position of biopsy targets with an error of less than 0.5 mm. A methodology was first developed to calibrate the SM system. Then, by imaging a set of the same physical points identifiable in both SM and 3DUS images, the two modalities were registered. In both cases, the target registration error (TRE) was calculated to quantify the error in determining the position of points imaged within the breast. For locating points in the SM images alone, the TRE was found to be 0.35 mm. The TRE found in registering the two modalities was 0.37 mm. The TRE value is clinically relevant as it indicates the position error associated with selecting an arbitrary image point for biopsy. When compared to the typical breast biopsy needle diameter of 2.1 mm, the calculated TRE from the two imaging modalities was sufficiently small.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".