Quantification of prostate deformation due to needle insertion during TRUS-guided biopsy: comparison of hand-held and mechanically stabilized systems
Bibliographic record
Abstract
Prostate biopsy is the clinical standard for the definitive diagnosis of prostate cancer. To overcome the limitations of 2D TRUS-guided biopsy systems when targeting pre-planned locations, systems have been developed with 3D guidance to improve the accuracy of cancer detection. Prostate deformation due to needle insertion and biopsy gun firing is a potential source of error that can cause target misalignments during biopsies. We use non-rigid registration of 2D TRUS images to quantify the deformation during the needle insertion and the biopsy gun firing procedure, and compare this effect in biopsies performed using a handheld TRUS probe with those performed using a mechanically assisted 3D TRUS guided biopsy system. Although the mechanically assisted biopsy system had a mean deformation approximately 0.2 mm greater than that of the handheld approach, it yielded a lower relative increase of deformation near the needle axis during the needle insertion stage and greater deformational stability of the prostate during the biopsy gun firing stage. We also analyzed the axial and lateral components of the tissue motion; our results indicated that the motion is weakly biased in the direction orthogonal to the needle, which is less than ideal from a targeting standpoint given the long, narrow cylindrical shape of the biopsy core.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".