A comparison of needle tip localization accuracy using 2D and 3D trans-rectal ultrasound for high-dose-rate prostate cancer brachytherapy treatment planning
Bibliographic record
Abstract
Background: High-dose-rate brachytherapy (HDR-BT) is a prostate cancer treatment option involving the insertion of hollow needles into the gland through the perineum to deliver a radioactive source. Conventional needle imaging involves indexing a trans-rectal ultrasound (TRUS) probe in the superior/inferior (S/I) direction, using the axial transducer to produce an image set for organ segmentation. These images have limited resolution in the needle insertion direction (S/I), so the sagittal transducer is used to identify needle tips, requiring a manual registration with the axial view. This registration introduces a source of uncertainty in the final segmentations and subsequent treatment plan. Our lab has developed a device enabling 3D-TRUS guided insertions with high S/I spatial resolution, eliminating the need to align axial and sagittal views. Purpose: To compare HDR-BT needle tip localization accuracy between 2D and 3D-TRUS. Methods: 5 prostate cancer patients underwent conventional 2D TRUS guided HDR-BT, during which 3D images were also acquired for post-operative registration and segmentation. Needle end-length measurements were taken, providing a gold standard for insertion depths. Results: 73 needles were analyzed from all 5 patients. Needle tip position differences between imaging techniques was found to be largest in the S/I direction with mean±SD of -2.5±4.0 mm. End-length measurements indicated that 3D TRUS provided statistically significantly lower mean±SD insertion depth error of -0.2±3.4 mm versus 2.3±3.7 mm with 2D guidance (p < .001). Conclusions: 3D TRUS may provide more accurate HDR-BT needle localization than conventional 2D TRUS guidance for the majority of HDR-BT needles.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".