Development of a 3D transrectal ultrasound-guided prostate biopsy system integrated with a prostate-specific PET system
Bibliographic record
Abstract
Prostate cancer (PCa) remains a significant public health challenge worldwide, as it is the most diagnosed cancer among men. Traditional 2D transrectal ultrasound (TRUS)-guided biopsies have high false-negative rates, necessitating repeat procedures. The novel prostate-specific PET (P-PET) system developed by Radialis offers increased sensitivity and resolution, addressing the limitations of whole-body PET systems. This study aims to develop and clinically validate an integrated 3D TRUS and P-PET-guided prostate biopsy system, incorporating robotic technologies for precise needle guidance. The project includes developing a motorized 3D TRUS mechanism with an integrated needle guidance device, calibrating and validating the system using a tissue-mimicking phantom, integrating 3D TRUS software with the P-PET system, and evaluating guidance accuracy with phantom models. The system consists of a motorized 3D TRUS system for the generation of volumetric images, a tracking arm for targeting and a needle guidance device for needle placement and repositioning. The mechatronic needle guidance system includes a needle template, which is capable of two-dimensional manual translation, and is used in the alignment of the needle with the P-PET defined lesions with the help of TRUS image. The 3D TRUS-guided biopsy system, tested using a custom-designed prostate phantom, demonstrated successful guidance with in-house software and alignment of needle paths. A virtual needle path was created and traced in the 3D US image, followed by live tracking of the biopsy needle in the 2D US image during insertion. The results indicated that the needle path planning, and placement was accurate with a mean guidance error of 0.85 ± 0.22 mm. Tests confirm the system’s potential to enhance PCa biopsy accuracy by reducing false-negative rates and minimizing the need for repeat biopsies. The enhanced sensitivity and specificity compared to MRI and whole-body PET systems makes it a viable and accessible diagnostic tool.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".