161 PSMA-PET Guided Intensification of Radiotherapy for Prostate Cancer: Preliminary Detection Rate and Impact on Radiotherapeutic Management
Bibliographic record
Abstract
Purpose: To present an innovative technology for continuous patient position monitoring during precision radiation therapy treatment.The system can detect intra-fraction patient motion with sub-millimeter accuracy in 3D during SRS treatment.It can also be used to detect respiratory motion during lung, breast, or liver treatment.The system provides real-time motion monitoring with no direct contact with the patient and without the use of ionizing radiation or relying on surrogates such as skin. Materials and Methods:This technology relies on continuous capacitance measurements for motion detection.The system is sensitive to position of the body (cranium, chest, etc.) and insensitive to the position of the surrounding immobilization devices, fabric, etc. Due to the conductivity of the human body, placing a conductive sensor near the body forms a capacitor and monitoring the resulting capacitance provides real-time information regarding the distance between the sensor and region of interest (ROI) of the body.A cranial array of four conductive sensors (4 by 6 inch) placed around the cranium was used to detect 3D motion of the cranium within the thermoplastic mask in real time with the help of a volunteer.A respiratory array comprised of three sensors (2 by 4 inch) was used to detect the respiratory motion for different regions of interest for chest and abdominal breathing with the help of a volunteer.Results: Our cranial prototype can detect 0.5mm motion with 0.1mm accuracy in three dimensions.The respiratory prototype can detect chest and abdominal motion in agreement with the RPM system.The system is not sensitive to the thermoplastic mask, fabrics, etc. and does not require an unobstructed view of the region of interest. Conclusions:This new technology provides a non-contact, real time, and non-ionizing motion monitoring system that is not dependent on deformable surrogates i.e. skin and does not require unobstructed view of the body.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".