TH‐D‐M100F‐09: Ultrasound‐Guided Prostate IMRT Planning: An Ultrasound‐CT Application
Bibliographic record
Abstract
Purpose: The prostate contours drawn by physicians on CT images tend to overestimate the real volume due to the poor contrast between the prostate and the surrounding soft tissues. The aim of this study was to utilize ultrasound (US)‐CT to guide a more accurate prostate segmentation for prostate IMRT planning. Method and Materials: In US‐CT modality, the ultrasound system (Restitu™, Resonant Medical System, Montreal, Canada) was integrated with CT‐Sim through an optic camera system, which was calibrated to the intersection point of wall lasers of CT‐Sim and was able to trace the position of ultrasound probe in real‐time. Thus, for each patient, the CT scan and ultrasound scan can take place at the same position and almost the same time. After compensating for the mechanical inaccuracy of CT‐sim and the image distortion of 3‐D US images due to inconsistent ultrasound wave propagation speed in different tissue types, 3‐D CT and 3‐D US images can be superimposed together naturally, since they share the same spatial coordinate system. Thus, the prostate contour drawn on 3‐D Ultrasound images can be transferred into CT images for IMRT planning. Results: Five patients underwent 3‐D US‐CT scan in this study. First, a physician contoured the target volumes and surrounding critical organs on CT images. Then the prostate was contoured on US images for comparison. The fused US‐CT images revealed that the discrepancy between the prostate volume drawn on CT images and ultrasound images takes place mostly at the lateral surface of prostate and the interface between prostate and rectum. The volume of the prostate drawn on US images is 30%∼50% less than that obtained from CT. Conclusion: Ultrasound‐CT, a new multi‐modality imaging system, has a potential to provide a more accurate prostate anatomy definition for prostate IMRT planning thereby reducing radiation dose to surrounding critical structures.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".