Assessment of contrast enhanced respiration managed cone-beam CT for image guided radiotherapy of intrahepatic tumors
Bibliographic record
Abstract
PURPOSE: Contrast enhancement and respiration management are widely used during image acquisition for radiotherapy treatment planning of liver tumors along with respiration management at the treatment unit. However, neither respiration management nor intravenous contrast is commonly used during cone-beam CT (CBCT) image acquisition for alignment prior to radiotherapy. In this study, the authors investigate the potential gains of injecting an iodinated contrast agent in combination with respiration management during CBCT acquisition for liver tumor radiotherapy. METHODS: Five rabbits with implanted liver tumors were subjected to CBCT with and without motion management and contrast injection. The acquired CBCT images were registered to the planning CT to determine alignment accuracy and dosimetric impact. The authors developed a simulation tool for simulating contrast-enhanced CBCT images from dynamic contrast enhanced CT imaging (DCE-CT) to determine optimal contrast injection protocols. The tool was validated against contrast-enhanced CBCT of the rabbit subjects and was used for five human patients diagnosed with hepatocellular carcinoma. RESULTS: In the rabbit experiment, when neither motion management nor contrast was used, tumor centroid misalignment between planning image and CBCT was 9.2 mm. This was reduced to 2.8 mm when both techniques were employed. Tumors were not visualized in clinical CBCT images of human subjects. Simulated contrast-enhanced CBCT was found to improve tumor contrast in all subjects. Different patients were found to require different contrast injections to maximize tumor contrast. CONCLUSIONS: Based on the authors' animal study, respiration managed contrast enhanced CBCT improves IGRT significantly. Contrast enhanced CBCT benefits from patient specific tracer kinetics determined from DCE-CT.
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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 | 0.002 |
| 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 | 0.000 | 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".