SU‐E‐T‐504: Usefulness of CT‐MR Fusion in Radiotherapy Planning for Prostate Cancer Patient with Bilateral Hip Replacements
Bibliographic record
Abstract
Purpose: Target localization of prostate for Intensity Modulated Radiation Therapy (IMRT) in patients with bilateral hip replacements is difficult due to artifacts in Computed Tomography (CT) images generated from the prostheses high Z materials. In this study, Magnetic Resonance (MR) images fused with CT images are tested as a solution. Methods: CT images of 2.5 mm slice thickness were acquired on a GE Lightspeed scanner with a flat‐topped couch for a prostate cancer patient with bilateral hip replacements. T2 weighted images of 5 mm separation were acquired on a MR Scanner. After the MR‐CT registration on a radiotherapy treatment planning system (Eclipse, Varian), the target volumes were defined by the radiation oncologists on MR images and then transferred to CT images for planning and dose calculation. The CT Hounsfield Units (HU) was reassigned to zero (as water) for artifacts. The Varian flat panel treatment couch was modeled for dose calculation accuracy with heterogeneity correction. A Volume Matrix Arc Therapy (VMAT) and a seven‐field IMRT plans were generated, each avoiding any beam transversing the prostheses; the two plans were compared. The superior VMAT plan was used for treating the patient. In‐vivo dosimetry was performed using MOSFET (Best Canada) placed in a surgical tube inserted into the patient rectum during therapy. The measured dose was compared with planned dose for MOSFET location. Results: The registration of MR‐CT images and the agreement of target volumes were confirmed by three physicians. VMAT plan was deemed superior to IMRT based on dose to critical nearby structures and overall conformality of target dosing. In‐vivo measured dose compared with calculated dose was ‐4.5% which was likely due to attenuation of the surgical tube surrounding MOSFET. Conclusion: When artifacts are present on planning CT due to bilateral hip prostheses, MR‐CT image fusion is a feasible solution for target delineation.
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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 | 0.000 | 0.001 |
| 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".