Dosimetric consequences of misalignment and realignment in prostate 3DCRT using intramodality ultrasound image guidance
Bibliographic record
Abstract
PURPOSE: It is common practice to correct for interfraction motion by shifting the patient from reference skin marks to better align the internal target at the linear accelerator's isocenter. Shifting the patient away from skin mark alignment causes the radiation beams to pass through a patient geometry different from that planned. Yet, dose calculations on the new geometry are not commonly performed. The intention of this work was to compare the dosimetric consequences of treating the patient with and without setup correction for the common clinical scenario of prostate interfraction motion. METHODS: In order to account for prostate motion, 32 patients initially aligned to the room lasers via skin marks were realigned under the treatment beams by shifting the treatment couch based on ultrasound image guidance. An intramodality 3D ultrasound image guidance system was used to determine the setup correction, so that errors stemming from different tissue representations on different imaging modalities were eliminated. Two scenarios were compared to the reference static treatment plan: (1) Uncorrected patient alignment and (2) corrected patient alignment. Prostate displacement statistics and the dose to the clinical target volume (CTV), bladder, and rectum are reported. Monte Carlo dose calculation methods were employed. RESULTS: Comparing the uncorrected and corrected scenarios using the static treatment plan as the reference, the average percent difference in D95 for the CTV improved from -5.1% (range -40%, 1.3%) to 0.0% (-3.5%, 2.0%) and the average percent difference in V90 for the bladder and rectum changed from -11% (-84%, 232%) to -8.3% (-61%, 5.2%) and from -47% (-100%, 108%) to 0.9% (-62%, 102%), respectively. There was no simple correlation between displacement and dose discrepancy before correction. After patient realignment, the prescribed dose to the CTV was achieved within 1% for 75% (24/32) of the patients. After patient realignment, 50% of the patients had doses that differed from the static treatment plan by 25% for the bladder and 8% for the rectum. CONCLUSIONS: The dose degradation due to prostate motion (before correction) is not accurately predicted from the average trends for all patients. Outliers included smaller displacements that lead to larger dosimetric differences in the corrected scenario, especially for the bladder and rectum, which exhibited doses substantially different from that planned.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".