Comparison of simple and complex liver intensity modulated radiotherapy
Bibliographic record
Abstract
BACKGROUND: Intensity-modulated radiotherapy (IMRT) may allow improvement in plan quality for treatment of liver cancer, however increasing radiation modulation complexity can lead to increased uncertainties and requirements for quality assurance. This study assesses whether target coverage and normal tissue avoidance can be maintained in liver cancer intensity-modulated radiotherapy (IMRT) plans by systematically reducing the complexity of the delivered fluence. METHODS: An optimal baseline six fraction individualized IMRT plan for 27 patients with 45 liver cancers was developed which provided a median minimum dose to 0.5 cc of the planning target volume (PTV) of 38.3 Gy (range, 25.9-59.5 Gy), in 6 fractions, while maintaining liver toxicity risk <5% and maximum luminal gastrointestinal structure doses of 30 Gy. The number of segments was systematically reduced until normal tissue constraints were exceeded while maintaining equivalent dose coverage to 95% of PTV (PTVD95). Radiotherapy doses were compared between the plans. RESULTS: Reduction in the number of segments was achieved for all 27 plans from a median of 48 segments (range 34-52) to 19 segments (range 6-30), without exceeding normal tissue dose objectives and maintaining equivalent PTVD95 and similar PTV Equivalent Uniform Dose (EUD(-20)) IMRT plans with fewer segments had significantly less monitor units (mean, 1892 reduced to 1695, p = 0.012), but also reduced dose conformity (mean, RTOG Conformity Index 1.42 increased to 1.53 p = 0.001). CONCLUSIONS: Tumour coverage and normal tissue objectives were maintained with simplified liver IMRT, at the expense of reduced conformity.
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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) | 0.002 | 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".