Image Guidance and the New Practice of Radiotherapy: What to Know and Use from a Decade of Investigation
Bibliographic record
Abstract
Over the past decade, fundamental advances in image-guided radiation therapy (IGRT) have been made that are now being implemented in clinical practice. Imaging technologies to direct and confirm beam accuracy at the time of radiotherapy delivery have been intensively researched and developed. More recently, these imaging data have been used to evaluate and even modify the daily dose delivery of intended treatment plans. The rationale for the use of IGRT, to improve tumor control while limiting normal tissue toxicity, is a universal goal in radiotherapy. Avoidance of unexpected under- or overdosing during treatment is the most important benefit of IGRT, and has led to its integration into the use of advanced radiotherapy planning/delivery technologies for many clinical applications. Evidence-based strategies to effectively use IGRT in the clinic are still emerging. The evolving role of IGRT and some proposed strategies to exploit its potential benefits in the clinic will be presented, emphasizing the perspective of the radiation clinician. Practical strategies will be proposed to exploit the potential benefits of IGRT technologies in the clinic.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".