Tomotherapy as a tool in image-guided radiation therapy (IGRT): current clinical experience and outcomes
Bibliographic record
Abstract
Modern radiotherapy is characterised by a better target definition through medical imaging accompanied by significantly improved radiation delivery methods, most notably Intensity-Modulate Radiation Therapy (IMRT). However, the treatment can only be as accurate as the positioning of patients for their daily radiotherapy fraction. It is in this context that a number of imaging modalities - ranging from ultrasound to on-board kilovoltage imaging and computed tomography (CT) - have found their way into the treatment room where they verify accurate patient positioning prior to or even during delivery of radiation. Helical tomotherapy (HT) combines IMRT delivery with in-built image guidance using megavoltage CT scanning. This paper discusses the initial experience of different centres with IGRT using HT illustrated by a number of clinical examples from the installation in London in Ontario, Canada, one of the world's first HT sites. We found that HT allows the delivery of highly conformal radiation dose distributions combined with adequate daily image acquisition. An important feature of this unit is its seamless integration, which also includes a customised inverse treatment planning system and a quality assurance module for individual patients.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".