Multimodality Image‐Guided Radiotherapy of the Liver
Bibliographic record
Abstract
Summary Technological advances have made it possible for tumoricidal doses of radiation to be delivered to primary and metastatic liver cancers. Computed tomography, magnetic resonance imaging, ultrasound and positron emission tomography are used to help with tumor definition at the time of radiation planning. Specialized imaging techniques are also used for characterization of tumor motion due to breathing at the time of radiation planning. Image‐guided radiation therapy (IGRT), referring to the use of frequent imaging in the treatment position during a course of radiotherapy to localize the tumor prior to or during each treatment, improves accuracy and precision of radiation delivery. IGRT improves the concordance between the delivered doses to the tumor and normal tissues and the planned doses, which should improve our understanding of dose–outcome analyses. IGRT also reduces the volume of normal tissue that needs to be irradiated, and facilitates dose escalation to the tumor, potentially improving tumor control probability and reducing the risk of toxicity. Image registration is required to bring imaging data sets together at the time of radiation planning and also for image guidance at treatment. This study provides an overview of multimodality imaging and IGRT used in liver cancer conformal radiation therapy.
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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 | 0.000 |
| 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".