Review of image-guided radiation therapy
Bibliographic record
Abstract
Image-guided radiation therapy represents a new paradigm in the field of high-precision radiation medicine. A synthesis of recent technological advances in medical imaging and conformal radiation therapy, image-guided radiation therapy represents a further expansion in the recent push for maximizing targeting capabilities with high-intensity radiation dose deposition limited to the true target structures, while minimizing radiation dose deposited in collateral normal tissues. By improving this targeting discrimination, the therapeutic ratio may be enhanced significantly. The principle behind image-guided radiation therapy relies heavily on the acquisition of serial image datasets using a variety of medical imaging platforms, including computed tomography, ultrasound and magnetic resonance imaging. These anatomic and volumetric image datasets are now being augmented through the addition of functional imaging. The current interest in positron-emitted tomography represents a good example of this sort of functional information now being correlated with anatomic localization. As the sophistication of imaging datasets grows, the precise 3D and 4D positions of the target and normal structures become of great relevance, leading to a recent exploration of real- or near-real-time positional replanning of the radiation treatment localization coordinates. This 'adaptive' radiotherapy explicitly recognizes that both tumors and normal tissues change position in time and space during a multiweek course of treatment, and even within a single treatment fraction. As targets and normal tissues change, the attenuation of radiation beams passing through these structures will also change, thus adding an additional level of imprecision in targeting unless these changes are taken into account. All in all, image-guided radiation therapy can be seen as further progress in the development of minimally invasive highly targeted cytotoxic therapies with the goal of substituting remote technologies for direct contact on the part of an operator or surgeon. Although data demonstrating clear-cut superiority of this new high-tech paradigm compared with more conventional radiation treatment approaches are scant, the emergence of preliminary data from several early studies shows that interest in this field is broad based and robust. As outcomes data accumulate, it is very likely that this field will continue to expand greatly. Although at present most of the work is being performed at major academic centers, the enthusiastic adoption of many of the devices and approaches being developed for this field suggest a rapid penetration into the community and the use of the technology by teams of specialists in the fields of radiation medicine, radiation physics and various branches of surgery. A recent survey of practitioners predicted very widespread adoption within the next 10 years.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".