Image Guidance: Treatment Target Localization Systems
Bibliographic record
Abstract
Highly conformal radiation therapy tailors treatment to match the target shape and position, minimizing normal tissue damage to a greater extent than previously possible. Technological advances such as intensity-modulated radiation therapy, introduced a decade ago, have yielded significant gains in tumor control and reduced toxicity. Continuing advances have focused on the characterization and control of patient movement, organ motion, and anatomical deformation, which all introduce geometric uncertainty. These sources of uncertainty limit the effectiveness of high-precision treatment. Target localization, performed using appropriate technologies and frequency, is a critical component of treatment quality assurance. Until recently, the target position with respect to the beams has been inferred from surface marks on the patient's skin or through an immobilization device, and verified using megavoltage radiographs of the treatment portal. Advances in imaging technologies have made it possible to image soft tissue volumes in the treatment setting. Real-time tracking is also possible using a variety of technologies, including fluoroscopic imaging and radiopaque markers implanted in or near the tumor. The capacity to acquire volumetric soft tissue images in the treatment setting can also be used to assess anatomical changes over a course of treatment. Enhancing localization practices reduces treatment errors, and gives the capacity to monitor anatomical changes and reduce uncertainties that could influence clinical outcomes. This review presents the technologies available for target localization, and discusses some of the considerations that should be addressed in the implementation of many new clinical processes in radiation oncology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".