Adaptive radiotherapy for nasopharyngeal carcinoma
Bibliographic record
Abstract
Abstract: The concept of “adaptive radiotherapy” (ART) was introduced more than 20 years ago. It refers to imaging feedback control strategies with treatment plan modification in response to patient-specific treatment variation during the course of radiotherapy. ART is particularly relevant to nasopharyngeal carcinoma (NPC) patients in the precision radiotherapy era since contemporary intensity-modulated radiotherapy (IMRT) combined with chemotherapy could result in significant volumetric alteration of the tumor and normal tissue during the treatment course. Studies have shown that ART could enhance locoregional control (LRC) and improve patients’ functional outcomes. ART has been evaluated in clinical research and implemented in clinical practice to improve IMRT customization for patients in need. However, no consensus exists regarding when and how to implement ART in a systematic manner. ART is often restricted by its labor-intensive and time-consuming nature and technical challenges. This review summarizes recent advances in the implementing ART for NPC relating to potential dosimetric and clinical benefit, when and how to trigger ART, efforts to streamline the workflow of ART including image registration, and potential integration of computer-assisted auto-contouring.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".