Delineation of clinical target volume in nasopharyngeal carcinoma
Bibliographic record
Abstract
Abstract Radiotherapy is the mainstay treatment modality for nasopharyngeal carcinoma (NPC). Intensity-modulated radiation therapy (IMRT), as the standard technique, achieves the purpose of improving target coverage and better sparing of normal tissue. Increased attention has been given to explore various strategies for deescalating treatment intensity. The optimization of clinical target volume (CTV) is one of the most active research areas being widely discussed. Although the International Guidelines for the delineating of CTV in NPC had provided important references for clinicians, there are marked variations in practice among different institutions. This article reviews the development of CTV delineation in non-metastatic NPC patients among centers, and compares the similarities and differences in CTV delineation of various current guidelines in the hope of providing insights for future investigation. This review aims to provide a comprehensive summary of the development and evolution of CTV delineation on primary tumor and lymph nodes for definitive radiotherapy in non-metastatic NPC through historical lens. We also compare the differences of CTV delineation ways. In addition, we look into the clinical and practical challenges of CTV delineation, hoping to provide direction for future research.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".