Intentional avoidance of the esophagus using intensity modulated radiation therapy to reduce dysphagia after palliative thoracic radiation
Bibliographic record
Abstract
BACKGROUND: or greater but with an increased incidence of esophagitis. The objective of this planning study was to assess the potential impact of esophageal-sparing IMRT (ES-IMRT) compared to the current standard of care using parallel-opposed pair beams (POP). METHODS: In this study, 15 patients with lung cancer treated to a dose of 30Gy in 10 fractions between August 2015 and January 2016 were identified. Radiation treatment plans were optimized using ES-IMRT by limiting the max esophagus point dose to 24Gy. Using published Lyman-Kutcher-Burman normal tissue complication probabilities (LKB-NTCP) models, both plans were evaluated for the likelihood of esophagitis (≥ grade 2) and pneumonitis (≥ grade 2). RESULTS: Using ES-IMRT, the median esophageal and lung mean doses reduced from 16 and 8Gy to 7 and 7Gy, respectively. Using the LKB models, the theoretical probability of symptomatic esophagitis and pneumonitis reduced from 13 to 2%, and from 5 to 3%, respectively. The median normalize total dose (NTD mean) accounting for fraction size for the GTV and PTV of the clinically approved POP plans compared to the ES-IMRT plans were similar. CONCLUSION: Advanced radiotherapy techniques such as ES-IMRT may have clinical utility in reducing treatment-related toxicity in advanced lung cancer patients. Our data suggests that the rate of esophagitis can be reduced without compromising local control.
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 | 0.000 |
| 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) | 0.001 | 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".