Patterns of local residual disease and local failure after intensity modulated/image guided radiation therapy for sinonasal tumors in dogs
Bibliographic record
Abstract
BACKGROUND: Most dogs with sinonasal tumors (SNT) treated with radiation therapy (RT) died because of local disease progression. HYPOTHESIS/OBJECTIVES: Our hypothesis is that the majority of local failure and residual disease would occur within the radiation field. ANIMALS: Twenty-two dogs with SNT treated with RT. METHODS: Retrospective cohort study. INCLUSION CRITERIA: dogs with SNT receiving 10 daily fractions of 4.2 Gy with intensity modulated radiation therapy (IMRT)/image guided radiation therapy (IGRT) and follow-up cone beam computed tomography (CBCT). Each CBCT was registered with the original radiation planning CT and the gross tumor volume (GTV) contoured. The GTV was classified as residual (GTVr) or a failure (GTVf). The dose statistic for each GTV was calculated with the original IMRT plan. For GTVf, failures were classified as "in-field," "marginal," or "out-field" if at least 95, 20-95, or less than 20% of the volume of failure was within 95% (D95) of the total prescription dose, respectively. RESULTS: There were 52 follow-up CBCT/CTs. Overall there was a GTVr for 20 dogs and GTVf for 16 dogs. The majority of GTVr volume was within the original GTV. GTVf analysis showed that 75% (12/16) were "in-field," 19% (3/16) were "marginal" and 6% (1/16) were "out-field." CONCLUSION AND CLINICAL IMPORTANCE: In-field failures are the main pattern for local recurrence, and there is evidence of radioresistant subvolumes within the GTV.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".