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.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".