Impact of tissue heterogeneity corrections in stereotactic body radiation therapy treatment plans for lung cancer
Bibliographic record
Abstract
This study aims at evaluating the impact of tissue heterogeneity corrections on dosimetry of stereotactic body radiation therapy treatment plans. Four-dimensional computed tomography data from 15 low stage non-small cell lung cancer patients was used. Treatment planning and dose calculations were done using pencil beam convolution algorithm of Varian Eclipse system with Modified Batho Power Law for tissue heterogeneity. Patient plans were generated with 6 MV co-planar non-opposing four to six field beams optimized with tissue heterogeneity corrections to deliver a prescribed dose of 60 Gy in three fractions to at least 95% of the planning target volume, keeping spinal cord dose <10 Gy. The same plans were then regenerated without heterogeneity correction by recalculating previously optimized treatment plans keeping identical beam arrangements, field fluences and monitor units. Compared with heterogeneity corrected plans, the non-corrected plans had lower average minimum, mean, and maximum tumor doses by 13%, 8%, and 6% respectively. The results indicate that tissue heterogeneity is an important determinant of dosimetric optimization of SBRT plans.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".