Stereotactic body radiation therapy (SBRT) and respiratory gating in lung cancer: dosimetric and radiobiological considerations
Bibliographic record
Abstract
The purpose of this study was to assess the impact of respiratory gating on tumor and normal tissue dosimetry in patients treated with SBRT for early stage non-small cell lung cancer (NSCLC). Twenty patients with stage I NSCLC were studied. Treatment planning was performed using four-dimensional computed tomography (4D CT) with free breathing (Plan I), near-end inhalation (Plan II), and near-end exhalation (Plan III). The prescription dose was 60 Gy in three fractions. The tumor displacement was most pronounced for lower peripheral lesions (average 7.0 mm, range 4.1-14.3 mm) when compared to upper peripheral (average 2.4mm, range 1.0-5.1 mm) or central lesions (average 2.9 mm, range 1.0-4.1 mm). In this study, the pencil beam convolution (PBC) algorithm with modified Batho power law for tissue heterogeneity was used for dose calculation. There were no significant differences in tumor and normal tissue dosimetry among the three gated plans. Tumor location however, significantly influenced tumor doses because of the necessity of respecting normal tissue constraints of centrally located structures. For plans I, II and III, average doses to central lesions were lower as compared with peripheral lesions by 4.88 Gy, 8.24 Gy and 6.93 Gy for minimum PTV and 0.98, 1.65 and 0.87 Gy for mean PTV dose, respectively. As a result, the mean single fraction equivalent dose (SFED) values were also lower for central compared to peripheral lesions. In addition, central lesions resulted in higher mean doses for lung, esophagus, and ipsilateral bronchus by 1.24, 1.93 and 7.75 Gy, respectively. These results indicate that the tumor location is the most important determinant of dosimetric optimization of SBRT plans. Respiratory gating proved unhelpful in the planning of these patients with severe COPD.
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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 | 0.002 | 0.002 |
| 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) | 0.000 | 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".