Dosimetric Planning Comparison for Left Ventricle Avoidance in Non-small Cell Lung Cancer Radiotherapy
Bibliographic record
Abstract
Introduction: Radiation may unintentionally injure myocardial tissue, potentially leading to radiation-induced cardiac disease (RICD), with the net benefit of non-small cell lung cancer (NSCLC) radiotherapy (RT) due to the proximity of the lung and heart. RTOG-0617 showed a greater reduction in overall survival (OS) comparing higher doses to standard radiation doses in NSCLC RT. V5GyHeart has been reported as an OS predictor in the first- and fifth-year follow-ups. A worsening OS trend was reported in another study where the mean left ventricle dose (mean LV) was ≥14.5 Gy. It is therefore important to spare the heart, specifically the LV, from radiation. Furthermore, dose-limiting factors toward the normal lung should be accounted for to prevent radiation-induced lung injury. Methods: The LV and left anterior descending artery (LAD) were also contoured on the average four-dimensional computed tomography (4D-CT) dataset that contained clinically defined targets and normal structures for stage III NSCLC RT. The prescribed treatment plans (n=15) were retrospectively optimized with the clinical goals of minimizing the mean LV and mean heart dose while maintaining the dose constraint of V20GyLung ≤30% and V95%PTV ≥95%. Dose-volume histograms were used to compare the heart and lung dosimetric parameters between the delivered and reoptimized RT plans. Results: A significant reduction (p≤0.044) was observed in the mean LV, mean heart dose, mean LAD dose, max LAD dose, and V5GyHeart from the reoptimized RT plans. V20GyLung ≤30% and V95%PTV ≥95% were maintained, and no differences were observed in the mean lung, V5GyLung, V20GyLung, mean esophagus, and max cord. Conclusion: Minimizing the LV dose in NSCLC RT plans is achievable and dosimetrically advantageous for the heart while maintaining dose constraints to the normal lung and maximizing tumor control. Radiation dose reduction to cardiac substructures may decrease the RICD risk in NSCLC patients.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".