SU‐E‐T‐461: Fractionation Schedule Optimization for Lung Cancer Treatments Using Radiobiological and Dose Distribution Characteristics
Bibliographic record
Abstract
PURPOSE: Lung cancer radiotherapy treatments employ a wide variety of fractionation protocols. The choice among protocols mostly depends on the size of the target volume (GTV or ITV) and the volume of normal tissue receiving a critical dose. Rigorous mathematical criteria for normal tissue (NT) dose distributions were derived to determine the type of dose per fraction schedule that maximizes linear-quadratic tumor effect. METHODS: Selecting the individual doses per fraction that maximize a linear-quadratic effect in the tumor while constraining the normal tissue complication probability according to the Lyman-Kutcher-Burman model leads to an optimization problem. For time-independent parameters, the solution is always an equal dose per fraction schedule; depending on parameter values, two different class solutions are suggested: minimal number of fractions clinically realized with hypo-fractionation, or minimizing dose per fraction clinically realized with standard- or hyper-fractionation. The value of a single scale-free "bifurcation" number, derived from the DVH of the NT dose distribution suggests which solution is preferred for a given plan with respect to a given normal tissue. The clinical relevance of the bifurcation number in selecting fractionation schemes was tested for 30 patients previously treated for non-small-cell lung cancer according to various fractionation protocols. RESULTS: The bifurcation numbers for both lung and esophagus were a good classifier for the hypofractionated and the conventional fractionation groups. The variability of the numbers within patients of the conventional fractionation group was much smaller than the variability of the treated ITV volumes or the ITV to lung volume ratios. The prescribed fractionations were also consisted with the currently accepted alpha-beta values for tumor (10) and radiation-induced pneumonities in the lung (4). CONCLUSIONS: Model-based criteria such as the bifurcation number may replace the more empirical volume criteria to decide the optimal fractionation protocol once the dose distribution has been optimized.
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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 | 0.000 | 0.001 |
| 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 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".