Experience‐driven dose‐volume histogram maps of NTCP risk as an aid for radiation treatment plan selection and optimization
Bibliographic record
Abstract
Commonly, the quality of treatment plans is judged by a dose-volume histogram (DVH) in regards to satisfying a series of dose-volume constraints. This paper presents a novel technique for mapping normal tissue complication probabilities (NTCP) onto regions of dose-volume space with statistical considerations of risk. Mapping is done for DVHs specific to one treatment technique for one disease site. In this study, the method is illustrated for simplified intensity modulated arc therapy of the prostate, and the resulting NTCP values apply to complications in the rectum. The method consists of implementing a Monte Carlo algorithm that creates a large set of DVH curves by simulating random walks through dose-volume space. The walks are guided by a base set of clinical DVHs. Grid points in the dose-volume space have an associated NTCP spectrum for curves passing above right of the grid point of interest. After a DVH is simulated and the NTCP estimate calculated using the Lyman model, dose-volume points located to the bottom left of the curve are scored with this NTCP value and contributed to the spectrum of each point. A NTCP tolerance value is then selected and the risk of violating this tolerance is identified by a gray-scale map in regions of dose-volume space. The generated maps distinguish technique-specific, high-risk regions, a feature which is advantageous over fixed single-point dose-volume constraints commonly used. The maps also provide a visualization tool to help select safe and robust treatment plans and open the possibility for improving the efficiency of biologically based plan optimization by focusing on the more critical sections of DVH curves.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".