Probabilistic target definition and planning in patients with prostate cancer
Bibliographic record
Abstract
Abstract Intro. Current radiation therapy (RT) planning guidelines handle uncertainties in RT using geometric margins. This approach is simple to use but oversimplifies complex underlying processes and is cumbersome for non-homogeneous dose prescriptions. In this work, we characterize the performance of a novel probabilistic target definition and planning (PTP) approach, which uses voxel-level tumor likelihood information in treatment plan optimization. Methods. We expanded a treatment planning system with probabilistic therapy planning functionality that utilizes non-binary target maps (TM) as voxel-level input to dose plan optimization. Different dose plans were calculated and compared for twelve prostate cancer patients with multiparametric magnetic resonance imaging derived TMs. Dose plans were created using both classical and PTP approaches for uniform and integrated dose boost prescriptions. Dose performance between the different approaches was compared using dose benchmarks on target and organ-at-risk (OAR) volumes. Results. Over all dose metrics, PTP was shown to be comparable to classical planning. For plans of uniform dose prescription, the PTP approach created plans within 1 Gy of the classical planning approach across all dose metrics, with no significant differences ( p > 0.2). For plans with the integrated dose boost, PTP plans exhibited higher dose heterogeneity, but still showed target doses comparable to the classical approach, without increasing doses to OAR. Conclusion. In this work we introduce direct incorporation of probabilistic target definition into treatment planning. This treatment planning approach can produce both uniform dose plans and plans with integrated dose boosts that are comparable to ones created using classical dose planning. PTP is a flexible way to optimize external beam radiotherapy, as it is not limited by the use of margins. PTP can produce dose plans equivalent to classical planning, while also allows for greater versatility in dose prescription and direct incorporation of patient target definition uncertainty into treatment planning.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".