Left hippocampus sparing model for glioblastoma radiotherapy by utilizing knowledge‐based planning and multi‐criteria optimization
Bibliographic record
Abstract
PURPOSE: Results of a prospective, randomized controlled trial at our institute demonstrate an association between the dose to the left hippocampus and neurocognitive decline post-radiotherapy for patients with glioblastoma. To minimize the dose to the left hippocampus, a left hippocampus sparing model was created using RapidPlan (RP) and multi-criteria optimization (MCO). MATERIALS AND METHODS: For 147 patients with newly diagnosed glioblastoma treated with volumetric modulated arc therapy (VMAT), the left and right hippocampus were delineated. Ninety-seven of 147 VMAT plans were used to configure a RP model named HCS1. The remaining 50 VMAT plans were used for the model validation. All 97 plans were replanned with the HCS1 and further optimized using MCO (HCS1+MCO). MCO was used to explore the trade-off between reducing the left hippocampus mean dose and planning objectives for the targets and other organs-at-risk (OAR) for HCS1 plans. These plans were used to create a new model called HCS2. MCO and RP model configuration were done within the Eclipse treatment planning system. RESULTS: The final HCS2 model decreased the mean dose to the left hippocampus by 26% compared to clinically treated plans without reducing target coverage for 50 validation data. The mean dose to the left hippocampus decreased from 32.65 Gy in clinically treated plans, 30.45 Gy in HCS1-generated plans, and 24.04 Gy in HCS2-generated plans. The mean volume receiving 95% of the prescription dose (V95%) of the planning target volume was 99.08% ± 1.39% in clinically treated plans, 99.03% ± 1.37% in HCS1-generated plans, and 98.80% ± 1.48% in HCS2-generated plans. Mean dose to 0.1 cc of the brainstem improved from 45.91 Gy in clinically treated plans to 39.29 Gy in HCS2-generated plans. CONCLUSIONS: The RP model and MCO helps to decrease left hippocampus mean dose while maintaining the target volume coverage and OAR sparing comparable to clinically treated plans for glioblastoma patients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".