Conventionally fractionated large volume head and neck re-irradiation using multileaf collimator-based robotic technique: A feasibility study
Bibliographic record
Abstract
PurposeTo report on the feasibility and performance of conventionally fractionated multileaf collimator (MLC)-based robotic stereotactic body re-irradiation of the head and neck region using MLC-based Cyberknife (CK) technology.MethodsPatients treated for recurrent or second primary head and neck cancer (HNC) with curative proton therapy to a target volume > 30 cm3 between 2011 and 2015 were included. MLC-based CK plans were generated using the CK M6 InCise2 MLC system. Dose statistics from MLC-based CK plans were compared to proton beam therapy (PBT) plans according to the following metrics: target coverage, target homogeneity index, gradient index, Paddick conformity index (CI), prescription isodose volume (PIV), treatment time (tTime) for one fraction as well as doses to organs at risk (OAR). Wilcoxon signed-rank test was used to compare dose metrics.ResultsEight patients were included; the tumor sites included: salivary glands, pharynx (oropharynx, hypopharynx and retropharynx) and sinonasal cavities. Five of 8 patients were treated with multifield optimisation intensity modulated proton therapy, 3 were treated with passive scattering proton therapy. Median dose was 67 Gy (range 60–70) in 32 fractions (range 30–35). The median high-dose planning target volume (PTV) was 45.4 cm3 (range 2.4 – 130.2 cm3) and the median elective PTV was 91.9 cm3 (range 61.2 – 269.7 cm3). Overall, the mean target coverage (mean 98.3% vs. 96.2% for CK vs. PBT, respectively), maximum dose to PTV (mean 111% vs. 111%, p = 0.2) and mean dose to PTV (mean 104% vs. 104%) were similar across modalities. Highly conformal plans were achieved with both modalities, but mean CI was better with PBT (0.5 vs. 0.6 for CK vs. PBT, p = 0.04). Homogeneity and gradient indexes were similar between the 2 modalities; mean tTime with PBT and CK was 17 vs. 18 min, respectively (p = 0.7). Case-based study revealed that CK and PBT plans allowed for excellent sparing of OAR, with some clinical scenarios associated with better performance of CK while others with better performance of PBT.ConclusionOur study has demonstrated the dosimetric performance of large volume head and neck re-irradiation using MLC-based CK in various clinical scenarios. While conformity was generally better achieved with PBT, MLC-based CK allowed for high dose gradient leading to rapid dose drop-off and sparing of OAR. Conventionally fractionated MLC-based CK could be a competitive alternative in large volume head and neck re-irradiation that deserves further investigation in the clinical setting.
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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) | 0.000 | 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".