Dosimetric, Radiobiological and Secondary Cancer Risk Evaluation in Head-and-Neck Three-dimensional Conformal Radiation Therapy, Intensity-Modulated Radiation Therapy, and Volumetric Modulated Arc Therapy: A Phantom Study
Bibliographic record
Abstract
This analysis estimated secondary cancer risks after volumetric modulated arc therapy (VMAT) and compared those risks to the risks associated with other modalities of head-and-neck (H&N) radiotherapy. Images of H&N anthropomorphic phantom were acquired with a computed tomography scanner and exported via digital imaging and communications in medicine (DICOM) standards to a treatment planning system. Treatment plans were performed using a VMAT dual-arc technique, a nine-field intensity-modulated radiation therapy (IMRT) technique, and a four-field three-dimensional conformal therapy (3DCRT) technique. The prescription dose was 66.0 Gy for all three techniques, but to accommodate the range of dosimeter responses, we delivered a single dose of 6.60 Gy to the isocenter. The lifetime risk for secondary cancers was estimated according to National Council on Radiation Protection and Measurements (NCRP) Report 116. VMAT delivered the lowest maximum doses to esophagus (23 Gy), and normal brain (40 Gy). In comparison, maximum doses for 3DCRT were 74% and 40%, higher than those for VMAT for the esophagus, and normal brain, respectively. The normal tissue complication probability and equivalent uniform dose for the brain (2.1%, 0.9%, 0.8% and 3.8 Gy, 2.6 Gy, 2.3 Gy) and esophagus (4.2%, 0.7%, 0.4% and 3.7 Gy, 2.2 Gy, 1.8 Gy) were calculated for the 3DCRT, IMRT and VMAT respectively. Fractional esophagus OAR volumes receiving more than 20 Gy were 3.6% for VMAT, 23.6% for IMRT, and 100% for 3DCRT. The calculations for mean doses, NTCP, EUD and OAR volumes suggest that the risk of secondary cancer induction after VMAT is lower than after IMRT and 3DCRT.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".