Dosimetric comparison of intensity-modulated radiation therapy (IMRT) and field-in-field (FIF) technique for head-and-neck cancer
Bibliographic record
Abstract
Abstract Purpose: This study compared the plan dosimetry between the intensity-modulated radiation therapy (IMRT) and field-in-field (FIF) technique for head-and-neck cancer using the Elekta Monaco treatment planning system (TPS). Materials and methods: A total of 20 head-and-neck cancer patients were selected in this study. IMRT and FIF plans for the patients were created on the Monaco TPS (ver. 5.11.02) using the 6-MV photon beam generated by the Elekta Synergy linear accelerator. The dose–volume histograms, maximum doses, minimum doses, mean doses of the target volumes and organs-at-risk (OARs), conformity index (CI), homogeneity index (HI) and monitor units (MUs) were determined for each IMRT and FIF plan. All IMRT plans passed the patient-specific quality assurance tests from the 2D diode array measurements (MatriXX Evolution System, IBA Dosimetry, Germany). Results: The results showed that the dose distribution to the target volumes of IMRT plans was better than FIF plans, while the dose (mean or max dose) to the OAR was significantly lower than FIF plan, respectively. IMRT and FIF resulted in planning target volume coverage with mean dose of 71·32 ± 0·76 and 73·12 ± 0·62 Gy, respectively, and HI values of 0·08 ± 0·01 (IMRT) and 0·19 ± 0·06 (FIF). The CI for IMRT was 0·98 ± 0·01 and FIF was 0·97 ± 0·01. For the spinal cord tolerance (maximum dose < 45 Gy), IMRT resulted in 39·85 ± 2·04 Gy compared to 41·37 ± 2·42 Gy for FIF. In addition, the mean doses to the parotid grand were 27·27 ± 7·48 and 48·68 ± 1·62 Gy for the IMRT and FIF plans, respectively. Significantly more MUs were required in IMRT plans than FIF plans (on average, 846 ± 100 MU in IMRT and 467 ± 41 MU in FIF). Conclusions: It is concluded that the IMRT technique could provide a better plan dosimetry than the FIF technique for head-and-neck 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 | 0.000 | 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 | 0.001 |
| 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".