Helical tomotherapy and volumetric modulated arc therapy: New therapeutic arms in the breast cancer radiotherapy
Bibliographic record
Abstract
AIM: To analyse clinical and dosimetric results of helical tomotherapy (HT) and volumetric modulated arc therapy (VMAT) in complex adjuvant breast and nodes irradiation. METHODS: Seventy-three patients were included (31 HT and 42 VMAT). Dose were 63.8 Gy (HT) and 63.2 Gy (VMAT) in the tumour bed, 52.2 Gy in the breast, 50.4 Gy in supraclavicular nodes (SCN) and internal mammary chain (IMC) with HT and 52.2 Gy and 49.3 Gy in IMC and SCN with VMAT in 29 fractions. Margins to particle tracking velocimetry were greater in the VMAT cohort (7 mm vs 5 mm). RESULTS: For the HT cohort, the coverage of clinical target volumes was as follows: Tumour bed: 99.4% ± 2.4%; breast: 98.4% ± 4.3%; SCN: 99.5% ± 1.2%; IMC: 96.5% ± 13.9%. For the VMAT cohort, the coverage was as follows: Tumour bed: 99.7% ± 0.5%, breast: 99.3% ± 0.7%; SCN: 99.6% ± 1.4%; IMC: 99.3% ± 3%. For ipsilateral lung, Dmean and V20 were 13.6 ± 1.2 Gy, 21.1% ± 5% (HT) and 13.6 ± 1.4 Gy, 20.1% ± 3.2% (VMAT). Dmean and V30 of the heart were 7.4 ± 1.4 Gy, 1% ± 1% (HT) and 10.3 ± 4.2 Gy, 2.5% ± 3.9% (VMAT). For controlateral breast Dmean was 3.6 ± 0.2 Gy (HT) and 4.6 ± 0.9 Gy (VMAT). Acute skin toxicity grade 3 was 5% in the two cohorts. CONCLUSION: HT and VMAT in complex adjuvant breast irradiation allow a good coverage of target volumes with an acceptable acute tolerance. A longer follow-up is needed to assess the impact of low doses to healthy tissues.
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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 | 0.001 |
| 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) | 0.001 | 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".