Dose-volume effects of breast cancer radiation therapy on the risk of second oesophageal cancer
Bibliographic record
Abstract
Purpose To investigate the relationship between oesophagus dose-volume distribution and long-term risk of oesophageal cancer after radiation therapy for breast cancer. Materials and methods In a case-control study nested within a cohort of 289,748 ≥5-year survivors of female breast cancer treated in 1943–2003 in five countries, doses to the second primary cancer (D SPC ) and individual dose-volume histograms (DVH) to the entire oesophagus were reconstructed for 252 oesophageal cancer cases and 488 matched controls (median follow-up time: 13, range: 5–37 years). Using conditional logistic regression, we estimated excess odds ratios (EOR) of oesophageal cancer associated with DVH metrics. We also investigated whether DVH metrics confounded or modified D SPC -related -risk estimates. Results Among the DVH metrics evaluated, median dose (D median ) to the entire oesophagus had the best statistical performance for estimating risk of all histological types combined (EOR/Gy = 0.071, 95% confidence interval [CI]: 0.018 to 0.206). For squamous cell carcinoma, the most common subtype, the EOR/Gy for D median increased by 31% (95% CI: 3% to 205%) for each increment of 10% of V30 ( p = 0.02). Adjusting for DVH metrics did not materially change the EOR/Gy for D SPC , but there was a borderline significant positive interaction between D SPC and V30 ( p = 0.07). Conclusion This first study investigating the relationship between oesophagus dose-volume distribution and oesophageal cancer risk showed an increased risk per Gy for D median with larger volumes irradiated at high doses. While current techniques allows better oesophagus sparing, constraints applied to D median and V30 could potentially further reduce the risk of oesophageal cancer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".