Poster — Thur Eve — 73: Prediction of risks of cardiac mortality and secondary cancers after radiotherapy for Hodgkin's lymphoma, non‐Hodgkin's lymphoma, and breast cancer
Bibliographic record
Abstract
PURPOSE: To predict the risks of late, radiation-induced effects for young patients with Hodgkin's lymphoma (HL), non-Hodgkin's lymphoma (NHL), or breast cancer (BC) if treated with intensity modulated proton therapy (IMPT) compared to 3D conformal photon radiotherapy (3D-CRT). Late effects considered were cardiac mortality and secondary cancer in the lungs and breasts (for female patients). METHODS: Patient data were acquired for twenty-six patients (ages 12-29) who were treated with 3D-CRT for HL, NHL, or BC in 2010. Original CT simulation images were used to re-plan the patients with IMPT using commercially-available treatment planning software. The contours of the organs at risk were reviewed by a single physician and modified for consistency. The dose-volume data of the 3D-CRT plans and the new IMPT plans were analyzed to model the risks of late effects. The relative seriality model was used to predict excess risk of cardiac mortality at fifteen years post-irradiation. A modified linear quadratic model was used to predict the Excess Absolute Risk (EAR) for induction of lung cancer and breast cancer at thirty years post-irradiation. RESULTS: For 3D-CRT and IMPT respectively, the mean excess risks of cardiac mortality were 0.9% and 0.5%. Mean EARs for lung cancer were 17.5 cases per 10,000 persons per year (PY) and 10.1 PY. Mean EARs for breast cancer were 8.2 PY and 2.8 PY. CONCLUSIONS: IMPT may significantly reduce the risks of radiation-induced cardiac mortality and secondary cancer in the lungs and breasts of young patients receiving radiotherapy for HL, NHL, or breast 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".