SU-E-T-282: Prediction of Secondary Cancer in Pediatric, Adolescent, and Young Adult Patients Receiving Abdominal External Beam Radiotherapy
Bibliographic record
Abstract
Purpose: Evaluate the risk of induction of secondary lung cancer and any solid cancer for patients receiving external beam radiotherapy in the abdomen using intensity modulated photon therapy (IMRT) or 3D conformal radiotherapy (3D' CRT) as compared to intensity modulated proton therapy (IMPT). Methods: Six patients (5 male, 1 female; ages 3–24; previously treated with IMRT or 3D-CRT in the abdomen) were re-planned for IMPT using commercially available treatment planning software. Plan dose volume histograms were used to assess the risk of induction of secondary cancer, specifically lung cancer and any solid tumor in the body. Excess Absolute Risk for lung cancer was predicted using the Schneider modified linear quadratic model. Risk for secondary solid cancer in the body was predicted using two Methods: Excess Relative Risk (ERR) based on a linear relationship between risk and integral dose, and Excess Absolute Risk (EAR) and lifetime cumulative risk implementing the Schneider Organ Equivalent Dose using linear, linear-exponential, and plateau models. An estimate of risk due to neutron dose was calculated for each patient and included in IMPT risk calculations. Results: EAR for lung cancer was on average reduced for IMPT by 49% and 48% as compared to 3D-CRT and IMRT respectively. ERR for secondary solid cancer was on average reduced for IMPT by 28% and 24% as compared to 3D-CRT and IMRT respectively. EAR for secondary solid cancer was on average reduced for IMPT by 32% and 36% as compared to 3D-CRT and IMRT respectively. Lifetime cumulative risk for secondary cancer induction was on average reduced for IMPT by 31% and 35% as compared to 3D-CRT and IMRT respectively. Conclusions: Models predict the risk for induction of secondary lung cancer and any solid cancer is reduced for IMPT as compared to 3D-CRT and IMRT for all patients in this study. This work is supported by a grant from the Fonds de recherche sante Quebec.
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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 | 0.000 | 0.002 |
| 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".