Individualized estimates of second cancer risks after contemporary radiation therapy for Hodgkin lymphoma
Bibliographic record
Abstract
BACKGROUND: Estimates of radiation-related second cancer risk among Hodgkin lymphoma survivors are largely based on radiation therapy (RT) fields and doses no longer in use, and these estimates do not account for differences in normal tissue dose among individual patients. This study gives individualized estimates for the risks of lung and female breast cancer expected with contemporary involved-field RT and low-dose (20 Gy) RT for mediastinal Hodgkin lymphoma. METHODS: Three RT plans were constructed for 37 consecutive patients with mediastinal Hodgkin lymphoma: 35 Gy mantle RT, 35 Gy involved-field RT (IFRT), and 20 Gy IFRT. For each of the 111 RT plans, individual-level dosimetry data were incorporated into a cell initiation/inactivation/proliferation model to estimate the excess relative risk (ERR) and cumulative incidence of radiation-induced second cancer. RESULTS: ERR estimates were compatible with results of epidemiological studies. Compared with 35 Gy mantle radiation therapy, 35 Gy IFRT was predicted to reduce the 20-year ERRs of breast and lung cancer by 63% and 21%, respectively, primarily because of lower normal tissue doses with the omission of axillary RT. Low-dose (20 Gy) IFRT was associated with a 77% and 57% decrease in these ERRs. Patient-specific differences in normal tissue dose with IFRT led to 11-fold and 3.6-fold variations among individual's estimates of breast and lung cancer ERR, respectively. CONCLUSIONS: Contemporary IFRT is predicted to substantially reduce risk of secondary breast and lung cancer compared with mantle RT, with considerable variation in risk among individuals. Individualized prospective risk estimates could facilitate patient-specific counseling and the development of more effective RT techniques.
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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 | 0.004 |
| 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 | 0.000 | 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".