Risk of solid subsequent malignant neoplasms after childhood Hodgkin lymphoma—Identification of high‐risk populations to guide surveillance: A report from the Late Effects Study Group
Bibliographic record
Abstract
BACKGROUND: Survivors of Hodgkin lymphoma (HL) in childhood have an increased risk of subsequent malignant neoplasms (SMNs). Herein, the authors extended the follow-up of a previously reported Late Effects Study Group cohort and identified patients at highest risk for SMNs to create evidence for risk-based screening recommendations. METHODS: The standardized incidence ratio was calculated using rates from the Surveillance, Epidemiology, and End Results program as a reference. The risk of SMN was estimated using proportional subdistribution hazards regression. The cohort included 1136 patients who were diagnosed with HL before age 17 years between 1955 and 1986. The median length of follow-up was 26.6 years. RESULTS: In 162 patients, a total of 196 solid SMNs (sSMNs) were identified. Compared with the general population, the cohort was found to be at a 14-fold increased risk of developing an sSMN (95% confidence interval, 12.0-fold to 16.3-fold). The cumulative incidence of any sSMN was 26.4% at 40 years after a diagnosis of HL. Risk factors for breast cancer among females were an HL diagnosis between ages 10 years and 16 years and receipt of chest radiotherapy. Males treated with chest radiotherapy at age <10 years were found to be at highest risk of developing lung cancer. Survivors of HL who were treated with abdominal/pelvic radiotherapy and high-dose alkylating agents were found to be at highest risk of developing colorectal cancer and females exposed to neck radiotherapy at age <10 years were at highest risk of thyroid cancer. By age 50 years, the cumulative incidence of breast, lung, colorectal, and thyroid cancer was 45.3%, 4.2%, 9.5%, and 17.3%, respectively, among those at highest risk. CONCLUSIONS: Survivors of childhood HL remain at an increased risk of developing sSMNs. In the current study, subgroups of survivors of HL at highest risk of specific sSMNs were identified, and evidence for screening provided.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".