Thyroid Cancer in Childhood Leukemia Survivors: A Systematic Review of the Incidence and Survival Outcomes
Bibliographic record
Abstract
Background/Objective: Radiotherapy for leukemia, the most common childhood malignancy, often exposes patients to radiation, increasing the risk of second malignancies, including thyroid cancer. To assess the incidence and survival outcomes of thyroid cancer after childhood acute lymphoblastic leukemia (ALL). Methods: We systematically reviewed articles reporting the incidence of thyroid cancer in childhood leukemia survivors (age at diagnosis < 18 years) published between 2000–2024 in Science Direct, PubMed, Google Scholar, CENTRAL, and EMBASE. The Newcastle Ottawa Scale was utilized to appraise the methodological quality of the included studies. Descriptive statistics and calculations of incidence were performed using Microsoft Excel. Results: The literature search yielded 1265 articles, of which 18 met the inclusion criteria. Data from 135,861 childhood cancer survivors, among whom 102,070 had a confirmed diagnosis of childhood leukemia, including ALL. The crude incidence of secondary malignancies after childhood leukemia was 10.1 per 1000 patients. Among these, 1.5 per 1000 patients developed second thyroid carcinomas. Overall, 14.6% of the second malignancies in childhood leukemia survivors were thyroid carcinomas, mostly of the papillary type. Survival rates after second thyroid cancer were 100% in all 11/18 studies reporting this outcome. Radiotherapy had been used as part of ALL treatments in 17/18 studies. The use of radiotherapy, female sex, and younger age at the diagnosis of primary ALL emerged as important risk factors for thyroid cancer. Conclusions: Thyroid carcinomas account for ~15% of secondary malignancies after childhood leukemia, with radiation remaining a significant risk factor despite its overall reduced use for the treatment of ALL in the last few decades. Importantly, survival rates remain high. Further research is warranted to determine the incidence and outcomes of thyroid cancer in childhood ALL survivors
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".