Abstract A030 Secondary malignancies after radiotherapy treatment of pediatric cancer patients in Egypt over a six-year period
Bibliographic record
Abstract
Abstract Background: Pediatric cancer survivors are at risk of developing secondary malignant neoplasms (SMNs) due to exposure to prior treatment modalities, including radiotherapy (RT). We conducted a retrospective cohort study to examine whether initial RT dose was associated with a greater risk of developing SMNs among pediatric cancer patients in Egypt. Methods: We identified patients (8 days-18 years at enrollment in care) who had received RT for treating primary cancer at Children’s Cancer Hospital (CCHE) in Cairo, Egypt, between 2009 and 2015. Patients were classified into two exposure groups based on the median initial RT course dosage, where ≤3600 centrigray (cGy) and >3600 cGy were considered low and high RT exposure, respectively. The cumulative incidence of SMN accounting for the competing risk of death was compared by initial RT dose using the cumulative incidence function, and multivariable sub-distributional hazard (SHR) models were fitted to compare the hazards of SMN for children with low vs high initial RT dose. Results: Of the 3,132 patients included in this study, 60.1% were male and the median age at enrollment in care was 6.2 years (interquartile range: 3.5-10.7). Approximately half (50.6%) of the patients received a low initial RT dose. The cumulative incidence of SMN at 11.1 years was 3.05% (95% CI 1.15-5.93%) overall and 3.92% (95% CI 1.19-9.39%) and 2.01% (95% CI 0.77-4.38%) for low and high RT dose patients, respectively. Children who received high initial RT doses did not have higher hazards of developing SMN in bivariable (SHR 0.55, 95% CI 0.32-1.75) and multivariable models (aSHR 0.25, 95% CI 0.04-1.70). Conclusion: In this cohort of pediatric cancer patients in Egypt, high initial RT dose was not associated with increased hazards of SMN. Future research should extend follow-up time after the first decade following an initial cancer diagnosis to capture a more complete picture of SMN incidence. Other initiatives may include establishing national cancer registries in low- to middle-income countries to better track longitudinal cancer data in these populations. Citation Format: Charlotte L. Sackett, Mohamed S Zaghloul, Ahmed Aldesouky, Amr S Soliman, Chloe A. Teasdale. Secondary malignancies after radiotherapy treatment of pediatric cancer patients in Egypt over a six-year period [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A030.
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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 | 0.001 | 0.001 |
| 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) | 0.014 | 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".