Incidence and Outcomes of CNS Tumors in Chinese Children: Comparative Analysis With the Surveillance, Epidemiology, and End Results Program
Bibliographic record
Abstract
PURPOSE Despite being the most common pediatric solid tumors, incidence and outcome of CNS tumors in Chinese children have not been systematically reported. We addressed this knowledge gap by comparing the epidemiology of pediatric CNS tumors in Hong Kong and the United States. PATIENTS AND METHODS Data between 1999 and 2016 from a population-based cancer registry in Hong Kong, China, on patients < 18 years old with CNS tumors (Hong Kong cohort) and from the US SEER Program (Asian/Pacific Islander and all ethnicities) were compared. Incidence and overall survival (OS) by histology were evaluated. RESULTS During the study period, 526 children were newly diagnosed with CNS tumors in Hong Kong (crude incidence rate, 2.47 per 100,000; 95% CI, 2.26 to 2.69). Adjusted incidences were significantly lower in the Hong Kong (2.51; 95% CI, 2.30 to 2.74) than in the SEER (Asian/Pacific Islander: 3.26; 95% CI, 2.97 to 3.57; P < .001; all ethnicities: 4.10 per 100,000; 95% CI, 3.99 to 4.22; P < .001) cohorts. Incidences of germ cell tumors (0.57 v 0.24; P < .001) were significantly higher, but those of glial and neuronal tumors (0.94 v 2.61; P < .001), ependymomas (0.18 v 0.31; P = .005), and choroid plexus tumors (0.08 v 0.16; P = .045) were significantly lower in Hong Kong compared with SEER (all ethnicities) cohorts. Compared with the SEER (Asian/Pacific Islander) cohort, histology-specific incidences were similar except for a lower incidence of glial and neuronal tumors in Hong Kong (0.94 v 1.74; P < .001). Among cohorts, OS differed only for patients with glial and neuronal tumors (5-year OS: Hong Kong, 52.5%; SEER [Asian/Pacific Islander], 73.6%; SEER [all ethnicities], 79.9%; P < .001). CONCLUSION We identified important ethnic differences in the epidemiology of CNS tumors in Chinese children. These results will inform the development of pediatric neuro-oncology services in China and aid further etiologic studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".