Pre-treatment journey and outcome for children with intracranial non-germinomatous germ cell tumors—the Shanghai experience
Bibliographic record
Abstract
Abstract Background Non-germinomatous germ cell tumors (NGGCTs) occur 3.5 times more frequently in Chinese children than in western populations. This study aimed to evaluate treatment outcomes, prognostic factors, and diagnostic challenges in a Chinese cooperative group, with particular focus on the roles of pathology and surgical intervention. Methods We retrospectively analyzed 62 consecutively diagnosed pediatric patients with NGGCT (September 2018-June 2023) from Shanghai Children’s Medical Center and Huashan Hospital. NGGCT was diagnosed by histopathology with/without elevated tumor markers (n = 46) or by elevated markers alone (n = 16). All patients received standardized treatment according to Children’s Oncology Group ACNS0122 protocol. Whole-exome sequencing was performed on 12 paired tumor-blood samples to characterize molecular alterations. Results The study cohort included 49 males and 13 females (median age, 9.8 year). Primary locations were mainly pineal (58%) and suprasellar (29%). Treatment delays (>6 mo) occurred in 21% of patients, particularly those with non-pineal locations and endocrine symptoms. The 3 years event-free survival and overall survival rates were 81.9 ± 5.4% and 91.3 ± 3.7%, respectively. Univariate analysis identified poor prognostic factors: elevated AFP >200 ng/mL, spinal metastases, and lack of complete/partial response after induction chemotherapy. Surgical resection of small residual tumors (<2 cm) provided no survival benefit. Molecular analysis revealed KRAS and KIT as the most frequent mutations, with chromosome 12p abnormalities in 50% of cases. Conclusions Standardized multidisciplinary treatment achieves favorable outcomes comparable to international benchmarks. Aggressive surgery does not improve survival when tumor markers normalize. Diagnostic delays remain common, emphasizing the need for improved awareness and referral systems in China.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".