Detection of Relapse by Tumor Markers Versus Imaging in Children and Adolescents With Nongerminomatous Malignant Germ Cell Tumors: A Report From the Children’s Oncology Group
Bibliographic record
Abstract
PURPOSE: To investigate relapse detection methods among children and adolescents with nongerminomatous malignant germ cell tumors (MGCTs) and to determine whether tumor markers alone might be sufficient for surveillance. METHODS: We retrospectively reviewed all patients enrolled in a phase III, single-arm trial for low-risk and intermediate-risk MGCTs. The method used to detect relapse was assessed based on case report forms, tumor markers, imaging, and pathology reports. Relapses were classified into one of two categories on the basis of whether they were (1) detectable by tumor marker elevation or (2) not detectable by tumor markers. RESULTS: A total of 302 patients were enrolled, and 284 patients had complete data for review. Seven patients had normal tumor markers at initial diagnosis, and none experienced a relapse. At a median follow-up of 5.3 years, 48 patients (16.9%) had experienced a relapse. After central review, 47 of 48 relapses (98%) were detected by tumor marker elevation. Of the 47 patients, 16 (33.3%) had abnormal tumor markers with normal/unknown imaging, 31 patients (64.6%) had abnormal tumor markers with abnormal imaging, and one patient (2.1%) had abnormal imaging with unknown marker levels at relapse. CONCLUSION: Tumor marker elevation is a highly sensitive method of relapse surveillance, at least among children and adolescents with tumor marker elevation at initial diagnosis. Eliminating exposure to imaging with ionizing radiation may enhance the safety of relapse surveillance in patients treated for MGCT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| 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) | 0.000 | 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 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".