Surveillance imaging and radiation exposure in the detection of relapsed neuroblastoma
Bibliographic record
Abstract
BACKGROUND: More than half of children with high-risk neuroblastoma (NB) will experience recurrence. Radiologic imaging is used for initial staging and during therapy to assess response. However, the role of surveillance imaging in the detection of relapse has not been well studied. Surveillance potentially results in high cumulative exposure to ionizing radiation, which may be associated with an increased risk of developing second malignancies. PROCEDURE: We reviewed NB cases at our institution between 2000 and 2011. We calculated radiation exposure due to imaging (during diagnosis, treatment, and posttherapy surveillance) using cumulative effective dose (CED) estimates and determined whether cross-sectional imaging identified recurrences. RESULTS: Fifty of 183 patients with NB experienced a recurrence. The median time from diagnosis to relapse was 1.20 years (range: 0.18-6.66 years). Most patients had evidence of metastases and only 4 of 50 patients presented with isolated primary tumor site recurrences. The mean CED prior to relapse was 125.2 mSv (range: 24.5-259.7), 64% of which was from computed tomography (CT) scans. Thirty-seven of 50 patients had clinically evident or measurable disease detected by X-ray (XR), ultrasound (US), or urinary catecholamines (UCats), and the addition of metaiodobenzylguanidine (MIBG) scans identified eight additional recurrences. Thus, cross-sectional imaging (CT/MRI, where MRI is magnetic resonance imaging) was only required to identify 10% (5/50) of cases. CONCLUSION: Relapsed disease was detected in most patients by symptoms/exam, MIBG scan, UCats, and/or XR/US, supporting reduced use of CT imaging in posttherapy surveillance, thereby decreasing cumulative radiation dose. Refinement of surveillance imaging may be further guided by risk stratification, disease sites, and potentially biomolecular markers.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".