MR Imaging of Pediatric Neuroblastoma: Is Gadolinium Enhancement Necessary for Evaluation of Image-Defined-Risk Factors?
Bibliographic record
Abstract
Background: Pre-treatment stratification and outcomes of neuroblastoma patients often depend on the assessment of image-defined risk factors (IDRFs) on MR Imaging, usually using Gadolinium-contrast materials which are cautioned in pediatrics. We aimed to address whether gadolinium contrast-enhanced sequences are necessary to identify the presence/absence of IDRFs. Methods: Patients with neuroblastoma with MR imaging were retrospectively identified from 2005 to 2021. Ninety confirmed IDRFs were evaluated in 23 patients. Corresponding MR studies were anonymized, randomized, and independently evaluated by 3 fellowship-trained pediatric radiologists. Each radiologist assessed the studies twice. At the first reading, all enhanced sequences were omitted, while in the second reading, the full study with enhanced sequences were included. Consensus reading was obtained among readers. Inter- and intra-rater agreements using Kappa statistics (κ) as well as the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of non-enhanced MR in assessing IDRFs with respect to enhanced MR were calculated. Results: There were substantial (ĸ: 0.64-0.73) intra-reader agreements, and moderate to substantial (ĸ: 0.57-0.62) inter-reader agreements among radiologists in identifying IDRFs using non-enhanced MR. Non-enhanced MR had a sensitivity of 87.8% (95% CI [79-94]), specificity of 93% (89-96), PPV of 82.3 (73-89), NPV of 95.4 (92-98), and accuracy of 91.6 (88-94) in identifying IDRFs. However, 5/23 patients (21.7%) had a change in staging with the inclusion of contrast sequences. Conclusion: Although contrast sequences have a role in IDRF assessment, the majority can be adequately assessed on MR without gadolinium-contrast enhancement. Validation in a larger cohort is an important next step.
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 | 0.003 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".