Can Dynamic Contrast-Enhanced MRI Be Used to Differentiate Hepatic Hemangioma from Other Lesions in Early Infancy?
Bibliographic record
Abstract
Abstract Background Confident diagnosis of hepatic hemangioma on imaging can avoid biopsy in early infancy and helps guide conservative management. Purpose This article aims to determine if dynamic contrast-enhanced magnetic resonance imaging (MRI) can be used to differentiate liver hemangioma from other lesions in infants ≤ 100 days and to determine association of MRI features with hepatic lesions. Methods MRI performed for liver lesions were retrospectively reviewed to note imaging characteristics and the MRI diagnosis. Final diagnosis was assigned based on pathology in available cases and by corroborative standard of reference including overall clinical features, lab findings, and follow-up. Results Of 30 infants (18 boys, 12 girls; average age 42.2 days) included, 18 had solitary and 12 had multifocal lesions. Diagnoses in total 33 lesions included hemangiomas (23), hepatoblastoma (6), arteriovenous malformation (2), neuroblastoma metastases (1), and infarction (1). MRI and final diagnosis matched in 94% lesions with almost perfect agreement (kappa 0.86) for reader 1, and matched in 88% lesions with substantial agreement (kappa 0.71) for reader 2. Interobserver agreement for MRI diagnosis was substantial (kappa 0.62). Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of MRI in differentiating hemangioma from other lesions were 100, 90, 96, 100, and 97%, respectively. Centripetal (16/23) or flash (5/23) filling were only seen with hemangioma. There was no significant difference in alpha-fetoprotein elevation (p 0.08), average size (p 0.35), multifocality (p 0.38), and intralesional hemorrhage (p 1) between hemangioma and hepatoblastoma. Conclusion Centripetal filling on dynamic imaging and absence of washout are characteristic MRI features of hepatic hemangioma that can help to differentiate it from other lesions in early infancy.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".