Histologic type predicts disparate outcomes in pediatric hepatocellular neoplasms: A Pediatric Surgical Oncology Research Collaborative study
Bibliographic record
Abstract
BACKGROUND: Hepatocellular carcinoma (HCC) is a rare cancer in children, with various histologic subtypes and a paucity of data to guide clinical management and predict prognosis. METHODS: A multi-institutional review of children with hepatocellular neoplasms was performed, including demographic, staging, treatment, and outcomes data. Patients were categorized as having conventional HCC (cHCC) with or without underlying liver disease, fibrolamellar carcinoma (FLC), and hepatoblastoma with HCC features (HB-HCC). Univariate and multivariate analyses identified predictors of mortality and relapse. RESULTS: In total, 262 children were identified; and an institutional histologic review revealed 110 cHCCs (42%; 69 normal background liver, 34 inflammatory/cirrhotic, 7 unknown), 119 FLCs (45%), and 33 HB-HCCs (12%). The authors observed notable differences in presentation and behavior among tumor subtypes, including increased lymph node involvement in FLC and higher stage in cHCC. Factors associated with mortality included cHCC (hazard ratio [HR], 1.63; P = .038), elevated α-fetoprotein (HR, 3.1; P = .014), multifocality (HR, 2.4; P < .001), and PRETEXT (pretreatment extent of disease) stage IV (HR, 5.76; P < .001). Multivariate analysis identified increased mortality in cHCC versus FLC (HR, 2.2; P = .004) and in unresectable tumors (HR, 3.4; P < .001). Disease-free status at any point predicted survival. CONCLUSIONS: This multi-institutional, detailed data set allowed a comprehensive analysis of outcomes for children with these rare hepatocellular neoplasms. The current data demonstrated that pediatric HCC subtypes are not equivalent entities because FLC and cHCC have distinct anatomic patterns and outcomes in concert with their known molecular differences. This data set will be further used to elucidate the impact of histology on specific treatment responses, with the goal of designing risk-stratified algorithms for children with HCC. LAY SUMMARY: This is the largest reported granular data set on children with hepatocellular carcinoma. The study evaluates different subtypes of hepatocellular carcinoma and identifies key differences between subtypes. This information is pivotal in improving understanding of these rare cancers and may be used to improve clinical management and subsequent outcome in children with these rare malignancies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".