Diagnostic Performance of LI-RADS Version 2018 for Evaluation of Pediatric Hepatocellular Carcinoma
Bibliographic record
Abstract
Background The Liver Imaging Reporting and Data System (LI-RADS) has standardized the evaluation of adult but not pediatric hepatocellular carcinoma (HCC). Purpose To evaluate the performance of LI-RADS version 2018 for diagnosis of pediatric HCC. Materials and Methods This multi-institution retrospective study evaluated all available dynamic CT and/or MRI scans of pediatric (≤18 years) HCC from five institutions between July 2009 and April 2019. The control group included an equal number of other enhancing hepatic lesions. Blinded to final diagnosis, three radiologists independently applied LI-RADS version 2018 criteria. The reference standard was pathologic examination or more than 1 year follow-up. Sensitivity and specificity of LI-RADS were computed using a dichotomous classification of LR-1, LR-2, or LR-3 versus LR-4, LR-5, LR-TIV (tumor in vein), or LR-M (probably or definitely malignant but not HCC-specific) for predicting hepatic malignancy in the entire cohort and in patients at risk for HCC. Results The cohort consisted of 116 children: 58 with HCC (mean age, 12 years ± 5; 31 girls) and 58 with other enhancing hepatic masses (mean age, 12 years ± 5; 42 girls). Frequencies of major criteria in classic HCC for the three readers were as follows: nonrim arterial phase hyperenhancement, 49%–62% (19–24 of 39 patients); nonperipheral “washout,” 36%–59% (14–23 of 39 patients); and enhancing “capsule,” 28%–38% (11–15 of 39 patients). For the full cohort, the sensitivity of LR-4, LR-5, LR-TIV, or LR-M for malignancy among the three readers ranged from 85% (95% CI: 76, 94) to 88% (95% CI: 80, 96); specificity of LR-1, LR-2, or LR-3 for benignity ranged from 54% (95% CI: 40, 68) to 70% (95% CI: 57, 83). In the at-risk subgroup, sensitivity ranged from 58% (95% CI: 36, 80) to 68% (95% CI: 48, 89); specificity ranged from 56% (95% CI: 37, 74) to 63% (95% CI: 45, 81). All lesions categorized as LR-TIV (n = 10–13) were HCCs. Conclusion Liver Imaging Reporting and Data System version 2018 had moderate sensitivity but low specificity for the diagnosis of pediatric hepatocellular carcinoma (HCC), which had low frequencies of the major criteria used for adult HCC diagnosis. © RSNA, 2021 See also the editorial by Paltiel in this issue.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".