Update on the Liver Imaging Reporting and Data System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is frequently diagnosed noninvasively with imaging techniques. Computed tomography and magnetic resonance imaging play critical roles in the detection, diagnosis, and staging of HCC. Standardization in the interpretation and reporting of imaging modalities has not existed until recently. In 2008, the American College of Radiology supported the development of the Liver Imaging Reporting and Data System (LI-RADS) for standardized terminology, interpretation, and reporting of imaging examinations for the diagnosis of HCC inpatients at risk for HCC. This article reviews the basic concepts of LI-RADS, emphasizing aspects that are most relevant to pathologists, including the categories, diagnostic algorithm, major features, and ancillary features for the diagnosis of HCC. The similarities and differences between LI-RADS and other major radiology-based diagnostic systems in terms of target population, intended users, categorization of observations, and imaging methods are addressed. Importantly, LI-RADS and other systems are designed to diagnose progressed HCC with high specificity and modest sensitivity. LI-RADS and other systems are not designed to detect early HCC and so have limited sensitivity for such lesions. Moreover, despite continuous advances in imaging technology, imaging detection and characterization of small (<1 cm) nodules remains limited; in addition, colocalization of small nodules and pathology is difficult. For these reasons LI-RADS and most other systems require lesions to be 1 cm or greater for the noninvasive diagnosis of HCC. As LI-RADS evolves, it is critical that stakeholders, including pathologists, provide expert input to help standardize and enhance reporting of radiologic findings.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it