LI‐RADS (Liver Imaging Reporting and Data System): Summary, discussion, and consensus of the LI‐RADS Management Working Group and future directions
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
To improve standardization and consensus regarding performance, interpreting, and reporting computed tomography (CT) and magnetic resonance imaging (MRI) examinations of the liver in patients at risk for hepatocellular carcinoma (HCC), LI-RADS (Liver Imaging Reporting and Data System) was launched in March 2011 and adopted by many clinical practices throughout the world. LI-RADS categorizes nodules recognized at CT or MRI, in patients at high risk of HCC, as definitively benign, probably benign, intermediate probability of being HCC, probably HCC, and definitively HCC (corresponding to LI-RADS categories 1-5). The LI-RADS Management Working Group, consisting of internationally recognized medical and surgical experts on HCC management, as well as radiologists involved in the development of LI-RADS, was convened to evaluate management implications related to radiological categorization of the estimated probability that a lesion will be ultimately diagnosed as HCC. In this commentary, we briefly review LI-RADS and the initial consensus of the LI-RADS Management Working Group reached during its deliberations in 2013. We then focus on initial discordance of LI-RADS with American Association for the Study of Liver Diseases and Organ Procurement Transplant Network guidelines, the basis for these differences, and how they are being addressed going forward to optimize reporting of CT and MRI findings in patients at risk for HCC and to increase consensus throughout the international community of physicians involved in the diagnosis and treatment of HCC.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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