Liver Iron Quantification with MR Imaging: A Primer for Radiologists
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Bibliographic record
Abstract
Iron overload is a systemic disorder and is either primary (genetic) or secondary (exogenous iron administration). Primary iron overload is most commonly associated with hereditary hemochromatosis and secondary iron overload with ineffective erythropoiesis (predominantly caused by β-thalassemia major and sickle cell disease) that requires long-term transfusion therapy, leading to transfusional hemosiderosis. Iron overload may lead to liver cirrhosis and hepatocellular carcinoma, in addition to cardiac and endocrine complications. The liver is one of the main iron storage organs and the first to show iron overload. Therefore, detection and quantification of liver iron overload are critical to initiate treatment and prevent complications. Liver biopsy was the historical reference standard for detection and quantification of liver iron content. Magnetic resonance (MR) imaging is now commonly used for liver iron quantification, including assessment of distribution, detection, grading, and monitoring of treatment response in iron overload. Several MR imaging techniques have been developed for iron quantification, each with advantages and limitations. The liver-to-muscle signal intensity ratio technique is simple and widely available; however, it assumes that the reference tissue is normal. Transverse magnetization (also known as R2) relaxometry is validated but is prone to respiratory motion artifacts due to a long acquisition time, is presently available only for 1.5-T imaging, and requires additional cost and delay for off-line analysis. The R2* technique has fast acquisition time, demonstrates a wide range of liver iron content, and is available for 1.5-T and 3.0-T imaging but requires additional postprocessing software. Quantitative susceptibility mapping has the highest sensitivity for detecting iron deposition; however, it is still investigational, and the correlation with liver iron content is not yet established. ©RSNA, 2018
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it