Improved Siderotic Nodule Detection in Cirrhosis with Susceptibility-Weighted Magnetic Resonance Imaging: A Prospective Study
Bibliographic record
Abstract
BACKGROUND: Hepatic cirrhosis is a common pathway of progressive liver destruction from multiple causes. Iron uptake can occur within the hepatic parenchyma or within the various nodules that form in a cirrhotic liver, termed siderotic nodules. Siderotic nodule formation has been shown to correlate with inflammatory activity, and while the relationship between siderotic nodule formation and malignancy remains unclear, iron distribution within hepatic nodules has known implications for the detection of hepatocellular carcinoma. We aimed to evaluate the role of abdominal susceptibility-weighted imaging in the detection of siderotic nodules in cirrhotic patients. METHODOLOGY/PRINCIPAL FINDINGS: Forty-six (46) cirrhotic patients with at least one siderotic nodule detected on previous imaging underwent both computed tomography and magnetic resonance imaging (T1-, T2-, T2*-, and susceptibility-weighted imaging) at 3.0 Tesla. Imaging data was independently analyzed by two radiologists. Siderotic nodule count was determined for each modality and imaging sequence. For each magnetic resonance imaging technique, siderotic nodule conspicuity was assessed on a 3 point scale (1 = weak, 2 = moderate, 3 = strong). More nodules were detected by susceptibility weighted imaging (n = 2935) than any other technique, and significantly more than by T2* weighted imaging (n = 1696, p<0.0001). Lesion conspicuity was also highest with susceptibility-weighted imaging, with all nodules found to be moderate (n = 6) or strong (n = 40); a statistically significant difference (p<0.001). CONCLUSIONS: Susceptibility-weighted imaging had the greatest lesion conspicuity and detected the highest number of siderotic nodules suggesting it is the most sensitive imaging technique to detect siderotic nodules in cirrhotic patients.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".