The Utility of Xenon-133 Liver Scan in the Diagnosis and Management of Nonalcoholic Fatty Liver Disease
Bibliographic record
Abstract
Nonalcoholic fatty liver disease (NAFLD) encompasses a wide spectrum of liver damage and is the most common cause of chronic liver diseases in Western countries. Although a relatively common condition affecting approximately 20% of the general population, NAFLD is especially prevalent in obese individuals, a figure likely to rise as obesity rates in Western countries continue to increase. Liver biopsy remains the gold standard diagnostic method; however, its invasive nature, among other factors, has prompted the need to develop less invasive, alternative methods to quantify hepatic fat and determine disease severity. Xenon-133 liver scanning is one such method that has been in use for more than 10 years in the evaluation of patients with suspected NAFLD. This study compared Xenon-133 liver scan with other currently used, invasive and noninvasive methods of liver assessment. BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is an important and common condition affecting approximately 20% of the general population. Given the limitation of radiological investigations, diagnosis often requires a liver biopsy. OBJECTIVE: To compare Xenon-133 (Xe-133) liver scanning with ultrasonography in the diagnosis of NAFLD. METHODS: From January 2003 to February 2007, 258 consecutive patients with suspected NAFLD underwent Xe-133 liver scanning at Royal Victoria Hospital (Montreal, Quebec). Of these, 43 patients underwent ultrasonography and liver biopsy for the evaluation of NAFLD. Patients with other liver diseases and significant alcohol consumption were excluded. Two nuclear medicine physicians assessed liver Xe-133 uptake and measured the grade of steatosis using a standardized protocol. The degree of steatosis was determined from biopsy specimens assessed by two hepatopathologists. RESULTS: NAFLD was identified by liver biopsy in 35 of 43 patients (81.4%). Xe-133 scan demonstrated 94.3% sensitivity (95% CI 81.4% to 98.4%) and 87.5% specificity (95% CI 52.9% to 99.4%) for the presence of NAFLD. The positive and negative predictive values for detection of steatosis by Xe-133 scan were 97.1% (95% CI 85.1% to 99.8%) and 77.8% (95% CI 45.3% to 93.7%), respectively. The positive and negative likelihood ratios were 7.54 (95% CI 1.20 to 47.26) and 0.07 (95% CI 0.02 to 0.26), respectively. Two patients with NAFLD (5.7%) who had a negative Xe-133 scan result had histologically mild steatosis (<10%). The grade of steatosis on liver biopsy was highly correlated with the results of the Xe-133 scan (r=0.87; P<0.001). The sensitivity and specificity of ultrasound in diagnosing steatosis were 62.9% and 75%, respectively. CONCLUSION: Xe-133 liver scan proved to be a safe, reliable, non-invasive method for diagnosing and quantifying hepatic steatosis, and was superior to ultrasound.
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.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".