Advances in Pancreatic Imaging: The Expanding Role of Dual-Energy CT in Clinical Diagnosis: A Comprehensive Review
Bibliographic record
Abstract
Dual-energy computed tomography has become a pivotal tool in abdominal imaging, particularly for pancreatic pathologies such as pancreatic ductal adenocarcinoma, trauma assessment, and acute pancreatitis. Its ability to provide enhanced contrast resolution, reduce artifacts, and optimize radiation dose makes it invaluable in both acute and non-acute clinical settings. This narrative review summarizes the technological advancements and clinical applications of dual-energy computed tomography in pancreatic imaging. A comprehensive review of 21 peer-reviewed studies published between 2013 and 2024 was conducted to evaluate the role of dual-energy computed tomography in all pancreatic imaging indications, including tumor detection, pancreatitis assessment, trauma evaluation, and radiation dose optimization. The analysis included retrospective and prospective studies retrieved from multiple databases, including PubMed, Scopus, and Google Scholar. The findings highlight the technology's capacity to improve diagnostic accuracy, reduce image artifacts, and lower radiation exposure through techniques such as virtual monoenergetic imaging and iodine quantification. Comparisons with conventional computed tomography focused on diagnostic performance metrics such as contrast-to-noise ratio, and signal-to-noise ratio. Additionally, this narrative review underscores the clinical relevance of dual-energy computed tomography in evaluating non-traumatic acute abdominal conditions, especially among elderly patients.
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How this classification was reachedexpand
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".