Role of multi-detector computed tomography in severity assessment of cases of acute pancreatitis

Acute pancreatitis: A review of diagnosis, severity prediction and prognosis assessment from imaging technology, scoring system and artificial intelligence

Acute pancreatitis (AP) is a potentially life-threatening inflammatory disease of the pancreas, with clinical management determined by the severity of the disease. Diagnosis, severity prediction, and prognosis assessment of AP typically involve the use of imaging technologies, such as computed tomography, magnetic resonance imaging, and ultrasound, and scoring systems, including Ranson, Acute Physiology and Chronic Health Evaluation II, and Bedside Index for Severity in AP scores. Computed tomography is considered the gold standard imaging modality for AP due to its high sensitivity and specificity, while magnetic resonance imaging and ultrasound can provide additional information on biliary obstruction and vascular complications. Scoring systems utilize clinical and laboratory parameters to classify AP patients into mild, moderate, or severe categories, guiding treatment decisions, such as intensive care unit admission, early enteral feeding, and antibiotic use. Despite the central role of imaging technologies and scoring systems in AP management, these methods have limitations in terms of accuracy, reproducibility, practicality and economics. Recent advancements of artificial intelligence (AI) provide new opportunities to enhance their performance by analyzing vast amounts of clinical and imaging data. AI algorithms can analyze large amounts of clinical and imaging data, identify scoring system patterns, and predict the clinical course of disease. AI-based models have shown promising results in predicting the severity and mortality of AP, but further validation and standardization are required before widespread clinical application. In addition, understanding the correlation between these three technologies will aid in developing new methods that can accurately, sensitively, and specifically be used in the diagnosis, severity prediction, and prognosis assessment of AP through complementary advantages.

Feasibility of using software-aided selection of virtual monoenergetic level for optimal image quality of acute necrotising pancreatitis based on dual-energy computed tomography: a preliminary study

OBJECTIVE

This study aimed to assess the feasibility of software-aided selection of monoenergetic level for acute necrotising pancreatitis (ANP) depiction compared to other automatic image series generated using dual-energy computed tomography (CT).

METHODS

The contrast-enhanced dual-source dual-energy CT images in the portal venous phase of 48 patients with ANP were retrospectively analysed. Contrast-to-noise ratio (CNR) of pancreatic parenchyma-to-necrosis, signal-to-noise ratio (SNR) of the pancreas, image noise, and score of subjective diagnosis were measured, calculated, and compared among the CT images of 100 kV, Sn140 kV, weighted-average 120 kV, and optimal single-energy level for CNR.

RESULTS

CNR of pancreatic parenchyma-to-necrosis in the images of 100 kV, Sn140 kV, weighted-average 120 kV, and the optimal single-energy level for CNR was 5.18 ± 2.39, 3.13 ± 1.35, 5.69 ± 2.35, and 9.99 ± 5.86, respectively; SNR of the pancreas in each group was 6.31 ± 2.77, 4.27 ± 1.56, 7.21 ± 2.69, and 11.83 ± 6.30, respectively; image noise in each group was 18.78 ± 5.20, 17.79 ± 4.63, 13.28 ± 3.13, and 9.31 ± 2.96, respectively; and score of subjective diagnosis in each group was 3.56 ± 0.50, 3.00 ± 0.55, 3.48 ± 0.55, and 3.88 ± 0.33, respectively. The four measurements of the optimal single-energy level for CNR images were significantly different from those of images in the other three groups (P < 0.05). CNR of pancreatic parenchyma-to-necrosis, SNR of the pancreas, and score of subjective diagnosis in the images of the optimal single-energy level for CNR were significantly higher, while the image noise was lower than those in the other three groups (all P = 0.000).

CONCLUSION

Optimal single-energy level imaging for CNR of dual-source CT could improve quality of CT images in patients with ANP, enhancing the display of necrosis in the pancreas.

Evaluation of Inflammatory Infiltration in the Retroperitoneal Space of Acute Pancreatitis Using Computer Tomography and Its Correlation with Clinical Severity

This paper investigates the correlation between the degree and severity of CT inflammatory infiltration in the retroperitoneal space of acute pancreatitis (AP). A total of 113 patients were included based on diagnostic criteria. The general data of the patients and the relationship between the computed tomography severity index (CTSI) and pleural effusion (PE), involvement, degree of inflammatory infiltration of retroperitoneal space (RPS), number of peripancreatic effusion sites, and degree of pancreatic necrosis on contrast-enhanced CT at different times were studied. The results showed that the mean age of onset in females was later than that in males; 62 cases involved RPS to varying degrees, with a positive rate of 54.9% (62/113), and the total involvement rates of only the anterior pararenal space (APS); both APS and perirenal space (PS); and APS, PS, and posterior pararenal space (PPS) were 46.9% (53/113), 53.1% (60/113), and 17.7% (20/113), respectively. The degree of inflammatory infiltration in the RPS worsened with the increase in CTSI score; the incidence of PE was higher in the group greater than 48 hours than in the group less than 48 hours; necrosis >50% grade was predominant (43.2%) 5 to 6 days after onset, with a higher detection rate than other time periods (P < 0.05). Thus, when the PPS was involved, the patient's condition can be treated as severe acute pancreatitis (SAP); the higher the degree of inflammatory infiltration in the retroperitoneum, the higher the severity of AP. Enhanced CT examination 5 to 6 days after onset in patients with AP revealed the greatest extent of pancreatic necrosis.