Liver spontaneous hypoattenuation on CT is an imaging biomarker of the severity of acute pancreatitis

Roussey, Benjamin; Calame, Paul; Revel, Lucie; Zver, Thibaut; Konan, Amoin Georgette; Piton, Gaël; Koch, Stéphane; Vuitton, Lucine; Delabrousse, Éric

doi:10.1016/j.diii.2022.03.008

Cited by 7 publications

(3 citation statements)

References 35 publications

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“…The present study demonstrated an association between HTG-AP and the L/S ratio (Spearman’s correlation, r = −0.418), which could reflect the degree of fatty liver. Consistent with the conclusions of other researchers, [31–34] the incidence rates of SAP exhibited an upward trend with the aggravation of hepatic steatosis. As a key risk factor for fatty liver, HTG indicates an elevation of lipids in the blood, which are hydrolyzed to generate free fatty acids.…”

Section: Discussionsupporting

confidence: 92%

Prediction of severe hypertriglyceridemia-associated acute pancreatitis using a nomogram based on CT findings and blood biomarkers

Dong,

Shen,

Wang

et al. 2024

Medicine

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Hypertriglyceridemia is a common cause of acute pancreatitis (AP). Fatty liver, a manifestation of metabolic syndrome, is related to the severity of AP. The present study aimed to construct an accurate predictive model for severe AP (SAP) by combining the fatty liver infiltration on a computerized tomography (CT) scan with a series of blood biomarkers in patients with hypertriglyceridemia-associated AP (HTG-AP). A total of 213 patients diagnosed with HTG-AP were included in the present retrospective study. Clinical information and imageological findings were retrospectively analyzed. The model was constructed from independent risk factors using univariate analysis, the least absolute shrinkage and selection operator method. Subsequently, the data from the training group of 111 patients with HTG-AP was analyzed using logistic regression analysis. The efficacy of the model was verified using an external validation group of 102 patients through the receiver operating characteristic curve (ROC). Independent predictors, including serum calcium, C-reactive protein, lactate dehydrogenase and liver-to-spleen CT attenuation ratio (L/S ratio), were incorporated into the nomogram model for SAP in HTG-AP. The model achieved a sensitivity of 91.3% and a specificity of 88.6% in the training group. Compared with the Ranson model, the established nomogram model exhibited a better discriminative ability in the training group [area under the curve (AUC): 0.957] and external validation group (AUC: 0.930), as well as better calibration and clinical benefits. The present study demonstrates that the constructed nomogram based on CT findings and blood biomarkers is useful for the accurate prediction of SAP in HTG-AP.

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Section: Discussionsupporting

confidence: 92%

Prediction of severe hypertriglyceridemia-associated acute pancreatitis using a nomogram based on CT findings and blood biomarkers

Dong,

Shen,

Wang

et al. 2024

Medicine

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“…Guido's group improved the noninvasive diagnosis of chemotherapy-associated liver injury by radiomic analysis of liver parenchyma with high accuracy in diagnosing nonalcoholic steatohepatitis, sinusoidal dilatation, and nodular regenerative hyperplasia (21). Benjamin and his colleagues reported that liver spontaneous attenuation (LSA), which re ects the degree of steatosis on CT, correlates with the clinical severity of AP and that the LSA can serve as an imaging biomarker of AP severity (22). Although there are many studies on liver injury based on radiomics, there is a lack of development and veri cation of radiomic models for diagnosing AP complicated with liver injury; therefore, we constructed a CT radiomic model based on machine learning.…”

Section: Discussionmentioning

confidence: 99%

Radiomic model of contrast-enhanced computed tomographyfor predicting liver injury in acute pancreatitis patients

Liu,

Yu,

Liu

et al. 2024

Preprint

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Objectives To predict liver injury in acute pancreatitis (AP) patients by establishing a radiomics model based on contrast-enhanced computed tomography (CECT). Methods A total of 1223 radiomic features were extracted from late arterial-phase pancreatic CECT images of 209 AP patients (146 in the training cohort and 63 in the test cohort), and the optimal radiomic features retained after dimensionality reduction by least absolute shrinkage and selection operator (LASSO) were used to construct a radiomic model through logistic regression analysis. In addition, clinical features were collected to develop a clinical model, and a joint model was established by combining the best radiomic features and clinical features to evaluate the practicality and application value of the radiomic models, clinical model and combined model. Results Four potential features were selected from the pancreatic parenchyma to construct the radiomic model, and the area under the receiver operating characteristic curve (AUC) of the radiomic model was significantly greater than that of the clinical model for both the training cohort (0.993 vs. 0.653, p = 0.000) and test cohort (0.910 vs. 0.574, p = 0.000). The joint model had a greater AUC than the radiomics model for both the training cohort (0.997 vs. 0.993, p = 0.357) and test cohort (0.925 vs. 0.910, p = 0.302). Conclusions The radiomic model based on CECT has good performance in predicting liver injury in AP patients and can guide clinical decision-making and improve the prognosis of patients with AP.

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“…Tasu et al (14) found that pancreatic enhancement less than 30 Hounsfield units (HU) in portal phase CT-enhanced images was an accurate and repeatable threshold for the diagnosis of necrotizing pancreatitis (NP) and could correctly distinguish nonsevere AP from severe AP. Roussey et al (15) revealed that liver spontaneous attenuation (LSA) reflecting the degree of steatosis on CT images was related to the severity of AP, and that the median LSA in images of patients with severe AP was lower than that of patients with nonsevere AP. Liao et al (16) analyzed the correlation between readmission and reintervention by determining the pancreatic necrosis volume (PNV) and confirmed that PNV was a useful tool for quantifying pancreatic necrosis and was strongly associated with readmission and reintervention.…”

Section: Discussionmentioning

confidence: 99%

Early prediction of acute pancreatitis severity based on changes in pancreatic and peripancreatic computed tomography radiomics nomogram

Zhao¹,

Wei²,

Xiao³

et al. 2023

Quant Imaging Med Surg

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Background: Early identification of severe acute pancreatitis (SAP) is key to reducing mortality and improving prognosis. We aimed to establish a radiomics model and nomogram for early prediction of acute pancreatitis (AP) severity based on contrast-enhanced computed tomography (CT) images. Methods: We retrospectively analyzed 215 patients with first-episode AP, including 141 in the training cohort (87 men and 54 women, mean age 51.37±16.09 years) and 74 in the test cohort (40 men and 34 women, mean age 55.49±17.83 years). Radiomics features were extracted from portal venous phase images based on pancreatic and peripancreatic regions. The light gradient boosting machine (LightGBM) algorithm was used for feature selection, a logistic regression (LR) model was established and trained by 10-fold crossvalidation, and a nomogram was established based on the best features. The model's predictive performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy.Results: A total of 13 optimal radiomics features were selected by LightGBM for LR model building. The AUC of the radiomics (LR) model was 0.992 [95% confidence interval (CI): 0.963-0.996] in the training cohort, 0.965 (95% CI: 0.924-0.981) in the validation cohort, and 0.894 (95% CI: 0.789-0.966) in the test cohort. The sensitivity was 0.862 (95% CI: 0.674-0.954), the specificity was 0.800 (95% CI: 0.649-0.899), and the accuracy was 0.824 (95% CI: 0.720-0.919). The nomogram based on the 13 radiomics features showed that SAP would be predicted when the total score was greater than 124. Conclusions:The radiomics model based on enhanced-CT images of pancreatic and peripancreatic regions performed well in the early prediction of AP severity. The nomogram based on selected radiomics features could provide a reference for AP clinical assessment.

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Liver spontaneous hypoattenuation on CT is an imaging biomarker of the severity of acute pancreatitis

Cited by 7 publications

References 35 publications

Prediction of severe hypertriglyceridemia-associated acute pancreatitis using a nomogram based on CT findings and blood biomarkers

Prediction of severe hypertriglyceridemia-associated acute pancreatitis using a nomogram based on CT findings and blood biomarkers

Radiomic model of contrast-enhanced computed tomographyfor predicting liver injury in acute pancreatitis patients

Early prediction of acute pancreatitis severity based on changes in pancreatic and peripancreatic computed tomography radiomics nomogram

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