MRI and CT radiomics for the diagnosis of acute pancreatitis

Purpose

To evaluate the single and combined diagnostic performances of CT and MRI radiomics for diagnosis of acute pancreatitis (AP).

Materials and methods

We prospectively enrolled 78 patients (mean age 55.7 ± 17 years, 48.7 % male) diagnosed with AP between 2020 and 2022. Patients underwent contrast-enhanced CT (CECT) within 48-72 h of symptoms and MRI ≤ 24 h after CECT. The entire pancreas was manually segmented tridimensionally by two operators on portal venous phase (PVP) CECT images, T2-weighted imaging (WI) MR sequence and non-enhanced and PVP T1-WI MR sequences. A matched control group (n = 77) with normal pancreas was used. Dataset was randomly split into training and test, and various machine learning algorithms were compared. Receiver operating curve analysis was performed.

Results

The T2WI model exhibited significantly better diagnostic performance than CECT and non-enhanced and venous T1WI, with sensitivity, specificity and AUC of 73.3 % (95 % CI: 71.5-74.7), 80.1 % (78.2-83.2), and 0.834 (0.819-0.844) for T2WI (p = 0.001), 74.4 % (71.5-76.4), 58.7 % (56.3-61.1), and 0.654 (0.630-0.677) for non-enhanced T1WI, 62.1 % (60.1-64.2), 78.7 % (77.1-81), and 0.787 (0.771-0.810) for venous T1WI, and 66.4 % (64.8-50.9), 48.4 % (46-50.9), and 0.610 (0.586-0.626) for CECT, respectively.The combination of T2WI with CECT enhanced diagnostic performance compared to T2WI, achieving sensitivity, specificity and AUC of 81.4 % (80-80.3), 78.1 % (75.9-80.2), and 0.911 (0.902-0.920) (p = 0.001).

Conclusion

The MRI radiomics outperformed the CT radiomics model to detect diagnosis of AP and the combination of MRI with CECT showed better performance than single models. The translation of radiomics into clinical practice may improve detection of AP, particularly MRI radiomics.

Progress of silk fibroin biomaterial use in oral tissue regeneration engineering

The field of tissue engineering has introduced novel prospects for the regeneration of oral tissues, wherein stent materials assume a pivotal role and have garnered increasing attention. As a natural protein with good biocompatibility and adjustable biodegradability, an increasing number of studies focus on the uses of silk fibroin (SF) biomaterials for medical tissue regeneration engineering. Solid evidence has been found for using SF biomaterials in various oral tissue regeneration fields, from endodontics and periodontics to regenerating the maxillofacial bone. In order to provide researchers with a systematic understanding of the application of SF biomaterials to oral tissue regeneration, the present work reviews in detail the common forms of SF biomaterials for oral tissue regeneration as well as their preparation methods. In addition, the common additives used in the corresponding materials are introduced.

Radiomics-based prediction of recurrent acute pancreatitis in individuals with metabolic syndrome using T2WI magnetic resonance imaging data

Objective

This study sought to clarify the utility of T2-weighted imaging (T2WI)-based radiomics to predict the recurrence of acute pancreatitis (AP) in subjects with metabolic syndrome (MetS).

Methods

Data from 196 patients with both AP and MetS from our hospital were retrospectively analyzed. These patients were separated into two groups according to their clinical follow-up outcomes, including those with first-onset AP (n = 114) and those with recurrent AP (RAP) (n = 82). The 196 cases were randomly divided into a training set (n = 137) and a test set (n = 59) at a 7:3 ratio. The clinical characteristics of these patients were systematically compiled for further analysis. For each case, the pancreatic parenchyma was manually delineated slice by slice using 3D Slicer software, and the appropriate radiomics characteristics were retrieved. The K-best approach, the least absolute shrinkage and selection operator (LASSO) algorithm, and variance thresholding were all used in the feature selection process. The establishment of clinical, radiomics, and combined models for forecasting AP recurrence in patients with MetS was then done using a random forest classifier. Model performance was measured using the area under the receiver operating characteristic curve (AUC), and model comparison was done using the DeLong test. The clinical utility of these models was evaluated using decision curve analysis (DCA), and the optimal model was determined via a calibration curve.

Results

In the training set, the clinical, radiomics, and combined models yielded respective AUCs of 0.651, 0.825, and 0.883, with corresponding test sets of AUCs of 0.606, 0.776, and 0.878. Both the radiomics and combined models exhibited superior predictive effectiveness compared to the clinical model in both the training (p = 0.001, p < 0.001) and test sets (p = 0.04, p < 0.001). The combined model outperformed the radiomics model (training set: p = 0.025, test set: p = 0.019). The DCA demonstrated that the radiomics and combined models had greater clinical efficacy than the clinical model. The calibration curve for the combined model demonstrated good agreement between the predicted probability of AP recurrence and the observed outcomes.

Conclusion

These findings highlight the superior predictive power of a T2WI-based radiomics model for predicting AP recurrence in patients with MetS, potentially supporting early interventions that can mitigate or alleviate RAP.

Postoperative pancreatic fistula is higher in patients with necrotizing pancreatitis who develop a colon-transverse fistula

BACKGROUND

This study explores the association between the need for open necrosectomy (ON) during infected necrotizing pancreatitis (INP) treatment and the development of postoperative pancreatic fistula (POPF) following definitive surgery (DS) for transverse colonic fistulas.

MATERIALS AND METHODS

This study was conducted at two tertiary hospitals and included patients who underwent DS for colonic fistula secondary to INP from January 2009 to December 2023. Patients were followed until hospital discharge. The primary outcome was the incidence of POPF.

RESULTS

A total of 135 patients were included. The median age was 38 years (interquartile range [IQR]: 32-44 years), with 85 (62.9%) being male. ON was required in 52 patients (38.5%), with 24 patients developing POPF post-DS. The need for ON (odds ratio [OR] = 2.78, 95% confidence interval [CI]: 1.03-7.58, p = 0.040) and the interval from INP resolution to DS (OR = 0.82, 95% CI: 0.68-0.92, p = 0.011) were associated with POPF.

CONCLUSION

The need for ON during INP treatment is significantly associated with an increased risk of POPF following DS for transverse colonic fistulas.

Moving From the Background Toward the Spotlight: A Critical Review of Radiation Therapy for Locally Advanced Pancreas Cancer

Radiation therapy (RT) for locally advanced pancreatic cancer (LAPC) continues to be controversial. Advances in both systemic therapy and RT techniques have changed the landscape of LAPC management in recent years. Clinical outcomes of ablative RT have been encouraging, and randomized clinical trials may clarify the role of RT for LAPC. We present a contemporary critical review of key aspects regarding optimal patient selection, radiation dose escalation techniques, novel radiosensitizers and radioprotectors, and treatment response assessment for LAPC.

Comprehensive management of gastrointestinal fistulas in necrotizing pancreatitis: a review of diagnostic and therapeutic approaches

INTRODUCTION

Gastrointestinal fistula (GIF) is a rare but severe complication in patients with necrotizing pancreatitis (NP), significantly prolonging disease course and increasing morbidity and mortality. Its subtle and nonspecific early symptoms often delay diagnosis and intervention. Despite its clinical significance, the low incidence of GIF in NP has resulted in limited research and a lack of consensus on optimal diagnostic and therapeutic strategies.

AREAS COVERED

This review focuses on the epidemiology, pathophysiology, diagnostic approaches, and therapeutic management of GIF in NP patients. Imaging techniques, such as contrast-enhanced computed tomography and endoscopy, have been integral to early diagnosis. Advances in interventional and surgical techniques provide new avenues for treatment, but variability in clinical practice highlights the need for standardized protocols.

EXPERT OPINION

Recent advances in diagnostic imaging have improved the detection of GIF, while innovations in interventional and surgical treatments show promise. Current research is still insufficient and varied. Future research should focus on developing diagnostic methods and treatment measures for such complications. By improving early diagnosis and offering insights into effective management strategies, it is hoped that patient outcomes can be improved.

Layering hyperintensity in T1-weighted magnetic resonance imaging predicts gallbladder sludge: a retrospective cohort and diagnostic accuracy study in patients with significant liver disease

BACKGROUND

Layering hyperintensity in the gallbladder is frequently observed on T1-weighted (T1w) magnetic resonance imaging (MRI), but its association with hepatobiliary disorders is not well understood.

OBJECTIVE

This study aimed to evaluate the prevalence of T1w layering in the gallbladder and its correlation with ultrasound (US) findings and patient characteristics in a cohort with significant liver disease.

METHODS

A single-center study from 2015 to 2022 included patients who underwent MRI and abdominal US within one week. Exclusion criteria were poor imaging quality and prior cholecystectomy. MRI findings were correlated with US and analyzed against patient characteristics.

RESULTS

Among 415 patients (mean age 58.3 ± 14.8 years; mean BMI 28.0 ± 4.5 kg/m²), 67% had abnormal liver function tests, with high prevalences of cirrhosis (n = 260), transjugular intrahepatic portosystemic shunt (TIPS) (n = 233), and choledocholithiasis (n = 106). T1w layering was observed in 56% (n = 232) and associated with higher BMI (p = 0.001) and with cholecystolithiasis (p < 0.001), but not with age, sex, or liver disease indicators. T1w layering was predictive of gallbladder sludge on US (odds ratio 17.2, 95% confidence interval 9.87-31.44, p < 0.001), with a sensitivity of 92.7% but moderate specificity (57.9%).

CONCLUSION

T1w layering on MRI strongly predicts gallbladder sludge detected on US and is associated with increased BMI in this cohort of patients with liver disease. However, the moderate specificity requires cautious interpretation, and our findings suggest that T1w layering may serve as a complementary diagnostic tool.

Deep learning radiomics nomogram for preoperatively identifying moderate-to-severe chronic cholangitis in children with pancreaticobiliary maljunction: a multicenter study

BACKGROUND

Long-term severe cholangitis can lead to dense adhesions and increased fragility of the bile duct, complicating surgical procedures and elevating operative risk in children with pancreaticobiliary maljunction (PBM). Consequently, preoperative diagnosis of moderate-to-severe chronic cholangitis is essential for guiding treatment strategies and surgical planning. This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) based on contrast-enhanced CT images and clinical characteristics to preoperatively identify moderate-to-severe chronic cholangitis in children with PBM.

METHODS

A total of 323 pediatric patients with PBM who underwent surgery were retrospectively enrolled from three centers, and divided into a training cohort (n = 153), an internal validation cohort (IVC, n = 67), and two external test cohorts (ETC1, n = 58; ETC2, n = 45). Chronic cholangitis severity was determined by postoperative pathology. Handcrafted radiomics features and deep learning (DL) radiomics features, extracted using transfer learning with the ResNet50 architecture, were obtained from portal venous-phase CT images. Multivariable logistic regression was used to establish the DLRN, integrating significant clinical factors with handcrafted and DL radiomics signatures. The diagnostic performances were evaluated in terms of discrimination, calibration, and clinical usefulness.

RESULTS

Biliary stones and peribiliary fluid collection were selected as important clinical factors. 5 handcrafted and 5 DL features were retained to build the two radiomics signatures, respectively. The integrated DLRN achieved satisfactory performance, achieving area under the curve (AUC) values of 0.913 (95% CI, 0.834-0.993), 0.916 (95% CI, 0.845-0.987), and 0.895 (95% CI, 0.801-0.989) in the IVC, and two ETCs, respectively. In comparison, the clinical model, handcrafted signature, and DL signature had AUC ranges of 0.654-0.705, 0.823-0.857, and 0.840-0.872 across the same cohorts. The DLRN outperformed single-modality clinical, handcrafted radiomics, and DL radiomics models, with all integrated discrimination improvement values > 0 and P < 0.05. The Hosmer-Lemeshow test and calibration curves showed good consistency of the DLRN (P > 0.05), and the decision curve analysis and clinical impact curve further confirmed its clinical utility.

CONCLUSIONS

The integrated DLRN can be a useful and non-invasive tool for preoperatively identifying moderate-to-severe chronic cholangitis in children with PBM, potentially enhancing clinical decision-making and personalized management strategies.

Acute respiratory distress syndrome (ARDS): from mechanistic insights to therapeutic strategies

Acute respiratory distress syndrome (ARDS) is a clinical syndrome of acute hypoxic respiratory failure caused by diffuse lung inflammation and edema. ARDS can be precipitated by intrapulmonary factors or extrapulmonary factors, which can lead to severe hypoxemia. Patients suffering from ARDS have high mortality rates, including a 28-day mortality rate of 34.8% and an overall in-hospital mortality rate of 40.0%. The pathophysiology of ARDS is complex and involves the activation and dysregulation of multiple overlapping and interacting pathways of systemic inflammation and coagulation, including the respiratory system, circulatory system, and immune system. In general, the treatment of inflammatory injuries is a coordinated process that involves the downregulation of proinflammatory pathways and the upregulation of anti-inflammatory pathways. Given the complexity of the underlying disease, treatment needs to be tailored to the problem. Hence, we discuss the pathogenesis and treatment methods of affected organs, including 2019 coronavirus disease (COVID-19)-related pneumonia, drowning, trauma, blood transfusion, severe acute pancreatitis, and sepsis. This review is intended to provide a new perspective concerning ARDS and offer novel insight into future therapeutic interventions.

A systematic review of machine learning-based prognostic models for acute pancreatitis: Towards improving methods and reporting quality

BACKGROUND

An accurate prognostic tool is essential to aid clinical decision-making (e.g., patient triage) and to advance personalized medicine. However, such a prognostic tool is lacking for acute pancreatitis (AP). Increasingly machine learning (ML) techniques are being used to develop high-performing prognostic models in AP. However, methodologic and reporting quality has received little attention. High-quality reporting and study methodology are critical for model validity, reproducibility, and clinical implementation. In collaboration with content experts in ML methodology, we performed a systematic review critically appraising the quality of methodology and reporting of recently published ML AP prognostic models.

METHODS/FINDINGS

Using a validated search strategy, we identified ML AP studies from the databases MEDLINE and EMBASE published between January 2021 and December 2023. We also searched pre-print servers medRxiv, bioRxiv, and arXiv for pre-prints registered between January 2021 and December 2023. Eligibility criteria included all retrospective or prospective studies that developed or validated new or existing ML models in patients with AP that predicted an outcome following an episode of AP. Meta-analysis was considered if there was homogeneity in the study design and in the type of outcome predicted. For risk of bias (ROB) assessment, we used the Prediction Model Risk of Bias Assessment Tool. Quality of reporting was assessed using the Transparent Reporting of a Multivariable Prediction Model of Individual Prognosis or Diagnosis-Artificial Intelligence (TRIPOD+AI) statement that defines standards for 27 items that should be reported in publications using ML prognostic models. The search strategy identified 6,480 publications of which 30 met the eligibility criteria. Studies originated from China (22), the United States (4), and other (4). All 30 studies developed a new ML model and none sought to validate an existing ML model, producing a total of 39 new ML models. AP severity (23/39) or mortality (6/39) were the most common outcomes predicted. The mean area under the curve for all models and endpoints was 0.91 (SD 0.08). The ROB was high for at least one domain in all 39 models, particularly for the analysis domain (37/39 models). Steps were not taken to minimize over-optimistic model performance in 27/39 models. Due to heterogeneity in the study design and in how the outcomes were defined and determined, meta-analysis was not performed. Studies reported on only 15/27 items from TRIPOD+AI standards, with only 7/30 justifying sample size and 13/30 assessing data quality. Other reporting deficiencies included omissions regarding human-AI interaction (28/30), handling low-quality or incomplete data in practice (27/30), sharing analytical codes (25/30), study protocols (25/30), and reporting source data (19/30).

CONCLUSIONS

There are significant deficiencies in the methodology and reporting of recently published ML based prognostic models in AP patients. These undermine the validity, reproducibility, and implementation of these prognostic models despite their promise of superior predictive accuracy.

REGISTRATION

Research Registry (reviewregistry1727).

Advances in the clinical application of machine learning in acute pancreatitis: a review

Traditional disease prediction models and scoring systems for acute pancreatitis (AP) are often inadequate in providing concise, reliable, and effective predictions regarding disease progression and prognosis. As a novel interdisciplinary field within artificial intelligence (AI), machine learning (ML) is increasingly being applied to various aspects of AP, including severity assessment, complications, recurrence rates, organ dysfunction, and the timing of surgical intervention. This review focuses on recent advancements in the application of ML models in the context of AP.

Artificial intelligence models assisting physicians in quantifying pancreatic necrosis in acute pancreatitis

Background

Acute pancreatitis (AP) is a potentially life-threatening condition characterized by inflammation of the pancreas, which can lead to complications such as pancreatic necrosis. The modified computed tomography severity index (MCTSI) is a widely used tool for assessing the severity of AP, particularly the extent of pancreatic necrosis. The accurate and timely assessment of the necrosis volume is crucial in guiding treatment decisions and improving patient outcomes. However, the current diagnostic process relies heavily on the manual interpretation of computed tomography (CT) scans, which can be subjective and prone to variability among clinicians. This study aimed to develop a deep-learning network model to assist clinicians in diagnosing the volume ratio of pancreatic necrosis based on the MCTSI for AP.

Methods

The datasets comprised retrospectively collected plain and contrast-enhanced CT scans from 144 patients (6 with scores of 0 points, 42 with scores of 2 points, and 65 with scores of 4 points) and the National Institutes of Health contrast-enhanced CT scans from 45 patients with scores of 0 points. An improved fully convolutional neural networks for volumetric medical image segmentation (V-Net) model was developed to segment the pancreatic volume (i.e., the whole pancreas, necrotic pancreatic tissue, and non-necrotic pancreatic tissue) and to quantify the split volume ratios. The improved strategy included three stages of body up- and down-sampling adapted to the task of segmentation in AP, and the selection of objects, loss function, and smoothing coefficients. The model interpretations were compared with those of clinicians with different levels of experience. The reference standard was manually segmented by a pancreatic radiologist. Accuracy, macro recall, and macro specificity were employed to compare the diagnostic efficacy of the model and the clinicians.

Results

In total, 144 patients (mean age: 44±13 years; 40 females, 104 males) were included in the study. Optimal training results were obtained using the necrotic pancreatic tissue and whole pancreas as the input objects, and combining dice loss and 500 smoothing coefficients as the loss function for training. The dice coefficient for the whole pancreas was 0.811 and that for the necrotic pancreatic tissue was 0.761. The performance of the artificial intelligence model and clinicians were compared. The accuracy, macro recall, and macro specificity of the improved V-net were 0.854, 0.850 and 0.923, respectively, which were all significantly higher than those of the senior and junior clinicians (P<0.05).

Conclusions

Our proposed model could improve the effectiveness of clinicians in diagnosing pancreatic necrosis volume ratios in clinical settings.

AI-powered innovations in pancreatitis imaging: a comprehensive literature synthesis

Early identification of pancreatitis remains a significant clinical diagnostic challenge that impacts patient outcomes. The evolution of quantitative imaging followed by deep learning models has shown great promise in the non-invasive diagnosis of pancreatitis and its complications. We provide an overview of advancements in diagnostic imaging and quantitative imaging methods along with the evolution of artificial intelligence (AI). In this article, we review the current and future states of methodology and limitations of AI in improving clinical support in the context of early detection and management of pancreatitis.

Acute Necrotizing Pancreatitis Leading to Hemosuccus Pancreaticus and Hemorrhagic Shock in the Setting of Decompensated Cirrhosis

Hemosuccus pancreaticus (HP) is a rare, life-threatening cause of upper gastrointestinal bleeding, often linked to chronic pancreatitis and pseudoaneurysm rupture into the pancreatic duct. However, its occurrence in acute necrotizing pancreatitis with decompensated cirrhosis is exceedingly rare and poses significant diagnostic and treatment challenges. We report a case of a 34-year-old male with decompensated alcoholic cirrhosis who developed hemorrhagic shock from HP following acute necrotizing pancreatitis. The initial imaging revealed a pancreatic tail hematoma and a splenic artery pseudoaneurysm, that was later found to have ruptured into the pancreatic duct, causing intermittent GI bleeding. Endoscopy showed clots extruding from the ampulla, and angiography confirmed active bleeding, leading to endovascular coil embolization. Despite intervention, the patient's coagulopathy and hemodynamic instability, related to his cirrhosis, worsened, ultimately resulting in death under comfort care. This case underscores the importance of considering HP in patients with pancreatic disease and unexplained GI bleeding, especially in the presence of pseudoaneurysms, as timely endovascular or surgical management, coupled with a multidisciplinary approach, is essential to improve outcomes.

Prediction of acute pancreatitis severity based on early CT radiomics

BACKGROUND

This study aims to develop and validate an integrated predictive model combining CT radiomics and clinical parameters for early assessment of acute pancreatitis severity.

METHODS

A retrospective cohort of 246 patients with acute pancreatitis was analyzed, with a 70%-30% split for training and validation groups. CT image segmentation was performed using ITK-SNAP, followed by the extraction of radiomics features. The stability of the radiomics features was assessed through inter-observer Intraclass Correlation Coefficient analysis. Feature selection was carried out using univariate analysis and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation. A radiomics model was constructed through logistic regression to compute the radiomics score. Concurrently, univariate and multivariate logistic regression were employed to identify independent clinical risk factors for the clinical model. The radiomics score and clinical variables were integrated into a combined model, which was visualized with a nomogram. Model performance and net clinical benefit were evaluated through the area under the receiver operating characteristic curve (AUC), the DeLong test, and decision curve analysis.

RESULTS

A total of 913 radiomics features demonstrated satisfactory consistency. Eight features were selected for the radiomics model. Serum calcium, C-reactive protein, and white blood cell count were identified as independent clinical predictors. The AUC of the radiomics model was 0.871 (95% CI, 0.793-0.949) in the training cohort and 0.859 (95% CI, 0.751-0.967) in the validation cohort. The clinical model achieved AUCs of 0.833 (95% CI, 0.756-0.910) and 0.810 (95% CI, 0.692-0.929) for the training and validation cohorts, respectively. The combined model outperformed both the radiomics and clinical models, with an AUC of 0.905 (95% CI, 0.837-0.973) in the training cohort and 0.908 (95% CI, 0.824-0.992) in the validation cohort. The DeLong test confirmed superior predictive performance of the combined model over both the radiomics and clinical models in the training cohort, and over the clinical model in the validation cohort. Decision curve analysis further demonstrated that the combined model provided greater net clinical benefit than the radiomics or clinical models alone.

CONCLUSION

The clinical-radiomics model offers a novel tool for the early prediction of acute pancreatitis severity, providing valuable support for clinical decision-making.

Clinical Impact of Pancreatic and Peripancreatic Hemorrhage Associated With Acute Pancreatitis

Purpose

The significance of pancreatitis-associated hemorrhage outside the context of a ruptured pseudoaneurysm remains unclear. This study aims to characterize the clinical significance of pancreatic hemorrhage during acute pancreatitis (AP).

Methods

This retrospective study included adult patients diagnosed with hemorrhagic pancreatitis (HP) from 2010 to 2021. HP was defined as a clinical diagnosis of AP and the presence of pancreatic or peripancreatic hemorrhage on cross-sectional imaging. Two radiologists assessed the pancreatitis type, degree of necrosis, hemorrhage location, peripancreatic collections, and peripancreatic vessels. Demographic and disease data, AP severity, and treatment decisions from admission to 3 months after discharge were extracted from hospital electronic health records.

Results

The study included 36 patients, stratified by AP severity into 12 (33.3%) mild, 13 (36.1%) moderate-severe, and 11 (30.6%) severe cases. Six (16.6%) of the patients experienced clinically significant bleeding, which led to changes in clinical management such as further imaging, modifications to anticoagulation regimens, or both. Among these, 50% (3 of 6) demonstrated active bleeding on further imaging, with 33% (2 of 6) of the bleeding being intrapancreatic. In contrast, 83% (30 of 36) of HP patients did not have clinically significant bleeding, and all but one did not require changes in clinical management. AP-associated splanchnic vein thrombosis occurred in 30.6% (11 of 36) of patients, and anticoagulation in these patients did not result in clinically significant bleeding.

Conclusions

HP without clinically significant bleeding does not necessitate changes in clinical management. However, hemorrhage may indicate more severe disease and is associated with a higher incidence of splanchnic vein thrombosis.

The Clinical Value of Serum Creatinine-to-Bilirubin Ratio in Predicting the Severity and Prognosis of Acute Pancreatitis

INTRODUCTION

Bilirubin (BIL) and creatinine (Cr) have long been recognized as potential early indicators of disease severity. A recent study found that the Cr-to-BIL ratio (CTR) was more sensitive and specific than either serum Cr or BIL alone. Our research focused on the clinical significance of CTR in evaluating the severity and prognosticating outcomes of acute pancreatitis (AP) in patients.

METHODS

Patients diagnosed with AP at the First Affiliated Hospital of Guangdong Pharmaceutical University between July 1, 2016, and December 31, 2020 were included. The analysis then focused on examining the relationship between CTR levels and the severity of the illness, the occurrence of complications, and the prognosticating outcomes for individuals diagnosed with AP. A total of 286 AP patients were enrolled.

RESULTS

Multivariate regression analyses indicated that AP patients with elevated CTR levels were more likely to develop severe AP. They exhibited higher MODS, Ranson, and APACHE-II scores, an increased incidence of organ failures (acute heart failure [AHF], acute kidney injury [AKI], and acute myocardial infarction), higher 30-day all-cause mortality rates, and a worse prognosis, often requiring more frequent use of vasoactive and diuretic agents compared to those with lower CTR levels. When CTR >14.05, AP patients had increased occurrence of AHF and AKI, higher 30-day all-cause mortality rates, more frequently using vasoactive agent and diuretic agent. Besides, the disease severity scores (MODS, Ranson, and APACHE-II) and hospital stays were markedly increased.

CONCLUSION

AP patients with elevated CTR levels are prone to more severe disease progression, increased complications, and poorer outcomes compared to those with lower CTR levels.

Early prediction of acute pancreatitis with acute kidney injury using abdominal contrast-enhanced CT features

Early prediction of acute pancreatitis (AP) with acute kidney injury (AKI) using abdominal contrast-enhanced CT could effectively reduce the mortality and the economic burden on patients and society. However, this challenge is limited by the imaging manifestations of early-stage AP that are not clearly visible to the naked eye. To address this, we developed a machine learning model using imperceptible variations in the structural changes of pancreas and peripancreatic region, extracted by radiomics and artificial intelligence technology, to screen and stratify the high-risk AP patients at the early stage of AP. The results demonstrate that the machine learning model could screen the high-risk AP with AKI patients with an area under the curve (AUC) of 0.82 for the external cohort, superior to the human radiologists. This finding confirms the significant potential of machine learning in the screening of acute pancreatitis and contributes to personalized treatment and management for AP patients.

Radiomics and 256-slice-dual-energy CT in the automated diagnosis of mild acute pancreatitis: the innovation of formal methods and high-resolution CT

INTRODUCTION

Acute pancreatitis (AP) is a common disease, and several scores aim to assess its prognosis. Our study aims to automatically recognize mild AP from computed tomography (CT) images in patients with acute abdominal pain but uncertain diagnosis from clinical and serological data through Radiomic model based on formal methods (FMs).

METHODS

We retrospectively reviewed the CT scans acquired with Dual Source 256-slice CT scanner (Somatom Definition Flash; Siemens Healthineers, Erlangen, Germany) of 80 patients admitted to the radiology unit of Antonio Cardarelli hospital (Naples) with acute abdominal pain. Patients were divided into 2 groups: 40 underwent showed a healthy pancreatic gland, and 40 affected by four different grades (CTSI 0, 1, 2, 3) of mild pancreatitis at CT without clear clinical presentation or biochemical findings. Segmentation was manually performed. Radiologists identified 6 patients with a high expression of diseases (CTSI 3) to formulate a formal property (Rule) to detect AP in the testing set automatically. Once the rule was formulated, and Model Checker classified 70 patients into "healthy" or "unhealthy".

RESULTS

The model achieved: accuracy 81%, precision 78% and recall 81%. Combining FMs results with radiologists agreement, and applying the mode in clinical practice, the global accuracy would have been 100%.

CONCLUSIONS

Our model was reliable to automatically detect mild AP at primary diagnosis even in uncertain presentation and it will be tested prospectively in clinical practice.

Clinical Outcomes of Diabetes Mellitus on Moderately Severe Acute Pancreatitis and Severe Acute Pancreatitis

Objective

To analyze the influence of diabetes mellitus on the clinical outcomes of moderately severe acute pancreatitis (MSAP) and severe acute pancreatitis (SAP).

Methods

This retrospective study included patients diagnosed with MSAP and SAP at Shanxi Bethune Hospital from January 1, 2017, to December 31, 2021. Clinical data were collected, including patient demographics, 24-hour laboratory indicators, and inflammation indices. Propensity score matching (PSM) was used to compare outcomes before and after matching. Patients were randomized into training and validation sets (7:3) to develop and validate a clinical prediction model for infected pancreatic necrosis (IPN).

Results

Among 421 patients, 79 had diabetes at admission. Before PSM, diabetic patients had higher incidences of peripancreatic fluid (71% vs 47%, p<0.001) and IPN (48% vs 10%, p<0.001), higher surgical intervention rates (24% vs 12%, p=0.008), and significant differences in abdominocentesis (22% vs 11%, p=0.014). After PSM, 174 patients were matched, and the diabetes group still showed higher incidences of peripancreatic fluid (69% vs 47%, p=0.008), IPN (48% vs 11%, p<0.001), and surgical intervention rates (27% vs 13%, p=0.037). Diabetes, modified CT severity index (MCTSI), serum calcium, and HDL-c were identified as independent risk factors for IPN. The prediction model demonstrated good predictive value.

Conclusion

In MSAP and SAP patients, diabetes mellitus can exert an influence on their clinical outcome and is an independent risk factor for IPN. The alignment diagram and web calculator constructed on the basis of diabetes mellitus, modified CT severity index (MCTSI), serum calcium and high-density lipoprotein cholesterol (HDL-c) have good predictive value and clinical guidance for the occurrence of IPN in MSAP and SAP.

Association between triglyceride glucose-body mass index and all-cause mortality in critically ill patients with acute pancreatitis

This study delves into the correlation between the triglyceride glucose-body mass index (TyG-BMI) index upon hospital admission and clinical outcomes among this patient population. We investigated the association between TyG-BMI at hospital admission and clinical outcomes in this patient group, and analyzed data from the Medical Information Mart for Intensive Care IV database, identifying acute pancreatitis (AP) patients admitted to ICUs and stratifying them by TyG-BMI quartiles. We assessed the relationship between TyG-BMI and mortality (both in-hospital and ICU) using Cox proportional hazards regression and restricted cubic splines. The cohort included 419 patients, average age 56.34 ± 16.62 years, with a majority being male (61.58%). Hospital and ICU mortality rates were 11.93% and 7.16%, respectively. Higher TyG-BMI was positively correlated with increased all-cause mortality. Patients in the highest TyG-BMI quartile had significantly greater risks of in-hospital and ICU mortality. An S-shaped curve in the spline analysis indicated a threshold effect at a TyG-BMI of 243 for increased in-hospital mortality risk. TyG-BMI is a reliable predictor of both in-hospital and ICU mortality in severely ill AP patients, suggesting its utility in enhancing risk assessment and guiding clinical interventions for this vulnerable population.

Comparison of endoscopic retrograde cholangiopancreatography with laparoscopic surgery for patients with mild and moderately severe acute biliary pancreatitis

Acute biliary pancreatitis (ABP) is an acute inflammatory reaction that occurs as a result of abnormal reflux of bile into the pancreatic duct, which activates pancreatic digestive enzymes to produce pancreatic auto-digestion.

Objectives

To explore the advantages of Endoscopic Retrograde Cholangiopancreatography (ERCP) treatment compared with laparoscopic surgery in the management of patients with mild and moderately severe ABP, and to study the risk factors for recurrence of ABP and construct a risk prediction model to assist in resolving clinical decision-making and improving prognosis.

Methods

Patients with mild and moderately severe ABP treated at General Hospital of Ningxia Medical University from January 1, 2019 to July 1, 2022 were reviewed. A total of 327 patients were enrolled according to the inclusion criteria and exclusion criteria. According to the different treatment modalities, they were divided into the group treated via ERCP (n = 239) and the group treated via laparoscopic surgery (n = 88). Statistical analyses were performed to compare the differences between the average levels of preoperative and postoperative blood routine and blood biochemical indexes, as well as the time of recovery from clinical symptoms, length of hospital stay, and postoperative complications between the two groups of patients. The 280 patients who participated in the follow-up were divided into the recurrence group (n = 130) and the non-recurrence group (n = 150) according to whether they had recurrence or not. Independent samples t-test and binary logistic regression were used to analyze the causative monofactors and risk factors of recurrent biliary pancreatitis, and then to construct the model and assess the predictive accuracy of the model.

Results

On postoperative day 2, the incidence of local complications, Balthazar CT score, and the number of analgesia were lower in the patients in the group treated by ERCP than in the group treated by laparoscopic surgery (P < 0.001), and the duration of antibiotics, enzyme-suppressing medication, fasting, and hospital stay were shorter in the patients in the group treated by ERCP than in the group treated by laparoscopic surgery (P < 0.001). Personal history, gamma glutamyl transpeptidase (GGT), and treatment modality are risk factors for recurrence of biliary pancreatitis. The model constructed by combining GGT, personal history, and treatment modality had the best predictive ability for disease recurrence compared with the model with GGT, personal history, and treatment modality alone (area under the ROC curve 0.815).

Conclusion

Compared with the laparoscopic surgery group, ERCP treatment can effectively relieve symptoms and restore gastrointestinal function in advance in patients with ABP, and reduce hospitalisation time and related complications. Personal history, GGT, and treatment modality are risk factors for recurrence of biliary pancreatitis. Patients can prevent recurrence by abstaining from smoking and alcohol, eating a healthy diet, and exercising appropriately.

Hallmarks of perineural invasion in pancreatic ductal adenocarcinoma: new biological dimensions

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignant tumor with a high metastatic potential. Perineural invasion (PNI) occurs in the early stages of PDAC with a high incidence rate and is directly associated with a poor prognosis. It involves close interaction among PDAC cells, nerves and the tumor microenvironment. In this review, we detailed discuss PNI-related pain, six specific steps of PNI, and treatment of PDAC with PNI and emphasize the importance of novel technologies for further investigation.

Chronic liver disease is an important risk factor for worse outcomes in acute pancreatitis: a systematic review and meta-analysis

Chronic liver diseases (CLD) affect 1.5 billion patients worldwide, with dramatically increasing incidence in recent decades. It has been hypothesized that the chronic hyperinflammation associated with CLD may increase the risk of a more severe course of acute pancreatitis (AP). This study aims to investigate the underlying impact of CLD on the outcomes of AP. A systematic search was conducted in Embase, Medline, and Central databases until October 2022. Studies investigating patients with acute pancreatitis and CLD, were included in the meta-analysis. A total of 14,963 articles were screened, of which 36 were eligible to be included. CLD was a risk factor for increased mortality with an odds ratio (OR) of 2.53 (CI 1.30 to 4.93, p = 0.01). Furthermore, renal, cardiac, and respiratory failures were more common in the CLD group, with ORs of 1.92 (CI 1.3 to 2.83, p = 0.01), 2.11 (CI 0.93 to 4.77, p = 0.062) and 1.99 (CI 1.08 to 3.65, p = 0.033), respectively. Moreover, the likelihood of developing Systemic Inflammatory Response Syndrome (SIRS) was significantly higher, with an OR of 1.95 (CI 1.03 to 3.68, p = 0.042). CLD is an important risk factor for worse outcomes in AP pancreatitis, leading to higher mortality and increased rates of local and systemic complications.

Comprehensive Review of Surgical and Radiological Management of Hemorrhagic Pancreatitis: Current Strategies and Outcomes

Hemorrhagic pancreatitis, a severe complication of acute and chronic pancreatitis, involves bleeding due to vascular disruptions. This condition presents significant clinical challenges and is associated with high morbidity and mortality. The bleeding can result from arterial or venous complications, often exacerbated by inflammatory and enzymatic damage to blood vessels within the pancreas. Patients with hemorrhagic pancreatitis may experience symptoms such as abdominal pain, nausea, vomiting, and gastrointestinal bleeding. Diagnostic imaging, including CT and MRI, is crucial in identifying the source of bleeding and guiding treatment decisions. Management strategies have evolved over the past two decades, shifting from purely surgical approaches to including interventional radiology techniques. Surgical intervention is often reserved for hemodynamically unstable patients or those with large pseudoaneurysms, offering definitive treatment but carrying higher risks of complications. Endovascular techniques, such as transcatheter embolization, provide a less invasive alternative with high success rates and shorter recovery times, though rebleeding may occur. Treatment choice depends on various factors, including the patient's stability, the size and location of the bleeding, and the availability of specialized expertise. Overall, the management of hemorrhagic pancreatitis requires a multidisciplinary approach, combining surgical and radiological techniques to optimize patient outcomes and reduce the risk of mortality. Long-term follow-up is essential to monitor for recurrent disease and manage the metabolic consequences of pancreatic insufficiency.

Consensus guidelines on the diagnosis and treatment of pancreatic pseudocyst and walled-off necrosis from a Chinese multiple disciplinary team expert panel

Objective

To prepare a set of practice guidelines to standardize the entire process, from diagnosis to treatment and follow-up, for pancreatic pseudocysts and walled-off necrosis.

Methods

Thirty-six experts in the fields of digestive endoscopy, pancreatic surgery, interventional radiology, and others presented their opinions via discussions in online conferences by referring to the patient, intervention, comparison, and outcomes principles and then reviewed the evidence and statements using the Delphi method to reach a consensus. The consensus of >80% was finally achieved for the items.

Results

The experts discussed and reached a consensus on 29 statements including 10 categories: (1) definition and classification, (2) imaging and endoscopic diagnosis, (3) therapeutic implications, (4) surgical therapy, (5) percutaneous catheter drainage, (6) endoscopic retrograde cholangiopancreatography, (7) EUS-guided drainage, (8) stent selection for EUS-guided drainage, (9) complication related to stents for cyst drainage, and (10) drug treatment and follow-up.

Conclusion

This consensus based on the clinical experience of experts in various fields and international evidence-based medicine further standardizes the multidisciplinary diagnosis and treatment processes for pancreatic pseudocysts and walled-off necrosis.

Reversal of Fatty Liver With Regression of Acute Necrotizing Pancreatitis: A Rare Case

Acute pancreatitis is a severe inflammatory condition that can lead to systemic repercussions, one of which is the development of hepatic steatosis (fatty liver). The accumulation of fat in liver cells can complicate the course of pancreatitis, exacerbating inflammation and causing additional metabolic disturbances. The presence of fatty liver in the context of acute pancreatitis can thus worsen the overall clinical picture, making management more challenging and potentially leading to further complications. Here, we discuss a rare case of a 34-year-old female who demonstrated the reversal of fatty liver following the improvement of acute pancreatitis. This case highlights the dynamic relationship between acute pancreatitis and hepatic steatosis, illustrating that effective management of pancreatitis can lead to significant improvements in associated conditions such as fatty liver.

Radiomics models of contrast-enhanced computed tomography for predicting the activity and prognosis of acute pancreatitis

BACKGROUND

The modified pancreatitis activity scoring system (mPASS) was proposed to assess the activity of acute pancreatitis (AP) while it doesn't include indicators that directly reflect pathophysiology processes and imaging characteristics.

OBJECTIVES

To determine the threshold of admission mPASS and investigate radiomics and laboratory parameters to construct a model to predict the activity of AP.

METHODS

AP inpatients at institution 1 were randomly divided into training and validation groups based on a 5:5 ratio. AP inpatients at Institution 2 were served as test group. The cutoff value of admission mPASS scores in predicting severe AP was selected to divide patients into high and low level of disease activity group. LASSO was used in screening features. Multivariable logistic regression was used to develop radiomics model. Meaningful laboratory parameters were used to construct combined model.

RESULTS

There were 234 (48 years ± 10, 155 men) and 101 (48 years ± 11, 69 men) patients in two institutions. The threshold of admission mPASS score was 112.5 in severe AP prediction. The AUC of the radiomics model was 0.79, 0.72, and 0.76 and that of the combined model incorporating rad-score and white blood cell were 0.84, 0.77, and 0.80 in three groups for activity prediction. The AUC of the combined model in predicting disease without remission was 0.74.

CONCLUSIONS

The threshold of admission mPASS was 112.5 in predicting severe AP. The model based on CECT radiomics has the ability to predict AP activity. Its ability to predict disease without remission is comparable to mPASS.

CRITICAL RELEVANCE STATEMENT

This work is the first attempt to assess the activity of acute pancreatitis using contrast-enhanced CT radiomics and laboratory parameters. The model provides a new method to predict the activity and prognosis of AP, which could contribute to further management.

KEY POINTS

Radiomics features and laboratory parameters are associated with the activity of acute pancreatitis. The combined model provides a new method to predict the activity and prognosis of AP. The ability of the combined model is comparable to the modified Pancreatitis Activity Scoring System.

Clinical Effect of the Traditional Japanese Herbal Medicine "Goreisan" on Water Balance in Patients With Severe Acute Pancreatitis

BACKGROUND

Since severe acute pancreatitis (SAP) involves inflammatory mediators produced by local inflammation of the pancreas that trigger a systemic inflammatory response, intensive fluid management is required to maintain hemodynamics in the early stages of the onset of SAP. Goreisan is considered to have a diuretic effect in a state of excess water and an antidiuretic effect in a state of dehydration, regulating water balance in both directions. We investigated the clinical effects of Goreisan on water balance in SAP patients. Patients and methods: SAP patients admitted to our ICU within 72 hours of being diagnosed with SAP were divided into two groups: the Rikkunshito group (before October 2015) and the Goreisan group (after November 2015). Cumulative volume of fluid infusion, urine, fluid removal by CHF, nasogastric tube drainage, and water balance from day 1 to day 5 of ICU admission.

RESULTS

Thirty patients were included. The median age was 57 (40-69) years, and 21/30 (70%) were male. The prognostic factor score in Japanese criteria for acute pancreatitis was 5.5 (3.3-7). Of the thirty patients, 14 were in the Rikkunshito group, and 16 were in the Goreisan group. There were no differences in the cumulative volume of fluid infusion, urine, fluid removal by CHF, or nasogastric tube drainage from day 1 to day 5 of ICU admission between the two groups. However, the cumulative water balance from day 1 to day 5 of admission was 4,957 ± 6,091 mL in the Rikkunshito group, whereas it was lower in the Goreisan group at 498 ± 3,918 mL (P = 0.023).

CONCLUSION

Our study showed that Goreisan administration in patients with severe acute pancreatitis might improve water balance in the early phase of onset. Early administration of Goreisan at the onset of severe acute pancreatitis may regulate fluid movement between capillaries and interstitium and alleviate fluid overload due to water refill.

Development and validation of an interpretable delta radiomics-based model for predicting invasive ground-glass nodules in lung adenocarcinoma: a retrospective cohort study

Background

Radiomics models based on computed tomography (CT) can be used to differentiate invasive ground-glass nodules (GGNs) in lung adenocarcinoma to help determine the optimal timing of GGN resection, improve the accuracy of prognostic prediction, and reduce unnecessary surgeries. However, general radiomics does not fully utilize follow-up data and often lacks model interpretation. Therefore, this study aimed to build an interpretable model based on delta radiomics to predict GGN invasiveness.

Methods

A retrospective analysis was conducted on a set of 303 GGNs that were surgically resected and confirmed as lung adenocarcinoma in Shanghai Chest Hospital between September 2017 and August 2022. Delta radiomics and general radiomics features were extracted from preoperative follow-up CT scans and combined with clinical features for modeling. The performance of the delta radiomics-clinical model was compared to that of the radiomics-clinical model. Additionally, Shapley additive explanations (SHAP) was employed to interpret and visualize the model.

Results

Two models were constructed using a combination of 34 radiomic features and 10 delta radiomic features, along with 14 clinical features. The radiomics-clinical model and the delta radiomics-clinical model exhibited area under the curve (AUC) of 0.986 [95% confidence interval (CI): 0.977-0.995] and 0.974 (95% CI: 0.959-0.987) in the training set, respectively, and 0.949 (95% CI: 0.908-0.978) and 0.927 (95% CI: 0.879-0.966) in the test set, respectively. The DeLong test of the two models showed no statistical significance (P=0.10) in the test set. SHAP was used to output a summary plot for global interpretation, which showed that preoperative mass, three-dimensional (3D) length, mean diameter, volume, mean CT value, and delta radiomics feature original_firstorder_RootMeanSquared were the relatively more important features in the model. Waterfall plots for local interpretation showed how each feature contributed to the prediction output of a given GGN.

Conclusions

The delta radiomics-based model proved to be a helpful tool for predicting the invasiveness of GGNs in lung adenocarcinoma. This approach offers a precise, noninvasive alternative in informing clinical decision-making. Additionally, SHAP provided insightful and user-friendly interpretations and visualizations of the model, enhancing its clinical applicability.

Identification and analysis of chemokine-related and NETosis-related genes in acute pancreatitis to develop a predictive model

Background: Chemokines and NETosis are significant contributors to the inflammatory response, yet there still needs to be a more comprehensive understanding regarding the specific molecular characteristics and interactions of NETosis and chemokines in the context of acute pancreatitis (AP) and severe AP (SAP). Methods: To address this gap, the mRNA expression profile dataset GSE194331 was utilized for analysis, comprising 87 AP samples (77 non-SAP and 10 SAP) and 32 healthy control samples. Enrichment analyses were conducted for differentially expressed chemokine-related genes (DECRGs) and NETosis-related genes (DENRGs). Three machine-learning algorithms were used for the identification of signature genes, which were subsequently utilized in the development and validation of nomogram diagnostic models for the prediction of AP and SAP. Furthermore, single-gene Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed. Lastly, an interaction network for the identified signature genes was constructed. Results: We identified 12 DECRGs and 7 DENRGs, and enrichment analyses indicated they were primarily enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and T cell receptor signaling pathway. Moreover, these machine learning algorithms finally recognized three signature genes (S100A8, AIF1, and IL18). Utilizing the identified signature genes, we developed nomogram models with high predictive accuracy for AP and differentiation of SAP from non-SAP, as demonstrated by area under the curve (AUC) values of 0.968 (95% CI 0.937-0.990) and 0.862 (95% CI 0.742-0.955), respectively, in receiver operating characteristic (ROC) curve analysis. Subsequent single-gene GESA and GSVA indicated a significant positive correlation between these signature genes and the proteasome complex. At the same time, a negative association was observed with the Th1 and Th2 cell differentiation signaling pathways. Conclusion: We have identified three genes (S100A8, AIF1, and IL18) related to chemokines and NETosis, and have developed accurate diagnostic models that might provide a novel method for diagnosing AP and differentiating between severe and non-severe cases.

Predictive value of machine learning for the severity of acute pancreatitis: A systematic review and meta-analysis

Background

Predicting the severity of acute pancreatitis (AP) early poses a challenge in clinical practice. While there are well-established clinical scoring tools, their actual predictive performance remains uncertain. Various studies have explored the application of machine-learning methods for early AP prediction. However, a more comprehensive evidence-based assessment is needed to determine their predictive accuracy. Hence, this systematic review and meta-analysis aimed to evaluate the predictive accuracy of machine learning in assessing the severity of AP.

Methods

PubMed, EMBASE, Cochrane Library, and Web of Science were systematically searched until December 5, 2023. The risk of bias in eligible studies was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Subgroup analyses, based on different machine learning types, were performed. Additionally, the predictive accuracy of mainstream scoring tools was summarized.

Results

This systematic review ultimately included 33 original studies. The pooled c-index in both the training and validation sets was 0.87 (95 % CI: 0.84-0.89) and 0.88 (95 % CI: 0.86-0.90), respectively. The sensitivity in the training set was 0.81 (95 % CI: 0.77-0.84), and in the validation set, it was 0.79 (95 % CI: 0.71-0.85). The specificity in the training set was 0.84 (95 % CI: 0.78-0.89), and in the validation set, it was 0.90 (95 % CI: 0.86-0.93). The primary model incorporated was logistic regression; however, its predictive accuracy was found to be inferior to that of neural networks, random forests, and xgboost. The pooled c-index of the APACHE II, BISAP, and Ranson were 0.74 (95 % CI: 0.68-0.80), 0.77 (95 % CI: 0.70-0.85), and 0.74 (95 % CI: 0.68-0.79), respectively.

Conclusions

Machine learning demonstrates excellent accuracy in predicting the severity of AP, providing a reference for updating or developing a straightforward clinical prediction tool.

Revolutionizing the Pancreatic Tumor Diagnosis: Emerging Trends in Imaging Technologies: A Systematic Review

Background and Objectives: The pancreas, ensconced within the abdominal cavity, requires a plethora of sophisticated imaging modalities for its comprehensive evaluation, with ultrasonography serving as a primary investigative technique. A myriad of pancreatic pathologies, encompassing pancreatic neoplasia and a spectrum of inflammatory diseases, are detectable through these imaging strategies. Nevertheless, the intricate anatomical confluence and the pancreas's deep-seated topography render the visualization and accurate diagnosis of its pathologies a formidable endeavor. The objective of our paper is to review the best diagnostic imagistic tools for the pancreas. Materials and Methods: we have gathered several articles using Prisma guidelines to determine the best imagistic methods. The imperative of pancreatic scanning transcends its diagnostic utility, proving to be a pivotal element in a multitude of clinical specialties, notably surgical oncology. Within this domain, multidetector computed tomography (MDCT) of the pancreas holds the distinction of being the paramount imaging modality, endorsed for its unrivaled capacity to delineate the staging and progression of pancreatic carcinoma. In synergy with MDCT, there has been a notable advent of avant-garde imaging techniques in recent years. These advanced methodologies, including ultrasonography, endoscopic ultrasonography, contrast-enhanced ultrasonography, and magnetic resonance imaging (MRI) conjoined with magnetic resonance cholangiopancreatography (MRCP), have broadened the horizon of tumor characterization, offering unparalleled depth and precision in oncological assessment. Other emerging diagnostic techniques, such as elastography, also hold a lot of potential and promise for the future of pancreatic imaging. Fine needle aspiration (FNA) is a quick, minimally invasive procedure to evaluate lumps using a thin needle to extract tissue for analysis. It is less invasive than surgical biopsies and usually performed as an outpatient with quick recovery. Its accuracy depends on sample quality, and the risks include minimal bleeding or discomfort. Results, guiding further treatment, are typically available within a week. Elastography is a non-invasive medical imaging technique that maps the elastic properties and stiffness of soft tissue. This method, often used in conjunction with ultrasound or MRI, helps differentiate between hard and soft areas in tissue, providing valuable diagnostic information. It is particularly useful for assessing liver fibrosis, thyroid nodules, breast lumps, and musculoskeletal conditions. The technique is painless and involves applying gentle pressure to the area being examined. The resulting images show tissue stiffness, indicating potential abnormalities. Elastography is advantageous for its ability to detect diseases in early stages and monitor treatment effectiveness. The procedure is quick, safe, and requires no special preparation, with results typically available immediately. Results: The assembled and gathered data shows the efficacy of various techniques in discerning the nature and extent of neoplastic lesions within the pancreas. Conclusions: The most common imaging modalities currently used in diagnosing pancreatic neoplasms are multidetector computed tomography (MDCT), endoscopic ultrasound (EUS), and magnetic resonance imaging (MRI), alongside new technologies, such as elastography.

Association between serum creatinine to albumin ratio and short- and long-term all-cause mortality in patients with acute pancreatitis admitted to the intensive care unit: a retrospective analysis based on the MIMIC-IV database

Background

Serum creatinine (Cr) and albumin (Alb) are important predictors of mortality in individuals with various diseases, including acute pancreatitis (AP). However, most previous studies have only examined the relationship between single Cr or Alb levels and the prognosis of patients with AP. To our knowledge, the association between short- and long-term all-cause mortality in patients with AP and the blood creatinine to albumin ratio (CAR) has not been investigated. Therefore, this study aimed to evaluate the short- and long-term relationships between CAR and all-cause mortality in patients with AP.

Methods

We conducted a retrospective study utilizing data from the Medical Information Market for Intensive Care (MIMIC-IV) database. The study involved analyzing various mortality variables and obtaining CAR values at the time of admission. The X-tile software was used to determine the optimal threshold for the CAR. Kaplan-Meier (K-M) survival curves and multivariate Cox proportional hazards regression models were used to assess the relationship between CAR and both short- and long-term all-cause mortality. The predictive power, sensitivity, specificity, and area under the curve (AUC) of CAR for short- and long-term mortality in patients with AP after hospital admission were investigated using Receiver Operating Characteristic analysis. Additionally, subgroup analyses were conducted.

Results

A total of 520 participants were included in this study. The CAR ideal threshold, determined by X-tile software, was 0.446. The Cox proportional hazards model revealed an independent association between CAR≥0.446 and all-cause mortality at 7-day (d), 14-d, 21-d, 28-d, 90-d, and 1-year (y) before and after adjustment for confounders. K-M survival curves showed that patients with CAR≥0.446 had lower survival rates at 7-d, 14-d, 21-d, 28-d, 90-d, and 1-y. Additionally, CAR demonstrated superior performance, with higher AUC values than Cr, Alb, serum total calcium, Glasgow Coma Scale, Systemic Inflammatory Response Syndrome score, and Sepsis-related Organ Failure Assessment score at 7-d, 14-d, 21-d, 28-d, 90-d, and 1-y intervals. Subgroup analyses showed that CAR did not interact with a majority of subgroups.

Conclusion

The CAR can serve as an independent predictor for short- and long-term all-cause mortality in patients with AP. This study enhances our understanding of the association between serum-based biomarkers and the prognosis of patients with AP.

Impact of Imaging Techniques in the Assessment of Gallstone Pancreatitis

From the category of biliary disease, gallstones registered an increase during the last years, approximately 6% of men and 9% of women being affected by the pathology in the United States only. In western countries between 10-20% of the adult population is suffering from cholelithiasis. Although increasing age is a major risk factor for their formation, late studies correlate gallstones appearance with an age decrease for the onset of symptoms. We therefore face a younger population manifesting pain and sometimes functional disability. In accordance with statistical analysis, the economic impact of gallstones in highly industrialized countries such as United States produces costs of up to 6.5 billion dollars annually. In this context, the appropriate timing for intervention becomes a factor of major interest. The present review uses 28 articles and specialized literature. Article selection was based on keywords and followed the effectiveness of imaging investigation such as ultrasound, CT and MRI for patients diagnosed with cholelithiasis. Since a direct comparison between the imaging investigation techniques is not concluding we have tried to establish the sensitivity and specificity offered by each imaging assessment. The comparative analysis revealed a p Kruskal-Wallis <0.001 for sensitivity and p Kruskal-Wallis=0.474 for specificity.

Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography

In the early diagnostic workup of acute pancreatitis (AP), the role of contrast-enhanced CT is to establish the diagnosis in uncertain cases, assess severity, and detect potential complications like necrosis, fluid collections, bleeding or portal vein thrombosis. The value of texture analysis/radiomics of medical images has rapidly increased during the past decade, and the main focus has been on oncological imaging and tumor classification. Previous studies assessed the value of radiomics for differentiating between malignancies and inflammatory diseases of the pancreas as well as for prediction of AP severity. The aim of our study was to evaluate an automatic machine learning model for AP detection using radiomics analysis. Patients with abdominal pain and contrast-enhanced CT of the abdomen in an emergency setting were retrospectively included in this single-center study. The pancreas was automatically segmented using TotalSegmentator and radiomics features were extracted using PyRadiomics. We performed unsupervised hierarchical clustering and applied the random-forest based Boruta model to select the most important radiomics features. Important features and lipase levels were included in a logistic regression model with AP as the dependent variable. The model was established in a training cohort using fivefold cross-validation and applied to the test cohort (80/20 split). From a total of 1012 patients, 137 patients with AP and 138 patients without AP were included in the final study cohort. Feature selection confirmed 28 important features (mainly shape and first-order features) for the differentiation between AP and controls. The logistic regression model showed excellent diagnostic accuracy of radiomics features for the detection of AP, with an area under the curve (AUC) of 0.932. Using lipase levels only, an AUC of 0.946 was observed. Using both radiomics features and lipase levels, we showed an excellent AUC of 0.933 for the detection of AP. Automated segmentation of the pancreas and consecutive radiomics analysis almost achieved the high diagnostic accuracy of lipase levels, a well-established predictor of AP, and might be considered an additional diagnostic tool in unclear cases. This study provides scientific evidence that automated image analysis of the pancreas achieves comparable diagnostic accuracy to lipase levels and might therefore be used in the future in the rapidly growing era of AI-based image analysis.

Pancreatic neuroendocrine tumour resection in circumportal pancreas: a rare anatomical anomaly with important surgical implications

Various congenital anomalies of the pancreas have been reported due to its complex embryological development involving the fusion of two separate buds. Circumportal pancreas is a rare anatomical anomaly where the pancreatic head and uncinate process fuse abnormally with the pancreatic body, encasing the portal vein and/or superior mesenteric vein completely. This anomaly poses several challenges to hepatobiliary surgeons, as the encasement of the portal vein by the abnormal pancreatic tissue makes an additional parenchymal transection necessary. Vascular variants have also been reported with circumportal pancreas, which, if not recognised preoperatively, can be catastrophic. Therefore, careful preoperative evaluation and planning are essential, to ensure safe pancreatic resection and recovery in a patient with circumportal pancreas. We present a case of a successful subtotal pancreatectomy and splenectomy in a patient with circumportal pancreas, for a suspected pancreatic duct adenocarcinoma. The aim of this case report is to contribute valuable insights that can aid hepatobiliary surgeons in enhancing their preoperative planning when encountered with patients with similar anatomical variances.

Periampullary cancer and neurological interactions: current understanding and future research directions

Periampullary cancer is a malignant tumor occurring around the ampullary region of the liver and pancreas, encompassing a variety of tissue types and sharing numerous biological characteristics, including interactions with the nervous system. The nervous system plays a crucial role in regulating organ development, maintaining physiological equilibrium, and ensuring life process plasticity, a role that is equally pivotal in oncology. Investigations into nerve-tumor interactions have unveiled their key part in controlling cancer progression, inhibiting anti-tumor immune responses, facilitating invasion and metastasis, and triggering neuropathic pain. Despite many mechanisms by which nerve fibers contribute to cancer advancement still being incompletely understood, the growing emphasis on the significance of nerves within the tumor microenvironment in recent years has set the stage for the development of groundbreaking therapies. This includes combining current neuroactive medications with established therapeutic protocols. This review centers on the mechanisms of Periampullary cancer's interactions with nerves, the influence of various types of nerve innervation on cancer evolution, and outlines the horizons for ongoing and forthcoming research.

Magnetic resonance imaging in the evaluation and management of acute pancreatitis: a review of current practices and future directions

Acute pancreatitis is a condition marked by inflammation of the pancreas and surrounding tissues. While the majority of cases of acute pancreatitis are mild, a minority of severe cases are the primary contributors to the morbidity and mortality attributed to this condition. Retroperitoneal morphologic changes can be detected by utilization of various imaging modalities, and their accurate evaluation is crucial for effective management. Acute pancreatitis is commonly diagnosed using computed tomography (CT). However, there are certain clinical scenarios where magnetic resonance imaging (MRI) may have superiority over CT. In particular, MRI is useful in cases where patients cannot receive iodinated CT contrast, or where there is a need to investigate the underlying cause of acute pancreatitis. Additionally, MRI can be utilized to evaluate ductal disconnection and guide interventions for necrotic collections. The unique features of MRI can be particularly useful, including its ability to provide superior contrast resolution and to offer greater functional information through techniques such as diffusion-weighted imaging. The aim of this review is to discuss the MRI assessment of individuals with acute pancreatitis. Additionally, the recent advances in MRI for evaluation of acute pancreatitis will also be introduced.

Acute Pancreatitis and Type 2 Diabetes Mellitus: The Chicken-Egg Paradox-A Seven-Year Experience of a Large Tertiary Center

(1) Background: Preexisting type 2 diabetes mellitus (T2DM) has been shown in some studies as a risk factor and a severity factor for acute pancreatitis (AP). In this study, we aimed to demonstrate the link between T2DM and AP using data from a large retrospective epidemiological registry in a tertiary center. (2) Methods: We conducted a retrospective, large-cohort study of 1855 cases of AP and recurrent AP drawn from the seven-year consecutive hospitalization electronic health records of the largest acute-care tertiary teaching center in Romania. (3) Results: We observed a significant association between T2DM and a more severe course of the disease, and between T2DM and admission to the intensive care unit (ICU) due to AP, in our cohort using a chi-square test. However, we did not see a meaningful difference in comparing LoS-ICU between T2DM-AP and OAP (other known cause of AP). AP patients with T2DM had a greater probability of a severe course of the disease and were more likely to be admitted to the ICU than to the OAP. (4) Conclusions: The association between T2DM and AP remains a topic very representative of the "chicken-egg paradox". We need further research on DM-related AP and their bidirectional association as our study is limited by its retrospective design.

Quantitative CT features on admission combined with laboratory biomarkers for predicting severe acute pancreatitis

AIM

To assess the association of quantitative computed tomography (CT) features on admission with acute pancreatitis (AP) severity, and to explore the performance of combined CT and laboratory markers for predicting severe AP (SAP).

MATERIALS AND METHODS

Data from 208 AP patients were reviewed retrospectively. Pancreas volume, the area of extrapancreatic inflammation, extrapancreatic fluid collection volume, and number were calculated based on CT images on admission. Laboratory biomarkers within 24 h of admission were collected. Interobserver agreement for CT measurements was measured by calculating interclass correlation coefficient (ICC). The associations of quantitative CT features with AP severity were evaluated. Predictive models for SAP were constructed based on CT and laboratory markers. Performances of single marker and the models were evaluated using receiver operating characteristic (ROC) curve and area under the ROC curve (AUC).

RESULTS

Pancreas volume, area of extrapancreatic inflammation, extrapancreatic fluid collection volume, and number were significantly different between severe and non-severe AP groups. In predicting SAP, the AUCs of quantitative CT indicators ranged from 0.72 to 0.79; the AUCs of laboratory biomarkers were between 0.53 and 0.66. The combined model of area of extrapancreatic inflammation, serum calcium, and haematocrit yielded an AUC of 0.84, significantly higher than that of the laboratory model, single CT, or laboratory marker. Interobserver agreements for quantitative CT indicators were excellent, with ICC ranging from 0.91 to 0.98.

CONCLUSION

Quantitative CT features on admission were significantly associated with AP severity; the combination of extrapancreatic inflammation area, serum calcium, and haematocrit could be taken as a new method for predicting SAP.

Complementary comments on diagnosis, severity and prognosis prediction of acute pancreatitis

The radiological differential diagnosis of acute pancreatitis includes diffuse pancreatic lymphoma, diffuse autoimmune pancreatitis and groove located mass lesions that may mimic groove pancreatitis. Dual energy computed tomography and diffusion weighted magnetic resonance imaging are useful in the early diagnosis of acute pancreatitis, and dual energy computed tomography is also useful in severity assessment and prognosis prediction. Walled off necrosis is an important complication in terms of prognosis, and it is important to know its radiological findings and distinguish it from pseudocyst.

The Society for Pediatric Radiology Magnetic Resonance Imaging and Emergency and Trauma Imaging Committees' consensus protocol recommendation for rapid MRI for evaluating suspected appendicitis in children

The imaging evaluation of acute abdominal pain in children with suspected appendicitis has evolved to include rapid abdominopelvic MRI (rMRI) over recent years. Through a collaborative effort between the Magnetic Resonance Imaging (MRI) and Emergency and Trauma Imaging Committees of the Society for Pediatric Radiology (SPR), we conducted a survey on the utilization of rMRI to assess practice specifics and protocols. Subsequently, we present a proposed consensus rMRI protocol derived from the survey results, literature review, and discussion and consensus between committee members.

Magnetic resonance imaging for acute pancreatitis in type 2 diabetes patients

Type 2 diabetes mellitus (T2DM) and its complications have significantly increased the burden of mortality and disability globally, making diabetes one of the most dangerous and prevalent chronic diseases. Acute pancreatitis (AP) is one of the most frequent gastrointestinal causes for hospital admission, which is a common exocrine pancreatic inflammatory disease that can cause severe abdominal pain and multiple organ dysfunction. There is an inseparable relationship between AP and diabetes. Diabetes is a high risk factor of AP, and patients with AP can develop pancreatogenic diabetes. In T2DM patients, the incidence rate of AP is significantly higher than that of the general population, and the clinical symptoms are more severe, with the majority of cases being moderate to severe AP. This review briefly introduces the pathogenesis and clinical features of AP in T2DM patients, focusing on the magnetic resonance imaging (MRI) manifestations of AP in T2DM patients. Our aim is to evaluate the severity of AP in patients with T2DM by MRI, so as to help clinicians assess the patient's condition and prognosis.

Predicting acute pancreatitis severity with enhanced computed tomography scans using convolutional neural networks

This study aimed to evaluate acute pancreatitis (AP) severity using convolutional neural network (CNN) models with enhanced computed tomography (CT) scans. Three-dimensional DenseNet CNN models were developed and trained using the enhanced CT scans labeled with two severity assessment methods: the computed tomography severity index (CTSI) and Atlanta classification. Each labeling method was used independently for model training and validation. Model performance was evaluated using confusion matrices, areas under the receiver operating characteristic curve (AUC-ROC), accuracy, precision, recall, F1 score, and respective macro-average metrics. A total of 1,798 enhanced CT scans met the inclusion criteria were included in this study. The dataset was randomly divided into a training dataset (n = 1618) and a test dataset (n = 180) with a ratio of 9:1. The DenseNet model demonstrated promising predictions for both CTSI and Atlanta classification-labeled CT scans, with accuracy greater than 0.7 and AUC-ROC greater than 0.8. Specifically, when trained with CT scans labeled using CTSI, the DenseNet model achieved good performance, with a macro-average F1 score of 0.835 and a macro-average AUC-ROC of 0.980. The findings of this study affirm the feasibility of employing CNN models to predict the severity of AP using enhanced CT scans.

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.

A Rare Case of a Pancreatic Intraductal Oncocytic Papillary Neoplasm Associated With Invasive Adenocarcinoma Presenting as a Gastric Mass

The World Health Organization recently recognized intraductal oncocytic papillary neoplasms of the pancreas (IOPNs) as distinct, pre-malignant pancreatic neoplasms. Due to their unique macroscopic and microscopic features, IOPNs are typically easy to diagnose and yield an indolent prognostic outcome. The diagnosis may be more complicated, and the prognosis may differ if an associated invasive carcinoma is present. Owing to the rarity of this entity, the available data is severely limited. Herein, we report a diagnostically challenging case of an IOPN associated with invasive carcinoma, initially presenting as a gastric mass with distinctive radiological and histopathological features.

Relation between triglycerides and the severity of acute pancreatitis combined with nonalcoholic fatty liver disease: a retrospective study

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) can exacerbate the severity of acute pancreatitis (AP), and this severity is worsened with increased severity of NAFLD. This study aimed to investigate the relation between serum triglyceride (TG) and the severity of AP with NAFLD by collecting clinical data from AP patients with NAFLD.

METHODS

AP patients with NAFLD were divided into 2 groups according to TG levels: hypertriglyceridemia (HTG) group and non-hypertriglyceridemia (NHTG) group.

RESULTS

In total, 598 AP patients with NAFLD were enrolled in this study, including 433 in the HTG group and 165 in the NHTG group. Compared with the NHTG group, AP patients in the HTG group were more serious (P < 0.05). The incidence of persistent organ failure (POF), especially persistent respiratory failure, and the ratio of acute peripancreatic fluid collection (APFC) were higher in the HTG group (P < 0.05). Higher TG levels were associated with a higher incidence of APFC (P < 0.05). Logistic regression analysis showed that the risk of APFC was significantly higher in moderate and severe NAFLD than in mild NAFLD.

CONCLUSION

HTG may aggravate the severity and local complications of AP combined with NAFLD.