View adaptive unified self-supervised technique for abdominal organ segmentation.

Automatic abdominal organ segmentation is an essential prerequisite for accurate volumetric analysis, disease diagnosis, and tracking by medical practitioners. However, the deformable shapes, variable locations, overlapping with nearby organs, and similar contrast make the segmentation challenging. Moreover, the requirement of a large manually labeled dataset makes it harder. Hence, a semi-supervised contrastive learning approach is utilized to perform the automatic abdominal organ segmentation. Existing 3D deep learning models based on contrastive learning are not able to capture the 3D context of medical volumetric data along three planes/views: axial, sagittal, and coronal views. In this work, a semi-supervised view-adaptive unified model (VAU-model) is proposed to make the 3D deep learning model as view-adaptive to learn 3D context along each view in a unified manner. This method utilizes the novel optimization function that assists the 3D model to learn the 3D context of volumetric medical data along each view in a single model. The effectiveness of the proposed approach is validated on the three types of datasets: BTCV, NIH, and MSD quantitatively and qualitatively. The results demonstrate that the VAU model achieves an average Dice score of 81.61% which is a 3.89% improvement compared to the previous best results for pancreas segmentation in multi-organ dataset BTCV. It also achieves an average Dice score of 77.76% and 76.76% for the pancreas under the single organ non-pathological NIH dataset, and pathological MSD dataset.

The feasibility of point shear wave elastography (pSWE) in the assessment of pancreas stiffness in diabetic patients and healthy volunteers.

Type 1 diabetes mellitus (T1DM) is an autoimmune disease characterized by the dysfunctional metabolism of carbohydrates, fats, and proteins caused by impaired insulin secretion and insulin resistance. This study investigated the feasibility of using point shear wave elastography (pSWE) of the pancreas by comparing the shear wave velocity (SWV) measurements of three anatomical areas in patients with T1DM and healthy volunteers. This study included 30 patients with T1DM (9 male, 21 female) and 23 healthy controls (11 men, 12 women). Two experienced certified operators performed the examinations and took the SWV measurements. The mean SWV of the entire pancreas parenchyma differed significantly between patients and controls (1.1 ± 0.29 and 0.74 ± 0.19 m/s, respectively; p ≤ 0.001). Moreover, the SWVs of the pancreatic segments were significantly different in patients and controls; the mean SWV values of the pancreas head, body, and tail (respectively) in patients vs. controls were 0.99 ± 0.36 vs. 0.76 ± 0.26 m/s (p = 0.012), 1.1 ± 0.52 vs. 0.74 ± 0.23 (p ≤ 0.001), and 1.0 ± 0.34 vs. 0.73 ± 0.28 (p ≤ 0.001). This study confirmed the feasibility of quantifying pancreas tissue stiffness with pSWE and revealed that patients with T1DM had higher pancreas tissue stiffness than controls. Further studies are required to determine the potential value of pSWE as a screening tool in patients with prediabetes.

Risk Prediction of Type 2 Diabetes Mellitus by MRI-based Pancreatic Morphology and Clinical Characteristics: A Cross-sectional Study.

OBJECTIVES: This study aimed to investigate the pancreatic morphology and clinical characteristics to predict risk factors of type 2 diabetes mellitus (T2DM) based on magnetic resonance imaging. METHODS: A total of 89 patients (T2DM group) and 68 healthy controls (HC group) were included. The T2DM group was divided into a long-term T2DM group and a short-term T2DM group according to whether the illness duration was more than 5 years. The clinical characteristics were collected, including sex, age, fasting plasma glucose, glycosylated hemoglobin, and lipoproteins. The pancreatic morphological characteristics, including the diameters of the pancreatic head, neck, body, and tail, the angle of the pancreaticobiliary junction (APJ), and the types of pancreaticobiliary junction were measured. The risk prediction model was established by logistic regression analysis. RESULTS: In the long-term T2DM group, the pancreatic diameters were smaller than the other two groups. In the short-term T2DM group, the diameters of the pancreatic tail and body were smaller than the HC group. The APJ, very low-density lipoprotein, and triglyceride levels in the two T2DM groups were greater than the HC group, and the APJ of the short-term T2DM group was smaller than the long-term T2DM group. Pancreatic diameters showed a negative correlation with illness duration. Logistic regression analysis revealed pancreatic body diameter was a protective factor, and APJ was a risk factor for T2DM. Prediction model accuracy was 90.20%. CONCLUSIONS: The morphology of the pancreas is helpful to predict the risk of the onset of T2DM. The risk of onset of T2DM increases with smaller pancreatic body diameter and higher APJ.

Tissue characterization using axicon probe-assisted common-path optical coherence tomography.

In this work, a common-path optical coherence tomography (OCT) system is demonstrated for characterizing the tissue in terms of some optical properties. A negative axicon structure chemically etched inside the fiber tip is employed as optical probe in the OCT. This probe generates a quality Bessel beam owning a large depth-of-field, ∼700 µm and small central spot size, ∼3 µm. The OCT system is probing the sample without using any microscopic lens. For experimental validation, the OCT imaging of chicken tissue has been obtained along with estimation of its refractive index and optical attenuation coefficient. Afterwards, the cancerous tissue is differentiated from the normal tissue based on the OCT imaging, refractive index, and optical attenuation coefficient. The respective tissue samples are collected from the human liver and pancreas. This probe could be a useful tool for endoscopic or minimal-invasive inspection of malignancy inside the tissue either at early-stage or during surgery.

Contrast-enhanced guided endoscopic ultrasound procedures.

Contrast-enhanced endoscopic ultrasound (CH-EUS) can overcome the limitations of endoscopic ultrasound-guided acquisition by identifying microvessels inside inhomogeneous tumours and improving the characterization of these tumours. Despite the initial enthusiasm that oriented needle sampling under CH-EUS guidance could provide better diagnostic yield in pancreatic solid lesions, further studies did not confirm the supplementary values in cases of tissue acquisition guided by CH-EUS. This review details the knowledge based on the available data on contrast-guided procedures. The indications for CH-EUS tissue acquisition include isoechoic EUS lesions with poor visible delineation where CH-EUS can differentiate the lesion vascularisation from the surrounding parenchyma and also the mural nodules within biliopancreatic cystic lesions, which occur in select cases. Additionally, the roles of CH-EUS-guided therapy in patients whose pancreatic fluid collections or bile ducts that have an echogenic content have indications for drainage, and patients who have nonvisualized vessels that need to be highlighted via Doppler EUS are presented. Another indication is represented if there is a need for an immediate assessment of the post-radiofrequency ablation of pancreatic neuroendocrine tumours, in which case CH-EUS can be used to reveal the incomplete tumour destruction.

The Incidental Pancreatic Cyst: When to Worry About Cancer.

Incidental pancreatic cystic lesions are a common challenge encountered by diagnostic radiologists. Specifically, given the prevalence of benign pancreatic cystic lesions, determining when to recommend aggressive actions such as surgical resection or endoscopic ultrasound with sampling is difficult. In this article, we review the common types of cystic pancreatic lesions including serous cystadenoma, intraductal papillary mucinous neoplasm, and mucinous cystic neoplasm with imaging examples of each. We also discuss high-risk or worrisome imaging features that warrant a referral to a surgeon or endoscopist and provid several examples of these features. These imaging features adhere to the latest guidelines from the International Consensus Guidelines, American Gastroenterological Association (2015), American College of Gastroenterology (2018), American College of Radiology (2010, 2017), and European Guidelines (2013, 2018). Our focused article addresses the imaging dilemma of managing incidental cystic pancreatic lesions, weighing the options between imaging follow-up and aggressive interventions.

Quality-driven deep cross-supervised learning network for semi-supervised medical image segmentation.

Semi-supervised medical image segmentation presents a compelling approach to streamline large-scale image analysis, alleviating annotation burdens while maintaining comparable performance. Despite recent strides in cross-supervised training paradigms, challenges persist in addressing sub-network disagreement and training efficiency and reliability. In response, our paper introduces a novel cross-supervised learning framework, Quality-driven Deep Cross-supervised Learning Network (QDC-Net). QDC-Net incorporates both an evidential sub-network and an vanilla sub-network, leveraging their complementary strengths to effectively handle disagreement. To enable the reliability and efficiency of semi-supervised training, we introduce a real-time quality estimation of the model's segmentation performance and propose a directional cross-training approach through the design of directional weights. We further design a truncated form of sample-wise loss weighting to mitigate the impact of inaccurate predictions and collapsed samples in semi-supervised training. Extensive experiments on LA and Pancreas-CT datasets demonstrate that QDC-Net surpasses other state-of-the-art methods in semi-supervised medical image segmentation. Code release is available at https://github.com/Medsemiseg.

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.

Contrast-enhanced endoscopic ultrasound likely does not improve diagnostic adequacy during endoscopic ultrasound guided tissue acquisition: A systematic review and meta-analysis.

BACKGROUND AND AIMS: Solid pancreatic masses are sampled through tissue acquisition by endoscopic ultrasound (EUS). Inadequate samples may significantly delay diagnosis, increasing costs and carrying risks to the patients. AIM: assess the diagnostic adequacy of tissue acquisition using contrast-enhanced harmonic endoscopic ultrasound (CEH-EUS) compared to conventional EUS. METHODS: Five databases (PubMed, Embase, CENTRAL, Scopus and Web of Science) were searched in November 2023. Studies comparing diagnostic adequacy, accuracy and safety using CEH-EUS versus conventional EUS for tissue acquisition of solid pancreatic masses were included. Risk of bias was assessed using the Risk of Bias tool for randomized controlled trials (RoB2) and the Risk Of Bias In Non-Randomized Studies - of Interventions (ROBINS-I) tool for non-randomized studies, level of evidence using the GRADE approach, Odds Ratios (RR) with 95 % Confidence Intervals (CI) calculated and pooled using a random-effects model. I2 quantified heterogeneity. RESULTS: The search identified 3858 records; nine studies (1160 patients) were included. OR for achieving an adequate sample was 1.467 (CI: 0.850-2.533), for randomized trials 0.902 (CI: 0.541-1.505), for non-randomized 2.396 (CI: 0.916-6.264), with significant subgroup difference. OR for diagnostic accuracy was 1.326 (CI: 0.890-1977), for randomized trials 0.997 (CI: 0.593-1.977) and for non-randomized studies 1.928 (CI: 1.096-3.393), significant subgroup difference (p = 0.0467). No differences were observed for technical failures or adverse events. Heterogeneity was low, risk of bias "low" to "some concerns" for most outcomes, mostly moderate for non-randomized studies. CONCLUSION: Non-randomized studies indicated differences in favor of contrast-enhanced EUS, randomized studies showed no difference in diagnostic adequacy, accuracy or sensitivity when using CEH-EUS.

Non-specific uptake of 18F-FAPI-04 in the pancreas and its related factors: a post-hoc analysis of an ongoing prospective clinical trial.

This study aimed to analyze the characteristics of the non-specific uptake (NSU) of 18F-labeled fibroblast activation protein inhibitor (18F-FAPI) of the pancreas and investigate the related factors. Totally, 78 patients who underwent both 18F-fluorodeoxyglucose (FDG) and 18F-FAPI PET/CT examinations were divided into normal (n = 53) and NSU (n = 25) groups. The differences in general information, medical history, laboratory indexes and uptake were compared. Receiver operating characteristic (ROC) curves were used to analyze the optimal cut-off values. The correlations between 18F-FAPI-SUVmax and blood cell analysis, liver function indexes, tumor markers, and inflammatory indices were analyzed. The logistic regression model was used to estimate the independent factors. Both 18F-FAPI (4.48 ± 0.98 vs. 2.01 ± 0.53, t = 11.718, P < 0.05) and 18F-FDG (2.23 ± 0.42 vs. 2.02 ± 0.44, t = 2.036, P = 0.045) showed significantly higher in NSU group. Patients in the NSU group tended to be complicated with a history of drinking (P = 0.034), chronic liver diseases (P = 0.006), and surgery of gastrectomy (P = 0.004). ROC analysis showed cutoff values of 3.25 and 2.05 for 18F-FAPI and 18F-FDG in identifying the NSU. Patients in the NSU group showed less platelet count, higher platelet volume, higher total bilirubin, direct or indirect bilirubin (P < 0.05). Platelet count, platelet crit, large platelet ratio, aspartate aminotransferase (AST), α-L-fucosidase, and total, direct or indirect bilirubin were correlated with 18F-FAPI-SUVmax (P < 0.05). AST [1.099 (1.014, 1.192), P = 0.021] and total bilirubin [1.137 (1.035, 1.249), P = 0.007] were two independent factors in the step forward logistic regression, and platelet/% [1.079 (1.004, 1.160), P = 0.039] and total bilirubin [1.459 (1.016, 2.095), P = 0.041] were two independent factors in the step backward logistic regression for the prediction of pancreatic uptake of 18F-FAPI. 18F-FAPI-PET/CT was better than 18F-FDG in predicting the pancreatic NSU, and NSU is related to a history of drinking, chronic liver diseases, gastrectomy, heteromorphic platelet, and impaired liver function.

Risk assessment for pancreatic fistula by intraoperative image analysis of laparoscopic and robotic gastrectomy.

BACKGROUND: Pancreatic fistula (PF) is one of the most serious postoperative complications of gastrectomy. Misidentification of the boundary between the pancreas and the dissected fat is a primary concern. In this study, we focused on differences in the appearance of the pancreas and the dissected fat in actual surgical images and statistically analyzed the relationship between the pancreas and the dissected fat. METHODS: We analyzed data from 109 gastric cancer patients who underwent curative gastrectomy between November 2018 and March 2023. Intraoperative images were taken from videos of lymph node dissections of Nos.6 and 8a regions, and the mean gray value of the areas was measured using ImageJ software for analysis. The visceral fat area (VFA) was evaluated by preoperative axial CT at the umbilical level using Ziostation software. RESULTS: A significant correlation was observed between the fat/pancreas gray value ratio in the No.8a lymph node region and the drain/serum amylase ratio (P < 0.001). The fat/pancreas gray value ratio in the No.6 lymph node region correlated with VFA (P < 0.001). The VFA and drain/serum amylase ratio were significantly higher in the group with intra-abdominal complications (P = 0.004). CONCLUSIONS: We revealed significant relationships between the fat/pancreas gray value ratio with drain/serum amylase and VFA. Detecting differences in gray values between the pancreas and the dissected fat may lead to a decrease in the drain/serum amylase ratio and PF.

Application of artificial intelligence in pancreas endoscopic ultrasound imaging- A systematic review.

The pancreas is a vital organ in digestive system which has significant health implications. It is imperative to evaluate and identify malignant pancreatic lesions promptly in light of the high mortality rate linked to such malignancies. Endoscopic Ultrasound (EUS) is a non-invasive precise technique to detect pancreas disorders, but it is highly operator dependent. Artificial intelligence (AI), including traditional machine learning (ML) and deep learning (DL) techniques can play a pivotal role to enhancing the performance of EUS regardless of operator. AI performs a critical function in the detection, classification, and segmentation of medical images. The utilization of AI-assisted systems has improved the accuracy and productivity of pancreatic analysis, including the detection of diverse pancreatic disorders (e.g., pancreatitis, masses, and cysts) as well as landmarks and parenchyma. This systematic review examines the rapidly developing domain of AI-assisted system in EUS of the pancreas. Its objective is to present a thorough study of the present research status and developments in this area. This paper explores the significant challenges of AI-assisted system in pancreas EUS imaging, highlights the potential of AI techniques in addressing these challenges, and suggests the scope for future research in domain of AI-assisted EUS systems.

Multiscale and multimodal imaging for three-dimensional vascular and histomorphological organ structure analysis of the pancreas.

Exocrine and endocrine pancreas are interconnected anatomically and functionally, with vasculature facilitating bidirectional communication. Our understanding of this network remains limited, largely due to two-dimensional histology and missing combination with three-dimensional imaging. In this study, a multiscale 3D-imaging process was used to analyze a porcine pancreas. Clinical computed tomography, digital volume tomography, micro-computed tomography and Synchrotron-based propagation-based imaging were applied consecutively. Fields of view correlated inversely with attainable resolution from a whole organism level down to capillary structures with a voxel edge length of 2.0 µm. Segmented vascular networks from 3D-imaging data were correlated with tissue sections stained by immunohistochemistry and revealed highly vascularized regions to be intra-islet capillaries of islets of Langerhans. Generated 3D-datasets allowed for three-dimensional qualitative and quantitative organ and vessel structure analysis. Beyond this study, the method shows potential for application across a wide range of patho-morphology analyses and might possibly provide microstructural blueprints for biotissue engineering.

Associations of Intrapancreatic Fat Deposition With Incident Diseases of the Exocrine and Endocrine Pancreas: A UK Biobank Prospective Cohort Study.

INTRODUCTION: To investigate whether increased intrapancreatic fat deposition (IPFD) heightens the risk of diseases of the exocrine and endocrine pancreas. METHODS: A prospective cohort study was conducted using data from the UK Biobank. IPFD was quantified using MRI and a deep learning-based framework called nnUNet. The prevalence of fatty change of the pancreas (FP) was determined using sex- and age-specific thresholds. Associations between IPFD and pancreatic diseases were assessed with multivariate Cox-proportional hazard model adjusted for age, sex, ethnicity, body mass index, smoking and drinking status, central obesity, hypertension, dyslipidemia, liver fat content, and spleen fat content. RESULTS: Of the 42,599 participants included in the analysis, the prevalence of FP was 17.86%. Elevated IPFD levels were associated with an increased risk of acute pancreatitis (hazard ratio [HR] per 1 quintile change 1.513, 95% confidence interval [CI] 1.179-1.941), pancreatic cancer (HR per 1 quintile change 1.365, 95% CI 1.058-1.762) and diabetes mellitus (HR per 1 quintile change 1.221, 95% CI 1.132-1.318). FP was also associated with a higher risk of acute pancreatitis (HR 3.982, 95% CI 2.192-7.234), pancreatic cancer (HR 1.976, 95% CI 1.054-3.704), and diabetes mellitus (HR 1.337, 95% CI 1.122-1.593, P = 0.001). DISCUSSION: FP is a common pancreatic disorder. Fat in the pancreas is an independent risk factor for diseases of both the exocrine pancreas and endocrine pancreas.

Early features of pancreatic cancer on magnetic resonance imaging (MRI): a case-control study.

PURPOSE: Magnetic resonance imaging has been recommended as a primary imaging modality among high-risk individuals undergoing screening for pancreatic cancer. We aimed to delineate potential precursor lesions for pancreatic cancer on MR imaging. METHODS: We conducted a case-control study at Kaiser Permanente Southern California (2008-2018) among patients that developed pancreatic cancer who had pre-diagnostic MRI examinations obtained 2-36 months prior to cancer diagnosis (cases) matched 1:2 by age, gender, race/ethnicity, contrast status and year of scan (controls). Patients with history of acute/chronic pancreatitis or prior pancreatic surgery were excluded. Images underwent blind review with assessment of a priori defined series of parenchymal and ductal features. We performed logistic regression to assess the associations between individual factors and pancreatic cancer. We further assessed the interaction among features as well as performed a sensitivity analysis stratifying based on specific time-windows (2-3 months, 4-12 months, 13-36 months prior to cancer diagnosis). RESULTS: We identified 141 cases (37.9% stage I-II, 2.1% III, 31.4% IV, 28.6% unknown) and 292 matched controls. A solid mass was noted in 24 (17%) of the pre-diagnostic MRI scans. Compared to controls, pre-diagnostic images from cancer cases more frequently exhibited the following ductal findings: main duct dilatation (51.4% vs 14.3%, OR [95% CI]: 7.75 [4.19-15.44], focal pancreatic duct stricture with distal (upstream) dilatation (43.6% vs 5.6%, OR 12.71 [6.02-30.89], irregularity (42.1% vs 6.0%, OR 9.73 [4.91-21.43]), focal pancreatic side branch dilation (13.6% vs1.6%, OR 11.57 [3.38-61.32]) as well as parenchymal features: atrophy (57.9% vs 27.4%, OR 46.4 [2.71-8.28], focal area of signal abnormality (39.3% vs 4.8%, OR 15.69 [6.72-44,78]), all p < 0.001). CONCLUSION: In addition to potential missed lesions, we have identified a series of ductal and parenchymal features on MRI that are associated with increased odds of developing pancreatic cancer.

Robust multishot diffusion-weighted imaging of the abdomen with region-based shot rejection.

PURPOSE: Diffusion-weighted (DW) imaging provides a useful clinical contrast, but is susceptible to motion-induced dephasing caused by the application of strong diffusion gradients. Phase navigators are commonly used to resolve shot-to-shot motion-induced phase in multishot reconstructions, but poor phase estimates result in signal dropout and Apparent Diffusion Coefficient (ADC) overestimation. These artifacts are prominent in the abdomen, a region prone to involuntary cardiac and respiratory motion. To improve the robustness of DW imaging in the abdomen, region-based shot rejection schemes that selectively weight regions where the shot-to-shot phase is poorly estimated were evaluated. METHODS: Spatially varying weights for each shot, reflecting both the accuracy of the estimated phase and the degree of subvoxel dephasing, were estimated from the phase navigator magnitude images. The weighting was integrated into a multishot reconstruction using different formulations and phase navigator resolutions and tested with different phase navigator resolutions in multishot DW-echo Planar Imaging acquisitions of the liver and pancreas, using conventional monopolar and velocity-compensated diffusion encoding. Reconstructed images and ADC estimates were compared qualitatively. RESULTS: The proposed region-based shot rejection reduces banding and signal dropout artifacts caused by physiological motion in the liver and pancreas. Shot rejection allows conventional monopolar diffusion encoding to achieve median ADCs in the pancreas comparable to motion-compensated diffusion encoding, albeit with a greater spread of ADCs. CONCLUSION: Region-based shot rejection is a linear reconstruction that improves the motion robustness of multi-shot DWI and requires no sequence modifications.

Intra-pancreatic fat deposition and its relation to obesity: a magnetic resonance imaging study.

OBJECTIVES: Intra-pancreatic fat deposition (IPFD) is suspected to be associated with various medical conditions. This study aimed to assess pancreatic fat content in lean and obese individuals, characterize obese individuals with and without IPFD, and explore the underlying mechanisms. MATERIALS AND METHODS: Sixty-two obese individuals without diabetes and 35 lean controls underwent magnetic resonance imaging (MRI) using proton density fat fraction (PDFF) maps to evaluate pancreatic and hepatic fat content, and visceral adipose tissue (VAT) content. Pancreatic fibrosis was explored by T1 relaxation time and MR elastography (MRE) measurements. Associations between pancreatic fat, measures of obesity and metabolic syndrome were examined using uni- and multivariate regression analyses. RESULTS: Pancreatic PDFF was higher in obese than in lean controls (median 8.0%, interquartile range (6.1;13.3) % vs 2.6(1.7;3.9)%, p < 0.001). Obese individuals with IPFD (PDFF ≥6.2%) had higher waist circumference (114.0 ± 12.5 cm vs 105.2 ± 8.7 cm, p = 0.007) and VAT (224.9(142.1; 316.1) cm2 vs 168.2(103.4; 195.3) cm2, p < 0.001) than those without. In univariate analysis, pancreatic PDFF in obese individuals correlated with BMI (r = 0.27, p = 0.03), waist circumference (r = 0.44, p < 0.001), VAT (r = 0.37, p = 0.004), hepatic PDFF (r = 0.25, p = 0.046) and diastolic blood pressure (r = 0.32, p = 0.01). However, in multivariate analysis, only VAT was associated to pancreatic fat content. MRI measures of pancreatic fibrosis indicated no evident fibrosis in relation to increased pancreatic fat content. CONCLUSIONS: Pancreatic fat content was increased in obese individuals compared with lean controls and predominantly correlated with the amount of visceral adipose tissue. Pancreatic fat content was not clearly linked to measures of pancreatic fibrosis.

Optimizing prediction models for pancreatic fistula after pancreatectomy: Current status and future perspectives.

Postoperative pancreatic fistula (POPF) is a frequent complication after pancreatectomy, leading to increased morbidity and mortality. Optimizing prediction models for POPF has emerged as a critical focus in surgical research. Although over sixty models following pancreaticoduodenectomy, predominantly reliant on a variety of clinical, surgical, and radiological parameters, have been documented, their predictive accuracy remains suboptimal in external validation and across diverse populations. As models after distal pancreatectomy continue to be progressively reported, their external validation is eagerly anticipated. Conversely, POPF prediction after central pancreatectomy is in its nascent stage, warranting urgent need for further development and validation. The potential of machine learning and big data analytics offers promising prospects for enhancing the accuracy of prediction models by incorporating an extensive array of variables and optimizing algorithm performance. Moreover, there is potential for the development of personalized prediction models based on patient- or pancreas-specific factors and postoperative serum or drain fluid biomarkers to improve accuracy in identifying individuals at risk of POPF. In the future, prospective multicenter studies and the integration of novel imaging technologies, such as artificial intelligence-based radiomics, may further refine predictive models. Addressing these issues is anticipated to revolutionize risk stratification, clinical decision-making, and postoperative management in patients undergoing pancreatectomy.