Harnessing Analytic Morphomics for Early Detection of Pancreatic Cancer

nnU-Net-Based Pancreas Segmentation and Volume Measurement on CT Imaging in Patients with Pancreatic Cancer

RATIONALE AND OBJECTIVES

To develop and validate a deep learning (DL)-based method for pancreas segmentation on CT and automatic measurement of pancreatic volume in pancreatic cancer.

MATERIALS AND METHODS

This retrospective study used 3D nnU-net architecture for fully automated pancreatic segmentation in patients with pancreatic cancer. The study used 851 portal venous phase CT images (499 pancreatic cancer and 352 normal pancreas). This dataset was divided into training (n = 506), internal validation (n = 126), and external test set (n = 219). For the external test set, the pancreas was manually segmented by two abdominal radiologists (R1 and R2) to obtain the ground truth. In addition, the consensus segmentation was obtained using Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm. Segmentation performance was assessed using the Dice similarity coefficient (DSC). Next, the pancreatic volumes determined by automatic segmentation were compared to those determined by manual segmentation by two radiologists.

RESULTS

The DL-based model for pancreatic segmentation showed a mean DSC of 0.764 in the internal validation dataset and DSC of 0.807, 0.805, and 0.803 using R1, R2, and STAPLE as references in the external test dataset. The pancreas parenchymal volume measured by automatic and manual segmentations were similar (DL-based model: 65.5 ± 19.3 cm3 and STAPLE: 65.1 ± 21.4 cm3; p = 0.486). The pancreatic parenchymal volume difference between the DL-based model predictions and the manual segmentation by STAPLE was 0.5 cm3, with correlation coefficients of 0.88.

CONCLUSION

The DL-based model efficiently generates automatic segmentation of the pancreas and measures the pancreatic volume in patients with pancreatic cancer.

An End-to-End Data-Adaptive Pancreas Segmentation System with an Image Quality Control Toolbox

With the development of radiology and computer technology, diagnosis by medical imaging is heading toward precision and automation. Due to complex anatomy around the pancreatic tissue and high demands for clinical experience, the assisted pancreas segmentation system will greatly promote clinical efficiency. However, the existing segmentation model suffers from poor generalization among images from multiple hospitals. In this paper, we propose an end-to-end data-adaptive pancreas segmentation system to tackle the problems of lack of annotations and model generalizability. The system employs adversarial learning to transfer features from labeled domains to unlabeled domains, seeking a dynamic balance between domain discrimination and unsupervised segmentation. The image quality control toolbox is embedded in the system, which standardizes image quality in terms of intensity, field of view, and so on, to decrease heterogeneity among image domains. In addition, the system implements a data-adaptive process end-to-end without complex operations by doctors. The experiments are conducted on an annotated public dataset and an unannotated in-hospital dataset. The results indicate that after data adaptation, the segmentation performance measured by the dice similarity coefficient on unlabeled images improves from 58.79% to 75.43%, with a gain of 16.64%. Furthermore, the system preserves quantitatively structured information such as the pancreas’ size and volume, as well as objective and accurate visualized images, which assists clinicians in diagnosing and formulating treatment plans in a timely and accurate manner.

Intrapancreatic, Liver, and Skeletal Muscle Fat Depositions in First Attack of Acute Pancreatitis Versus Health

INTRODUCTION

Increased intrapancreatic fat deposition (IPFD) has emerged as a harbinger of pancreatic cancer and chronic pancreatitis. Although it is well recognized that diseases of the exocrine pancreas often lie on a continuum (with acute pancreatitis preceding the development of chronic pancreatitis and/or pancreatic cancer), whether increased IPFD predisposes to acute pancreatitis is unknown. This study aimed to compare fat depositions in the pancreas (as well as the liver and skeletal muscle) between individuals who developed first attack of acute pancreatitis and healthy individuals.

METHODS

This was a matched case-control study nested into population-based cohort. MRI on a single 3 T scanner was used to quantify intrapancreatic, liver, and skeletal muscle fat depositions using the same protocols in all study participants. Binary logistic regression with adjustment for body mass index and other possible confounders was performed.

RESULTS

Fifty individuals with first attack of nonnecrotizing acute pancreatitis comprised the case group and 100 healthy individuals comprised the control group. A 1% increase in IPFD (but not the other fat depositions) was significantly associated with a more than 30% higher chance of developing first attack of acute pancreatitis, consistently in both the unadjusted ( P = 0.004) and all adjusted models. Furthermore, a 1% increase in IPFD (but not the other fat depositions) was significantly associated with up to a 27% higher chance of developing first attack of acute pancreatitis in individuals with normotriglyceridemia, consistently in both the unadjusted ( P = 0.030) and all adjusted models.

DISCUSSION

Increased IPFD may predispose to the development of acute pancreatitis. This opens up opportunities for reducing the burden of acute pancreatitis by means of primary prevention.

Decreased skeletal muscle density is an independent predictor of mortality in necrotizing pancreatitis- A single tertiary center experience in 507 patients

BACKGROUND AND AIMS

Necrotizing pancreatitis has a variable clinical course and it is essential to identify determinants associated with high risk of mortality and poor clinical outcomes. The aim of this study is to evaluate the association between CT-assessed body composition parameters such as visceral fat area (VFA), skeletal muscle index (SMI) and skeletal muscle density (SMD) and inpatient mortality in NP patients. Secondary outcomes include organ failure on admission, persistent organ failure, length of stay (LOS), need for ICU admission, need for endoscopic, percutaneous or surgical interventions for NP and 30-day unplanned readmission.

METHODS

All NP patients managed at a single center between 2009 and 2019 with a CT scan within a week of admission were included. SMI, SMD and VFA was calculated from CT imaging at the third lumbar vertebra and multivariable analysis was performed after correcting for age, sex, BMI, ASA classification, multi- organ failure on admission to determine independent association with inpatient mortality and secondary outcomes.

RESULTS

507 NP patients [males = 349 (68.8%), median age 53 (IQR 37-65) years were included in this study. The lowest tertile SMD was independently associated with inpatient mortality on multivariable analysis: adjusted OR 3.36 (1.57-7.2), P = 0.002. The lowest SMI tertile and highest VFA tertile were not independently associated with mortality. Lowest tertile SMD was significantly associated with persistent organ failure (OR 2.01, 95% CI 1.34-3.01, p = 0.001), need for percutaneous drainage (OR 1.84, 95% CI 1.21-2.8, p = 0.004), need for ICU admission (OR 2.32, 95% CI 1.59-3.38, p < 0.0001) and LOS.

CONCLUSION

Low SMD was independently associated with in-hospital mortality in NP patients and can be usefully incorporated in CT based predictive scoring models as a prognostic marker.

Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset

PURPOSE

To develop a two-stage three-dimensional (3D) convolutional neural networks (CNNs) for fully automated volumetric segmentation of pancreas on computed tomography (CT) and to further evaluate its performance in the context of intra-reader and inter-reader reliability at full dose and reduced radiation dose CTs on a public dataset.

METHODS

A dataset of 1994 abdomen CT scans (portal venous phase, slice thickness ≤ 3.75-mm, multiple CT vendors) was curated by two radiologists (R1 and R2) to exclude cases with pancreatic pathology, suboptimal image quality, and image artifacts (n = 77). Remaining 1917 CTs were equally allocated between R1 and R2 for volumetric pancreas segmentation [ground truth (GT)]. This internal dataset was randomly divided into training (n = 1380), validation (n = 248), and test (n = 289) sets for the development of a two-stage 3D CNN model based on a modified U-net architecture for automated volumetric pancreas segmentation. Model's performance for pancreas segmentation and the differences in model-predicted pancreatic volumes vs GT volumes were compared on the test set. Subsequently, an external dataset from The Cancer Imaging Archive (TCIA) that had CT scans acquired at standard radiation dose and same scans reconstructed at a simulated 25% radiation dose was curated (n = 41). Volumetric pancreas segmentation was done on this TCIA dataset by R1 and R2 independently on the full dose and then at the reduced radiation dose CT images. Intra-reader and inter-reader reliability, model's segmentation performance, and reliability between model-predicted pancreatic volumes at full vs reduced dose were measured. Finally, model's performance was tested on the benchmarking National Institute of Health (NIH)-Pancreas CT (PCT) dataset.

RESULTS

Three-dimensional CNN had mean (SD) Dice similarity coefficient (DSC): 0.91 (0.03) and average Hausdorff distance of 0.15 (0.09) mm on the test set. Model's performance was equivalent between males and females (P = 0.08) and across different CT slice thicknesses (P > 0.05) based on noninferiority statistical testing. There was no difference in model-predicted and GT pancreatic volumes [mean predicted volume 99 cc (31cc); GT volume 101 cc (33 cc), P = 0.33]. Mean pancreatic volume difference was -2.7 cc (percent difference: -2.4% of GT volume) with excellent correlation between model-predicted and GT volumes [concordance correlation coefficient (CCC)=0.97]. In the external TCIA dataset, the model had higher reliability than R1 and R2 on full vs reduced dose CT scans [model mean (SD) DSC: 0.96 (0.02), CCC = 0.995 vs R1 DSC: 0.83 (0.07), CCC = 0.89, and R2 DSC:0.87 (0.04), CCC = 0.97]. The DSC and volume concordance correlations for R1 vs R2 (inter-reader reliability) were 0.85 (0.07), CCC = 0.90 at full dose and 0.83 (0.07), CCC = 0.96 at reduced dose datasets. There was good reliability between model and R1 at both full and reduced dose CT [full dose: DSC: 0.81 (0.07), CCC = 0.83 and reduced dose DSC:0.81 (0.08), CCC = 0.87]. Likewise, there was good reliability between model and R2 at both full and reduced dose CT [full dose: DSC: 0.84 (0.05), CCC = 0.89 and reduced dose DSC:0.83(0.06), CCC = 0.89]. There was no difference in model-predicted and GT pancreatic volume in TCIA dataset (mean predicted volume 96 cc (33); GT pancreatic volume 89 cc (30), p = 0.31). Model had mean (SD) DSC: 0.89 (0.04) (minimum-maximum DSC: 0.79 -0.96) on the NIH-PCT dataset.

CONCLUSION

A 3D CNN developed on the largest dataset of CTs is accurate for fully automated volumetric pancreas segmentation and is generalizable across a wide range of CT slice thicknesses, radiation dose, and patient gender. This 3D CNN offers a scalable tool to leverage biomarkers from pancreas morphometrics and radiomics for pancreatic diseases including for early pancreatic cancer detection.

Pancreas image mining: a systematic review of radiomics

OBJECTIVES

To systematically review published studies on the use of radiomics of the pancreas.

METHODS

The search was conducted in the MEDLINE database. Human studies that investigated the applications of radiomics in diseases of the pancreas were included. The radiomics quality score was calculated for each included study.

RESULTS

A total of 72 studies encompassing 8863 participants were included. Of them, 66 investigated focal pancreatic lesions (pancreatic cancer, precancerous lesions, or benign lesions); 4, pancreatitis; and 2, diabetes mellitus. The principal applications of radiomics were differential diagnosis between various types of focal pancreatic lesions (n = 19), classification of pancreatic diseases (n = 23), and prediction of prognosis or treatment response (n = 30). Second-order texture features were most useful for the purpose of differential diagnosis of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature), whereas filtered image features were most useful for the purpose of classification of diseases of the pancreas and prediction of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature). The median radiomics quality score of the included studies was 28%, with the interquartile range of 22% to 36%. The radiomics quality score was significantly correlated with the number of extracted radiomics features (r = 0.52, p < 0.001) and the study sample size (r = 0.34, p = 0.003).

CONCLUSIONS

Radiomics of the pancreas holds promise as a quantitative imaging biomarker of both focal pancreatic lesions and diffuse changes of the pancreas. The usefulness of radiomics features may vary depending on the purpose of their application. Standardisation of image acquisition protocols and image pre-processing is warranted prior to considering the use of radiomics of the pancreas in routine clinical practice.

KEY POINTS

• Methodologically sound studies on radiomics of the pancreas are characterised by a large sample size and a large number of extracted features. • Optimisation of the radiomics pipeline will increase the clinical utility of mineable pancreas imaging data. • Radiomics of the pancreas is a promising personalised medicine tool in diseases of the pancreas.

A Systematic Review of Intra-pancreatic Fat Deposition and Pancreatic Carcinogenesis

BACKGROUND

Excess adiposity is considered causally related to pancreatic cancer. While most knowledge on the topic comes from studies on general and visceral adiposity, the role of intra-pancreatic fat deposition in pancreatic carcinogenesis just begins to be elucidated. The aim was to conduct a comprehensive systematic review of clinical studies on intra-pancreatic fat deposition in individuals with pancreatic cancer or pre-malignant lesions.

METHODS

A literature search was conducted independently by two reviewers using three electronic databases. Studies were included if they reported on intra-pancreatic fat deposition determined based on modern radiology or histology. Summary estimates were presented as pooled prevalence or relative risk and 95% confidence interval.

RESULTS

A total of 13 studies (encompassing 2178 individuals) were included. The pooled prevalence of intra-pancreatic fat deposition in individuals with pancreatic cancer or pre-malignant lesions was 52% (95% confidence interval, 38-66%). The presence of pancreatic cancer or pre-malignant lesions was associated with a significantly increased risk of intra-pancreatic fat deposition (relative risk 2.78 (95% confidence interval, 1.56-4.94, p < 0.001).

CONCLUSION

Individuals with pancreatic cancer or pre-malignant lesions are characterized by increased intra-pancreatic fat deposition. There are sound grounds for conceptually viewing intra-pancreatic fat deposition as a combination of fat accumulation in the pancreas (due to expansion of excess visceral fat) and fatty replacement of the pancreas (due to changes in cellular identity within the pancreas). Guidelines on reporting intra-pancreatic fat deposition need to be developed with a view to informing a comprehensive and standardized characterization of this clinical entity in future studies.

The Relationship between Abdominal Fat Phenotypes and Insulin Resistance in Non-Obese Individuals after Acute Pancreatitis

Both type 2 prediabetes/diabetes (T2DM) and new-onset prediabetes/diabetes after acute pancreatitis (NODAP) are characterized by impaired tissue sensitivity to insulin action. Although the outcomes of NODAP and T2DM are different, it is unknown whether drivers of insulin resistance are different in the two types of diabetes. This study aimed to investigate the associations between abdominal fat phenotypes and indices of insulin sensitivity in non-obese individuals with NODAP, T2DM, and healthy controls. Indices of insulin sensitivity (homeostasis model assessment of insulin sensitivity (HOMA-IS), Raynaud index, triglyceride and glucose (TyG) index, Matsuda index) were calculated in fasting and postprandial states. Fat phenotypes (intra-pancreatic fat, intra-hepatic fat, skeletal muscle fat, visceral fat, and subcutaneous fat) were determined using magnetic resonance imaging and spectroscopy. Linear regression and relative importance analyses were conducted. Age, sex, and glycated hemoglobin A1c were adjusted for. A total of 78 non-obese individuals (26 NODAP, 20 T2DM, and 32 healthy controls) were included. Intra-pancreatic fat was significantly associated with all the indices of insulin sensitivity in the NODAP group, consistently in both the unadjusted and adjusted models. Intra-pancreatic fat was not significantly associated with any index of insulin sensitivity in the T2DM and healthy controls groups. The variance in HOMA-IS was explained the most by intra-pancreatic fat (R² = 29%) in the NODAP group and by visceral fat (R² = 21%) in the T2DM group. The variance in the Raynaud index was explained the most by intra-pancreatic fat (R² = 18%) in the NODAP group and by visceral fat (R² = 15%) in the T2DM group. The variance in the TyG index was explained the most by visceral fat in both the NODAP group (R² = 49%) and in the T2DM group (R² = 25%). The variance in the Matsuda index was explained the most by intra-pancreatic fat (R² = 48%) in the NODAP group and by visceral fat (R² = 38%) in the T2DM group. The differing association between intra-pancreatic fat and insulin resistance can be used to differentiate NODAP from T2DM. Insulin resistance in NODAP appears to be predominantly driven by increased intra-pancreatic fat deposition.

Postpancreatitis Diabetes Confers Higher Risk for Pancreatic Cancer Than Type 2 Diabetes: Results From a Nationwide Cancer Registry

OBJECTIVE

Pancreatitis and diabetes are established risk factors for pancreatic cancer. However, to date, studies have investigated only the risk associated with either of them alone. The aim of this study was to investigate the effect of pancreatitis and diabetes combined, as well as their temporal relationship, on the risk of pancreatic cancer.

RESEARCH DESIGN AND METHODS

Nationwide cancer registry was linked to hospital discharge and mortality data from 1998 to 2015 in New Zealand. Incidence of primary pancreatic cancer in the four study groups (type 2 diabetes [T2D] alone, pancreatitis alone, T2D followed by pancreatitis, and postpancreatitis diabetes mellitus [PPDM]) was identified. Multivariable Cox regression analyses were conducted, with T2D as the reference group. A head-to-head comparison between the T2D followed by pancreatitis and PPDM groups was also performed.

RESULTS

Among 139,843 individuals (735,541 person-years), 913 (0.7%) were diagnosed with pancreatic cancer. The proportion of pancreatic cancer was 3.1%, 2.3%, 2.0%, and 0.6% in individuals with PPDM, T2D followed by pancreatitis, pancreatitis alone, and T2D alone, respectively. PPDM (hazard ratio [HR] 6.94; 95% CI 4.09-11.77) and T2D followed by pancreatitis (HR 5.35; 95% CI 3.52-8.14) were associated with significantly higher risks of pancreatic cancer compared with T2D alone. In the head-to-head comparison, PPDM was associated with a higher risk of pancreatic cancer compared with T2D followed by pancreatitis (HR 2.35; 95% CI 1.12-4.93).

CONCLUSIONS

Pancreatitis significantly increases the risk of pancreatic cancer in individuals with diabetes. In particular, PPDM poses the highest risk for pancreatic cancer.

Chronic Pancreatitis Is Characterized by Elevated Circulating Periostin Levels Related to Intra-Pancreatic Fat Deposition

Background

Periostin is a matricellular protein that induces fibrillogenesis and activates cell migration. It is overexpressed in common fibrotic diseases and is also associated with abdominal adiposity/ectopic fat phenotypes. The study aimed to investigate circulating levels of periostin in health and after an attack of pancreatitis, as well as their associations with abdominal adiposity/ectopic fat phenotypes.

Methods

Blood samples were obtained from healthy controls, as well as definite chronic pancreatitis (CP) and acute pancreatitis (AP) individuals during follow-up visits. Fat depositions in the pancreas, liver, skeletal muscle, as well as visceral and subcutaneous fat volumes, were quantified with the use of magnetic resonance imaging. A series of multivariable analyses were conducted, accounting for possible confounders.

Results

A total of 121 individuals were included. Periostin levels were significantly higher in the CP group compared with the other groups in both unadjusted (F = 3.211, P = 0.044) and all adjusted models (F = 4.165, P = 0.019 in the most adjusted model). Intra-pancreatic fat deposition (but not the other fat phenotypes) was significantly associated with periostin concentration in the CP group (β = 49.63, P = 0.034) and explained most of its variance (32.0%).

Conclusions

Individuals with CP, but not healthy individuals or those after clinical resolution of AP, are characterized by elevated circulating levels of periostin that are positively associated with intra-pancreatic fat deposition.

Evaluation of Ethnic Variations in Visceral, Subcutaneous, Intra-Pancreatic, and Intra-Hepatic Fat Depositions by Magnetic Resonance Imaging among New Zealanders

Anthropometric indices, such as body mass index (BMI), waist circumference (WC), and waist to height ratio (WHtR), have limitations in accurately predicting the pathophysiology of diabetes mellitus, cardiovascular diseases, and metabolic syndrome due to ethnic differences in fat distribution. Recent studies showed that the visceral adipose tissue (VAT) deposition and fat content of internal organs, most notably intra-hepatic and intra-pancreatic fat, has emerged as a more important parameter. In this study, we aimed to assess the coordination between the traditional anthropometric indices and the various fat depositions within different ethnicities in New Zealand. We recruited 104 participants with different ethnic backgrounds, including New Zealand Europeans, Māori (the indigenous people of New Zealand), Pacific Islanders (PI), and Asians. Their weight, height, and WC were measured, and subcutaneous, visceral, intra-hepatic, and intra-pancreatic fat depositions were obtained by magnetic resonance imaging (MRI). The result showed VAT, but not subcutaneous adipose tissue (SAT) depositions at all levels were significantly varied among the three groups. BMI was associated best with L23SAT in NZ Europeans (30%) and L45VAT in Māori/PI (24.3%). WC and WHtR were correlated well with L45SAT in the total population (18.8% and 12.2%, respectively). Intra-pancreatic fat deposition had a positive Pearson relationship with NZ European BMI and Māori/PI WC, but no regression correlation with anthropometric indices. Conventional anthropometric indices did not correspond to the same fat depositions across different ethnic groups.

Antidiabetic Medications and Mortality Risk in Individuals With Pancreatic Cancer-Related Diabetes and Postpancreatitis Diabetes: A Nationwide Cohort Study

OBJECTIVE

There are no specific treatment guidelines for diabetes of the exocrine pancreas. High-quality studies are warranted to investigate whether the use of antidiabetic medications has survival benefit in individuals with diabetes of the exocrine pancreas. The objective was to determine the risk of mortality associated with the use of antidiabetic medications in individuals with pancreatic cancer-related diabetes (PCRD) and postpancreatitis diabetes mellitus (PPDM).

RESEARCH DESIGN AND METHODS

Nationwide pharmaceutical dispensing data (2006-2015) linked to hospital discharge data were used to identify 1,862 individuals with PCRD or PPDM. Multivariable Cox regression analysis was conducted, and the risk was expressed as hazard ratios and 95% CIs. A 6-month lag was used to minimize reverse causality.

RESULTS

In individuals with PCRD, ever users of metformin (adjusted hazard ratio 0.54; 95% CI 0.46-0.63) and ever users of insulin (adjusted hazard ratio 0.46; 95% CI 0.39-0.55) had significantly lower risks of mortality compared with never users of antidiabetic medications. These associations attenuated toward the null with the use of a 6-month lag. In individuals with PPDM, ever users of metformin had a significantly lower risk of mortality (adjusted hazard ratio 0.51; 95% CI 0.36-0.70), whereas ever-users of insulin did not have a significantly changed risk of mortality (adjusted hazard ratio 0.75; 95% CI 0.49-1.14) compared with never users of antidiabetic medications. The former association remained significant with the use of a 6-month lag.

CONCLUSIONS

Metformin promotes a survival benefit in individuals with PPDM but not PCRD. Reverse causality may play a role in the association between insulin use and mortality in PCRD.

Serum lipid profile as a biomarker of intra-pancreatic fat deposition: A nested cross-sectional study

BACKGROUND AND AIMS

The relationship between intra-pancreatic fat deposition (IPFD) and lipid profile has been investigated in individuals with obesity and/or type 2 diabetes, but not in healthy non-obese individuals and those after acute pancreatitis. The aim of the study was to investigate the association between serum lipid profile and IPFD in the latter individuals and to determine the effect of abdominal fat distribution and other covariates.

METHODS AND RESULTS

A total of 90 individuals with a history of acute pancreatitis as well as 23 healthy non-obese individuals participated in the study. Magnetic resonance imaging was used to quantify IPFD and visceral-to-subcutaneous fat volume ratio, followed by fasting state measurement of high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC), TC/HDL-C ratio, and triglycerides. In healthy non-obese individuals, IPFD was not significantly associated with any of the studied markers. In individuals after acute pancreatitis, IPFD was significantly associated with triglycerides in both unadjusted (β = 0.360; 95% CI, 0.090-0.629; p = 0.009) and adjusted models, with a β-coefficient of 0.280 [(95% CI, 0.016-0.545); p = 0.038] in the most adjusted model. Also, IPFD was significantly associated with TC/HDL-C ratio in both unadjusted (β = 0.336; 95% CI, 0.045-0.626; p = 0.024) and adjusted models, with a β-coefficient of 0.375 [(95% CI, 0.090-0.660); p = 0.010] in the most adjusted model. Multiple regression yielded triglycerides, but not TC/HDL-C ratio, as a significant marker of IPFD in individuals after acute pancreatitis.

CONCLUSIONS

Serum lipid profile is not associated with IPFD in healthy non-obese. Triglycerides, but not other components of lipid profile, is a promising biomarker for IPFD in individuals following acute pancreatitis.

Automated pancreas segmentation from computed tomography and magnetic resonance images: A systematic review

The pancreas is a highly variable organ, the size, shape, and position of which are affected by age, sex, adiposity, the presence of diseases affecting the pancreas (e.g., diabetes, pancreatic cancer, pancreatitis) and other factors. Accurate automated segmentation of the pancreas has the potential to facilitate timely diagnosing and managing of diseases of the endocrine and exocrine pancreas. The aim was to systematically review studies reporting on automated pancreas segmentation algorithms derived from computed tomography (CT) or magnetic resonance (MR) images. The MEDLINE database and three patent databases were searched. Data on the performance of algorithms were meta-analysed, when possible. The algorithms were classified into one of four groups: multiorgan atlas-based, landmark-based, shape model-based, and neural network-based. A total of 13 cohorts suitable for meta-analysis were pooled to determine the performance of pancreas segmentation algorithms altogether using the Dice coefficient. These cohorts, comprising 1110 individuals, yielded a weighted mean Dice coefficient of 74.4%. Eight cohorts suitable for meta-analysis were pooled to determine the performance of pancreas segmentation algorithms altogether using the Jaccard index. These cohorts, comprising 636 individuals, yielded a weighted mean Jaccard index of 63.7%. Multiorgan atlas-based algorithms had a weighted mean Dice coefficient of 70.1% and a weighted mean Jaccard index of 59.8%. Neural network-based algorithms had a weighted mean Dice coefficient of 82.3% and a weighted mean Jaccard index of 70.1%. Studies using the other two types of algorithms were not meta-analysable. The above findings indicate that the automation of pancreas segmentation represents a considerable challenge as the performance of current automated pancreas segmentation algorithms is suboptimal. Adopting standardised reporting on performance of pancreas segmentation algorithms and encouraging the use of benchmark pancreas segmentation datasets will allow future algorithms to be tested and compared more easily and fairly.

Circulating levels of lipocalin-2 are associated with fatty pancreas but not fatty liver

Lipocalin-2 (LCN-2), a peptide with diverse expression pattern, has been identified as a biomarker of various diseases as well as a factor contributing to inflammatory responses associated with excess adiposity and ensuing metabolic disorders. Although the inter-relationship between LCN-2 and excess adiposity is increasingly recognized, little is known about the inter-relationship between LCN-2 and ectopic fat deposition. The present study aimed to investigate the associations between LCN-2 and fatty pancreas as well as fatty liver. In addition, the associations between LCN-2 and pro-inflammatory cytokines were studied. Magnetic resonance imaging was used to quantify intra-pancreatic fat deposition and visceral-to-subcutaneous fat volume ratio whereas magnetic resonance spectroscopy was used to quantify liver fat deposition. Fasting venous blood was analyzed for LCN-2, C-C motif chemokine ligand 2, interleukin-6, leptin, tumor necrosis factor-α, glycated hemoglobin, glucose, and insulin. Binary logistic regression and linear regression analyses were conducted. Three statistical models were built to adjust for demographics, comorbidities, levels of glycated hemoglobin, insulin resistance, and abdominal fat distribution. A total of 79 individuals were studied, of whom 20 had fatty pancreas, 14 had fatty liver, and 4 had both. Lipocalin-2 was significantly associated with fatty pancreas in all the adjusted models (p = 0.014 in the most adjusted model) but was not significantly associated with fatty liver in any of the studied models. Lipocalin-2 was significantly associated with interleukin-6 and tumor necrosis factor-α, in both the unadjusted and adjusted models. Leptin and C-C motif chemokine ligand 2 were not significantly associated with LCN-2 in any of the studied models. These findings suggest that LCN-2 is a potential biomarker of fatty pancreas, independent of abdominal fat distribution, insulin resistance, and other covariates. The role of LCN-2 in intra-pancreatic fat deposition and related low-grade inflammation warrants further investigations.

Intrapancreatic fat deposition and visceral fat volume are associated with the presence of diabetes after acute pancreatitis

Ectopic fat and abdominal adiposity phenotypes have never been studied holistically in individuals after acute pancreatitis (AP). The aim of the study was to investigate phenotypical differences in ectopic fat and abdominal fat between individuals after AP (with and without diabetes) and to determine the role of pancreatitis-related factors. Eighty-four individuals were studied cross-sectionally after a median of 21.5 mo since last episode of AP and were categorized into "diabetes" and "no diabetes" groups. Twenty-eight healthy volunteers were also recruited. With the use of magnetic resonance imaging, intrapancreatic fat percentage, liver fat percentage, visceral fat volume (VFV), subcutaneous fat volume, and visceral-to-subcutaneous (V/S) fat volume ratio were quantified. Analysis of variance was used to investigate the differences in these phenotypes between the groups. All analyses were adjusted for age and sex. Linear regression analysis was used to investigate the association between pancreatitis-related factors and the studied phenotypes. Intrapancreatic fat percentage was significantly higher in the diabetes group (10.2 ± 1.2%) compared with the no diabetes (9.2 ± 1.7%) and healthy volunteers (7.9 ± 1.9%) groups (P < 0.001). VFV was significantly higher in the diabetes (2,715.3 ±1,077.6 cm³) compared with no diabetes (1,983.2 ± 1,092.4 cm³) and healthy volunteer (1,126.2 ± 740.4 cm³) groups (P < 0.001). V/S fat volume ratio was significantly higher in the diabetes (0.97 ± 0.27) compared with no diabetes (0.68 ± 0.42) and healthy volunteer (0.52 ± 0.34) groups (P = 0.001). Biliary AP was associated with significantly higher intrapancreatic fat percentage (β = 0.67; 95% CI, 0.01, 1.33; P = 0.047). C-reactive protein levels during hospitalization for AP were associated with significantly higher VFV (β = 3.32; 95% CI, 1.68, 4.96; P < 0.001). In conclusion, individuals with diabetes after AP have higher intrapancreatic fat percentage, VFV, and V/S fat volume ratio. Levels of C-reactive protein during AP are significantly associated with VFV, whereas biliary AP is significantly associated with intrapancreatic fat percentage. NEW & NOTEWORTHY Individuals with diabetes after acute pancreatitis have significantly higher intrapancreatic fat percentage and visceral fat volume compared with individuals without diabetes after acute pancreatitis and healthy controls. C-reactive protein levels during hospitalization for acute pancreatitis and biliary etiology of acute pancreatitis are associated with significantly larger visceral fat and pancreatic fat depots, respectively.