Determining age and sex-specific distribution of pancreatic whole-gland CT attenuation using artificial intelligence aided image segmentation: Associations with body composition and pancreatic cancer risk

High prevalence of pancreatic steatosis in pancreatic cancer patients: A meta-analysis and systematic review

OBJECTIVE

In the last decade there has been increasing interest in defining pancreatic steatosis (PS) and establishing its association with pancreatic ductal adenocarcinoma (PDAC). However, no consensus guidelines have yet been published on the management of PS. In this systematic review and meta-analysis performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we investigated the association between PS and PDAC.

DESIGN

Medical literature between 2007 and 2023 was reviewed for eligible trials investigating the prevalence of PS in patients with PDAC. Eligible studies reporting on PS, assessed via imaging or histology, were included. The primary objective was to determine the association between PDAC and PS by comparing the prevalence of PS in individuals with- and without PDAC. Secondary, an evaluation was conducted to establish whether the method of assessment correlated with the association of PDAC and PS, and the prevalence of PDAC in individuals with PS. Measures of effect size were determined using odds ratios (ORs) and corresponding 95 % confidence intervals (95 % CI).

RESULTS

The systematic review identified a total of 23 studies, of which seventeen studies examined PS prevalence among PDAC patients and were included in the meta-analysis. Overall, the pooled prevalence of PS in patients with PDAC was 53.6 % (95 % CI 40.9-66.2). No significant difference in PS prevalence was observed across various diagnostic methods or geographical regions. Overall, the pooled OR for PS in patients with PDAC compared to controls was 3.23 (95 % CI 1.86-5.60).

CONCLUSIONS

PDAC patients have a high prevalence of PS, and they are significantly more likely to have PS compared to controls. These findings emphasize the need to prioritize a standardized approach to the diagnosis, follow-up, and treatment of PS, with future studies focusing on identifying patients who would benefit from PDAC surveillance programs.

Temporal Trends in Body Composition and Metabolic Markers Prior to Diagnosis of Pancreatic Ductal Adenocarcinoma

BACKGROUND & AIMS

Changes in body composition and metabolic factors may serve as biomarkers for the early detection of pancreatic ductal adenocarcinoma (PDAC). The aim of this study was to capture the longitudinal changes in body composition and metabolic factors before diagnosis of PDAC.

METHODS

We performed a retrospective cohort study in which all patients (≥18 years) diagnosed with PDAC from 2002 to 2021 were identified. We collected all abdominal computed tomography scans and 10 different blood-based biomarkers up to 36 months before diagnosis. We applied a fully automated abdominal segmentation algorithm previously developed by our group for 3-dimensional quantification of body composition on computed tomography scans. Longitudinal trends of body composition and blood-based biomarkers before PDAC diagnosis were estimated using linear mixed models, compared across different time windows, and visualized using spline regression.

RESULTS

We included 1690 patients in body composition analysis, of whom 516 (30.5%) had ≥2 prediagnostic computed tomography scans. For analysis of longitudinal trends of blood-based biomarkers, 3332 individuals were included. As an early manifestation of PDAC, we observed a significant decrease in visceral and subcutaneous adipose tissue (β = -1.94 [95% confidence interval (CI), -2.39 to -1.48] and β = -2.59 [95% CI, -3.17 to -2.02]) in area (cm2)/height (m2) per 6 months closer to diagnosis, accompanied by a decrease in serum lipids (eg, low-density lipoprotein [β = -2.83; 95% CI, -3.31 to -2.34], total cholesterol [β = -2.69; 95% CI, -3.18 to -2.20], and triglycerides [β = -1.86; 95% CI, -2.61 to -1.11]), and an increase in blood glucose levels. Loss of muscle tissue and bone volume was predominantly observed in the last 6 months before diagnosis.

CONCLUSIONS

This study identified significant alterations in a variety of soft tissue and metabolic markers that occur in the development of PDAC. Early recognition of these metabolic changes may provide an opportunity for early detection.

Artificial intelligence assisted whole organ pancreatic fat estimation on magnetic resonance imaging and correlation with pancreas attenuation on computed tomography

BACKGROUND

Fatty pancreas is associated with inflammatory and neoplastic pancreatic diseases. Magnetic resonance imaging (MRI) is the diagnostic modality of choice for measuring pancreatic fat. Measurements typically use regions of interest limited by sampling and variability. We have previously described an artificial intelligence (AI)-aided approach for whole pancreas fat estimation on computed tomography (CT). In this study, we aimed to assess the correlation between whole pancreas MRI proton-density fat fraction (MR-PDFF) and CT attenuation.

METHODS

We identified patients without pancreatic disease who underwent both MRI and CT between January 1, 2015 and June 1, 2020. 158 paired MRI and CT scans were available for pancreas segmentation using an iteratively trained convolutional neural network (CNN) with manual correction. Boxplots were generated to visualize slice-by-slice variability in 2D-axial slice MR-PDFF. Correlation between whole pancreas MR-PDFF and age, BMI, hepatic fat and pancreas CT-Hounsfield Unit (CT-HU) was assessed.

RESULTS

Mean pancreatic MR-PDFF showed a strong inverse correlation (Spearman -0.755) with mean CT-HU. MR-PDFF was higher in males (25.22 vs 20.87; p = 0.0015) and in subjects with diabetes mellitus (25.95 vs 22.17; p = 0.0324), and was positively correlated with age and BMI. The pancreatic 2D-axial slice-to-slice MR-PDFF variability increased with increasing mean whole pancreas MR-PDFF (Spearman 0.51; p < 0.0001).

CONCLUSION

Our study demonstrates a strong inverse correlation between whole pancreas MR-PDFF and CT-HU, indicating that both imaging modalities can be used to assess pancreatic fat. 2D-axial pancreas MR-PDFF is variable across slices, underscoring the need for AI-aided whole-organ measurements for objective and reproducible estimation of pancreatic fat.

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review

BACKGROUND

Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer.

OBJECTIVE

This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature.

METHODS

A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively.

RESULTS

Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms.

CONCLUSIONS

This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.

Artificial intelligence and body composition

AIMS

Although obesity is associated with chronic disease, a large section of the population with high BMI does not have an increased risk of metabolic disease. Increased visceral adiposity and sarcopenia are also risk factors for metabolic disease in people with normal BMI. Artificial Intelligence (AI) techniques can help assess and analyze body composition parameters for predicting cardiometabolic health. The purpose of the study was to systematically explore literature involving AI techniques for body composition assessment and observe general trends.

METHODS

We searched the following databases: Embase, Web of Science, and PubMed. There was a total of 354 search results. After removing duplicates, irrelevant studies, and reviews(a total of 303), 51 studies were included in the systematic review.

RESULTS

AI techniques have been studied for body composition analysis in the context of diabetes mellitus, hypertension, cancer and many specialized diseases. Imaging techniques employed for AI methods include CT (Computerized Tomography), MRI (Magnetic Resonance Imaging), ultrasonography, plethysmography, and EKG(Electrocardiogram). Automatic segmentation of body composition by deep learning with convolutional networks has helped determine and quantify muscle mass. Limitations include heterogeneity of study populations, inherent bias in sampling, and lack of generalizability. Different bias mitigation strategies should be evaluated to address these problems and improve the applicability of AI to body composition analysis.

CONCLUSIONS

AI assisted measurement of body composition might assist in improved cardiovascular risk stratification when applied in the appropriate clinical context.

Pancreatic changes with lifestyle and age: What is normal and what is concerning?

During the aging process, typical morphological changes occur in the pancreas, which leads to a specific "patchy lobular fibrosis in the elderly." The aging process in the pancreas is associated with changes in volume, dimensions, contour, and increasing intrapancreatic fat deposition. Typical changes are seen in ultrasonography, computed tomography, endosonography, and magnetic resonance imaging. Typical age-related changes must be distinguished from lifestyle-related changes. Obesity, high body mass index, and metabolic syndrome also lead to fatty infiltration of the pancreas. In the present article, age-related changes in morphology and imaging are discussed. Particular attention is given to the sonographic verification of fatty infiltration of the pancreas. Ultrasonography is a widely used screening examination method. It is important to acknowledge the features of the normal aging processes and not to interpret them as pathological findings. Reference is made to the uneven fatty infiltration of the pancreas. The differential diagnostic and the differentiation from other processes and diseases leading to fatty infiltration of the pancreas are discussed.

Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review (Preprint).. [DOI: 10.2196/preprints.44248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

BACKGROUND

Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer.

OBJECTIVE

This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature.

METHODS

A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively.

RESULTS

Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of &lt;1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms.

CONCLUSIONS

This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.

Volumetric Pancreas Segmentation on Computed Tomography: Accuracy and Efficiency of a Convolutional Neural Network Versus Manual Segmentation in 3D Slicer in the Context of Interreader Variability of Expert Radiologists

PURPOSE

This study aimed to compare accuracy and efficiency of a convolutional neural network (CNN)-enhanced workflow for pancreas segmentation versus radiologists in the context of interreader reliability.

METHODS

Volumetric pancreas segmentations on a data set of 294 portal venous computed tomographies were performed by 3 radiologists (R1, R2, and R3) and by a CNN. Convolutional neural network segmentations were reviewed and, if needed, corrected ("corrected CNN [c-CNN]" segmentations) by radiologists. Ground truth was obtained from radiologists' manual segmentations using simultaneous truth and performance level estimation algorithm. Interreader reliability and model's accuracy were evaluated with Dice-Sorenson coefficient (DSC) and Jaccard coefficient (JC). Equivalence was determined using a two 1-sided test. Convolutional neural network segmentations below the 25th percentile DSC were reviewed to evaluate segmentation errors. Time for manual segmentation and c-CNN was compared.

RESULTS

Pancreas volumes from 3 sets of segmentations (manual, CNN, and c-CNN) were noninferior to simultaneous truth and performance level estimation-derived volumes [76.6 cm 3 (20.2 cm 3 ), P < 0.05]. Interreader reliability was high (mean [SD] DSC between R2-R1, 0.87 [0.04]; R3-R1, 0.90 [0.05]; R2-R3, 0.87 [0.04]). Convolutional neural network segmentations were highly accurate (DSC, 0.88 [0.05]; JC, 0.79 [0.07]) and required minimal-to-no corrections (c-CNN: DSC, 0.89 [0.04]; JC, 0.81 [0.06]; equivalence, P < 0.05). Undersegmentation (n = 47 [64%]) was common in the 73 CNN segmentations below 25th percentile DSC, but there were no major errors. Total inference time (minutes) for CNN was 1.2 (0.3). Average time (minutes) taken by radiologists for c-CNN (0.6 [0.97]) was substantially lower compared with manual segmentation (3.37 [1.47]; savings of 77.9%-87% [ P < 0.0001]).

CONCLUSIONS

Convolutional neural network-enhanced workflow provides high accuracy and efficiency for volumetric pancreas segmentation on computed tomography.

Artificial Intelligence and the Future of Gastroenterology and Hepatology

The integration of artificial intelligence (AI) into gastroenterology and hepatology (GI) will inevitably transform the practice of GI in the coming decade. While the application of AI in health care is not new, advancements are occurring rapidly, and the future landscape of AI is beginning to come into focus. From endoscopic assistance via computer vision technology to the predictive capabilities of the vast information contained in the electronic health records, AI promises to optimize and expedite clinical and procedural practice and research in GI. The extensive body of literature already available on AI applications in gastroenterology may seem daunting at first; however, this review aims to provide a breakdown of the key studies conducted thus far and demonstrate the many potential ways this technology may impact the field. This review will also take a look into the future and imagine how GI can be transformed over the coming years, as well as potential limitations and pitfalls that must be overcome to realize this future.