Current opinion in gastroenterology

Liver fibrosis MR images classification based on higher-order interaction and sample distribution rebalancing

The fractal features of liver fibrosis MR images exhibit an irregular fragmented distribution, and the diffuse feature distribution lacks interconnectivity, result- ing in incomplete feature learning and poor recognition accuracy. In this paper, we insert recursive gated convolution into the ResNet18 network to introduce spatial information interactions during the feature learning process and extend it to higher orders using recursion. Higher-order spatial information interactions enhance the correlation between features and enable the neural network to focus more on the pixel-level dependencies, enabling a global interpretation of liver MR images. Additionally, the existence of light scattering and quantum noise during the imaging process, coupled with environmental factors such as breathing artifacts caused by long time breath holding, affects the quality of the MR images. To improve the classification performance of the neural network and better cap- ture sample features, we introduce the Adaptive Rebalance loss function and incorporate the feature paradigm as a learnable adaptive attribute into the angular margin auxiliary function. Adaptive Rebalance loss function can expand the inter-class distance and narrow the intra-class difference to further enhance discriminative ability of the model. We conduct extensive experiments on liver fibrosis MR imaging involving 209 patients. The results demonstrate an average improvement of two percent in recognition accuracy compared to ResNet18. The github is at https://github.com/XZN1233/paper.git.

Machine learning model identifies aggressive acute pancreatitis within 48 h of admission: a large retrospective study

BACKGROUND

Acute pancreatitis (AP) with critical illness is linked to increased morbidity and mortality. Current risk scores to identify high-risk AP patients have certain limitations.

OBJECTIVE

To develop and validate a machine learning tool within 48 h after admission for predicting which patients with AP will develop critical illness based on ubiquitously available clinical, laboratory, and radiologic variables.

METHODS

5460 AP patients were enrolled. Clinical, laboratory, and imaging variables were collected within 48 h after hospital admission. Least Absolute Shrinkage Selection Operator with bootstrap method was employed to select the most informative variables. Five different machine learning models were constructed to predictive likelihood of critical illness, and the optimal model (APCU) was selected. External cohort was used to validate APCU. APCU and other risk scores were compared using multivariate analysis. Models were evaluated by area under the curve (AUC). The decision curve analysis was employed to evaluate the standardized net benefit.

RESULTS

Xgboost was constructed and selected as APCU, involving age, comorbid disease, mental status, pulmonary infiltrates, procalcitonin (PCT), neutrophil percentage (Neu%), ALT/AST, ratio of albumin and globulin, cholinesterase, Urea, Glu, AST and serum total cholesterol. The APCU performed excellently in discriminating AP risk in internal cohort (AUC = 0.95) and external cohort (AUC = 0.873). The APCU was significant for biliogenic AP (OR = 4.25 [2.08-8.72], P < 0.001), alcoholic AP (OR = 3.60 [1.67-7.72], P = 0.001), hyperlipidemic AP (OR = 2.63 [1.28-5.37], P = 0.008) and tumor AP (OR = 4.57 [2.14-9.72], P < 0.001). APCU yielded the highest clinical net benefit, comparatively.

CONCLUSION

Machine learning tool based on ubiquitously available clinical variables accurately predicts the development of AP, optimizing the management of AP.

Imaging Inflammation and Infection in the Gastrointestinal Tract

A variety of seemingly non-specific symptoms manifest within the gastrointestinal (GI) tract, particularly in the colon, in response to inflammation, infection, or a combination thereof. Differentiation between symptom sources can often be achieved using various radiologic studies. Although it is not possible to provide a comprehensive survey of imaging gastrointestinal GI tract infections in a single article, the purpose of this review is to survey several topics on imaging of GI tract inflammation and infections. The review discusses such modalities as computed tomography, positron emission tomography, ultrasound, endoscopy, and magnetic resonance imaging while looking at up-an-coming technologies that could improve diagnoses and patient comfort. The discussion is accomplished through examining a combination of organ-based and organism-based approaches, with accompanying selected case examples. Specific focus is placed on the bacterial infections caused by Shigella spp., Escherichia coli, Clostridium difficile, Salmonella, and inflammatory conditions of diverticulitis and irritable bowel disease. These infectious and inflammatory diseases and their detection via molecular imaging will be compared including the appropriate differential diagnostic considerations.

Immunonutrition Support for Patients Undergoing Surgery for Gastrointestinal Malignancy: Preoperative, Postoperative, or Perioperative? A Bayesian Network Meta-Analysis of Randomized Controlled Trials

Enteral immunonutrition (EIN) has been established to be as a significantly important modality to prevent the postoperative infectious and noninfectious complications, enhance the immunity of host, and eventually improve the prognosis of gastrointestinal (GI) cancer patients undergoing surgery. However, different support routes, which are the optimum option, remain unclear. To evaluate the effects of different EIN support regimes for patients who underwent selective surgery for resectable GI malignancy, a Bayesian network meta-analysis (NMA) of randomized controlled trials (RCTs) was conducted. A search of PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials (CENTRAL) was electronically searched until the end of December 2014. Moreover, we manually checked reference lists of eligible trials and review and retrieval unpublished literature. RCTs which investigated the comparative effects of EIN versus standard enteral nutrition (EN) or different EIN regimes were included if the clinical outcomes information can be extracted from it. A total of 27 RCTs were incorporated into this study. Pair-wise meta-analyses suggested that preoperative (relative risk [RR], 0.58; 95% confidence interval [CI], 0.43-0.78), postoperative (RR, 0.63; 95% CI, 0.52-0.76), and perioperative EIN methods (RR, 0.46; 95% CI, 0.34-0.62) reduced incidence of postoperative infectious complications compared with standard EN. Moreover, perioperative EIN (RR, 0.65; 95% CI, 0.44-0.95) reduced the incidence of postoperative noninfectious complications, and the postoperative (mean difference [MD], -2.38; 95% CI, -3.4 to -1.31) and perioperative EIN (MD, -2.64; 95% CI, -3.28 to -1.99) also shortened the length of postoperative hospitalization compared with standard EN. NMA found that EIN support effectively improved the clinical outcomes of patients who underwent selective surgery for GI cancer compared with standard EN. Our results suggest EIN support is promising alternative for operation management in comparison with standard EN, and perioperative EIN regime is the optimum option for managing clinical status of patients who underwent selective surgery for GI cancer.