Clinical utility of 3D magnetic resonance elastography in patients with biliary obstruction

Should All Pancreatic Cystic Lesions with Worrisome or High-Risk Features Be Resected? A Clinical and Radiological Machine Learning Model May Help to Answer

RATIONALE AND OBJECTIVES

According to current guidelines, pancreatic cystic lesions (PCLs) with worrisome or high-risk features may have overtreatment. The purpose of this study was to build a clinical and radiological based machine-learning (ML) model to identify malignant PCLs for surgery among preoperative PCLs with worrisome or high-risk features.

MATERIALS AND METHODS

Clinical and radiological details of 317 pathologically confirmed PCLs with worrisome or high-risk features were retrospectively analyzed and applied to ML models including Support Vector Machine, Logistic Regression (LR), Decision Tree, Bernoulli NB, Gaussian NB, K Nearest Neighbors and Linear Discriminant Analysis. The diagnostic ability for malignancy of the optimal model with the highest diagnostic AUC in the cross-validation procedure was further evaluated in internal (n = 77) and external (n = 50) testing cohorts, and was compared to two published guidelines in internal mucinous cyst cohort.

RESULTS

Ten clinical and radiological feature-based LR model was the optimal model with the highest AUC (0.951) in the cross-validation procedure. In the internal testing cohort, LR model reached an AUC, accuracy, sensitivity, and specificity of 0.927, 0.909, 0.914, and 0.905; in the external testing cohort, LR model reached 0.948, 0.900, 0.963, and 0.826. When compared to the European guidelines and the ACG guidelines, LR model demonstrated significantly better accuracy and specificity in identifying malignancy, while maintaining the same high sensitivity.

CONCLUSION

Clinical- and radiological-based LR model can accurately identify malignant PCLs in patients with worrisome or high-risk features, possessing diagnostic performance better than the European guidelines as well as ACG guidelines.

MR and Ultrasound Elastography for Fibrosis Assessment in Children: Practical Implementation and Supporting Evidence-AJR Expert Panel Narrative Review

Quantitative MRI and ultrasound biomarkers of liver fibrosis have become important tools in the diagnosis and clinical management of children with chronic liver disease (CLD). In particular, MR elastography (MRE) is now routinely performed in clinical practice to evaluate the liver for fibrosis. Ultrasound shear-wave elastography has also become widely performed for this purpose, especially in young children. These noninvasive methods are increasingly used to replace liver biopsy for the diagnosis, quantitative staging, and treatment monitoring of patients with CLD. Although ultrasound has advantages of portability and lower equipment cost, available evidence indicates that MRI may have greater reliability and accuracy in liver fibrosis evaluation. In this AJR Expert Panel Narrative Review, we describe how, why, and when to use MRI- and ultrasound-based elastography methods for liver fibrosis assessment in children. Practical approaches are discussed for adapting and optimizing these methods in children, with consideration of clinical indications, patient preparation, equipment requirements, acquisition technique, as well as pitfalls and confounding factors. Guidance is provided for interpretation and reporting, and representative case examples are presented.