Preoperative prediction of curative surgery of perihilar cholangiocarcinoma by combination of endoscopic ultrasound and computed tomography

AI-based digital histopathology for perihilar cholangiocarcinoma: A step, not a jump

INTRODUCTION

Perihilar cholangiocarcinoma (PHCC) is a rare malignancy with limited survival prediction accuracy. Artificial intelligence (AI) and digital pathology advancements have shown promise in predicting outcomes in cancer. We aimed to improve prognosis prediction for PHCC by combining AI-based histopathological slide analysis with clinical factors.

METHODS

We retrospectively analyzed 317 surgically treated PHCC patients (January 2009-December 2018) at the University Hospital of Essen. Clinical data, surgical details, pathology, and outcomes were collected. Convolutional neural networks (CNN) analyzed whole-slide images. Survival models incorporated clinical and histological features.

RESULTS

Among 142 eligible patients, independent survival predictors were tumor grade (G), tumor size (T), and intraoperative transfusion requirement. The CNN-based model combining clinical and histopathological features demonstrates proof of concept in prognosis prediction, limited by histopathological complexity and feature extraction challenges. However, the CNN-based model generated heatmaps assisting pathologists in identifying areas of interest.

CONCLUSION

AI-based digital pathology showed potential in PHCC prognosis prediction, though refinement is necessary for clinical relevance. Future research should focus on enhancing AI models and exploring novel approaches to improve PHCC patient prognosis prediction.

Role of Endoscopic Ultrasound in Liver Disease: Where Do We Stand?

As the burden of liver disease in the general populace steadily increases, so does the need for both advanced diagnostic and treatment options. Endoscopic ultrasound is a reliable diagnostic and therapeutic method that has an established role, foremost in pancreatobiliary pathology. This paper aims to summarize the growing role of endoscopic ultrasound in hepatology based on the search of the current literature. A number of applications of endoscopic ultrasound are reviewed, including both noninvasive methods and tissue acquisition in focal and diffuse liver disease, portal hypertension measurement, detection and management of gastric and esophageal varices, treatment of focal liver lesions and staging of pancreatobiliary malignancies, treatment of cystic and solid liver lesions, as well as liver abscess drainage. Both hepatologists and endoscopists should be aware of the evolving role of endoscopic ultrasound in liver disease. The inherent invasive nature of endoscopic examination limits its use to a targeted population identified using noninvasive methods. Endoscopic ultrasound is one the most versatile methods in gastroenterology, allowing immediate access with detection, sampling, and treatment of digestive tract pathology. Further expansion of its use in hepatology is immanent.

Feasibility of magnetic resonance imaging-based radiomics features for preoperative prediction of extrahepatic cholangiocarcinoma stage

AIM

The aim of this study is to develop and test radiomics models based on magnetic resonance imaging (MRI) to preoperatively and respectively predict the T stage, perineural invasion, and microvascular invasion of extrahepatic cholangiocarcinoma (eCCA) through a non-invasive approach.

METHODS

This research included 101 eCCA patients (29-83 years; 45 females and 56 males) between August 2011 and December 2019. Radiomics features were retrospectively extracted from T1-weighted imaging, T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient map using MaZda software. The region of interest was manually delineated in the largest section on four MRI images as ground truth while keeping 1-2 mm margin to tumor border, respectively. Pretreatment, dimension reduction method, and classifiers were used to establish radiomics signatures for assessing three pathological characteristics of eCCA. Finally, independent training and testing datasets were used to assess radiomics signature performance based on receiver operating characteristic curve analysis, accuracy, precision, sensitivity, and specificity.

RESULTS

This study extracted 1208 radiomics features from four MRI images of each patient. The best performing radiomics signatures for assessing the T stage, perineural invasion, and microvascular invasion were respectively produced by L1_normalization + linear discriminant analysis (LDA) + logistic regression, Box_Cox transformer + LDA + K-nearest neighbor, and L2_normalization + LDA + AdaBoost. The area under the curve values of the radiomics signatures for predicting the training and testing cohorts in each subgroup were respectively 1 and 0.962 (T stage), 1 and 1 (both perineural invasion and microvascular invasion).

CONCLUSION

These proposed radiomic models based on MR images had powerful performance and high potential in predicting T stage, perineural, and microvascular invasion of eCCA.

REPORTING GUIDELINES/RESEARCH DESIGN

Prognostic study.

Novel score predicts risk for cytomegalovirus infection in ulcerative colitis

BACKGROUND

Cytomegalovirus (CMV) infection is associated with relapse and exacerbation of ulcerative colitis (UC), especially in immunosuppressed patients.

OBJECTIVES

The aim of this study was to identify risk factors for CMV colitis and to develop a predictive risk score to estimate the probability of CMV colitis in UC patients supporting clinical decision making.

STUDY DESIGN

A cohort of 239 UC-patients was retrospectively analyzed. Univariate and multivariate regression analysis identified several independent risk factors for CMV colitis and a predictive risk score was established using ROC analysis.

RESULTS

CMV colitis is common in patients with severe ulcerative colitis. Clinical UC activity, disease duration and extent as well as the use of steroids and anti-TNF-α agents were identified as risk factors (p < 0.05 each). Based on five predictive parameters, a web-based risk score was developed. A strong correlation between the predicted and actual rates of CMV colitis was found (AUC: 0.855; 95% CI 0.79-0.92; p < 0.0001).

CONCLUSIONS

Our study supports the pathogenic relevance of CMV in UC. The predictive risk score estimates the risk of CMV colitis and might aid in clinical decision making, especially when timely modifications of therapeutic regimens are needed and reliable diagnostic tools are not readily available.