Cholangiocarcinoma Evaluation via Imaging and Artificial Intelligence

A dual-electrode label-free immunosensor based on in situ prepared Au-MoO3-Chi/porous graphene nanoparticles for point-of-care detection of cholangiocarcinoma

A novel label-free electrochemical immunosensor was prepared for the detection of carbohydrate antigen 19-9 (CA19-9) and carcinoembryonic antigen (CEA) as biomarkers of cholangiocarcinoma (CCA). A nanocomposite of gold nanoparticles, molybdenum trioxide, and chitosan (Au-MoO3-Chi) was layer-by-layer assembled on the porous graphene (PG) modified a dual screen-printed electrode using a self-assembling technique, which increased surface area and conductivity and enhanced the adsorption of immobilized antibodies. The stepwise self-assembling procedure of the modified electrode was further characterized morphologically and functionally. The electroanalytical detection of biomarkers was based on the interaction between the antibody and antigen of each marker via linear sweep voltammetry using ferrocyanide/ferricyanide as an electrochemical redox indicator. Under optimized conditions, the fabricated immunosensor showed linear relationships between current change (ΔI) and antigen concentrations in two ranges: 0.0025-0.1 U mL-1 and 0.1-1.0 U mL-1 for CA19-9, and 0.001-0.01 ng mL-1 and 0.01-1.0 ng mL-1 for CEA. The limits of detection (LOD) were 1.0 mU mL-1 for CA19-9 and 0.5 pg mL-1 for CEA. Limits of quantitation (LOQ) were 3.3 mU mL-1 for CA19-9 and 1.6 pg mL-1 for CEA. The selectivity of the developed immunosensor was tested on mixtures of antigens and was then successfully applied to determine CA19-9 and CEA in human serum samples, producing satisfactory results consistent with the clinical method.

A multi-center diagnostic system for intrahepatic mass-forming cholangiocarcinoma based on preoperative MRI and clinical features

OBJECTIVES

To establish a non-invasive diagnostic system for intrahepatic mass-forming cholangiocarcinoma (IMCC) via decision tree analysis.

METHODS

Totally 1008 patients with 504 pathologically confirmed IMCCs and proportional hepatocellular carcinomas (HCC) and combined hepatocellular cholangiocarcinomas (cHCC-CC) from multi-centers were retrospectively included (internal cohort n = 700, external cohort n = 308). Univariate and multivariate logistic regression analyses were applied to evaluate the independent clinical and MRI predictors for IMCC, and the selected features were used to develop a decision tree-based diagnostic system. Diagnostic efficacy of the established system was calculated by the receiver operating characteristic curve analysis in the internal training-testing and external validation cohorts, and also in small lesions  ≤ 3 cm.

RESULTS

Multivariate analysis revealed that female, no chronic liver disease or cirrhosis, elevated carbohydrate antigen 19-9 (CA19-9) level, normal alpha-fetoprotein (AFP) level, lobulated tumor shape, progressive or persistent enhancement pattern, no enhancing tumor capsule, targetoid appearance, and liver surface retraction were independent characteristics favoring the diagnosis of IMCC over HCC or cHCC-CC (odds ratio = 3.273-25.00, p < 0.001 to p = 0.021). Among which enhancement pattern had the highest weight of 0.816. The diagnostic system incorporating significant characteristics above showed excellent performance in the internal training (area under the curve (AUC) 0.971), internal testing (AUC 0.956), and external validation (AUC 0.945) cohorts, as well as in small lesions  ≤ 3 cm (AUC 0.956).

CONCLUSIONS

In consideration of the great generalizability and clinical efficacy in multi-centers, the proposed diagnostic system may serve as a non-invasive, reliable, and easy-to-operate tool in IMCC diagnosis, providing an efficient approach to discriminate IMCC from other HCC-containing primary liver cancers.

CLINICAL RELEVANCE STATEMENT

This study established a non-invasive, easy-to-operate, and explainable decision tree-based diagnostic system for intrahepatic mass-forming cholangiocarcinoma, which may provide essential information for clinical decision-making.

KEY POINTS

• Distinguishing intrahepatic mass-forming cholangiocarcinoma (IMCC) from other primary liver cancers is important for both treatment planning and outcome prediction. • The MRI-based diagnostic system showed great performance with satisfying generalization ability in the diagnosis and discrimination of IMCC. • The diagnostic system may serve as a non-invasive, easy-to-operate, and explainable tool in the diagnosis and risk stratification for IMCC.

Preliminary Evaluation of Artificial Intelligence-Based Anti-Hepatocellular Carcinoma Molecular Target Study in Hepatocellular Carcinoma Diagnosis Research

In this paper, in-depth research analysis of anti-hepatocellular carcinoma molecular targets for hepatocellular carcinoma diagnosis was conducted using artificial intelligence. Because BRD4 plays an important role in gene transcription for cell cycle regulation and apoptosis, tumor-targeted therapy by inhibiting the expression or function of BRD4 has received increasing attention in the field of antitumor research. Study subjects in small samples were used as the validation set for validating each diagnostic model constructed based on the training set. The diagnostic effect of each model in the validation set is evaluated by calculating the sensitivity, specificity, and compliance rate, and the model with the best and most stable diagnostic value is selected by combining the results of model construction, validation, and evaluation. The total sample was divided into a training set and test set by using a stratified sampling method in the ratio of 7 : 3. Logistic regression, weighted k-nearest neighbor, decision tree, and BP artificial neural network were used in the training set to construct diagnostic models for early-stage liver cancer, respectively, and the optimal parameters of the corresponding models were obtained, and then, the constructed models were validated in the test set. To evaluate the diagnostic efficacy, stability, and generalization ability of the four classification methods more robustly, a 10-fold crossover test was performed for each classification method. BRD4 is an epigenetic regulator that is associated with the upregulation of expression of various oncogenic drivers in tumors. Targeting BRD4 with pharmacological inhibitors has emerged as a novel approach for tumor treatment. However, before we implemented this topic, there were no detailed studies on whether BRD4 could be used for the treatment of HCC, the role of BRD4 in HCC cell proliferation and apoptosis, and the ability of small molecule BRD4 inhibitors to induce apoptosis in hepatocellular carcinoma cells.

The Cytomorphologic and Molecular Assessment of Bile Duct Brushing Specimens

Biliary duct brushing cytology is the standard of care for the assessment of bile duct strictures but suffers from low sensitivity for the detection of a high-risk stricture. Pathologic diagnosis of strictures is optimized by integration of cytomorphology and molecular analysis with fluorescence in situ hybridization or next-generation sequencing. Bile duct cancers are genetically heterogeneous, requiring analysis of multiple gene panels to increase sensitivity. Using molecular analysis as an ancillary test for bile duct brushing samples aids in the identification of mutations that support the diagnosis of a high-risk stricture as well as the identification of actionable mutations for targeted therapies currently in clinical trials for the treatment of patients with bile duct cancer.

Trends of the Extra-Hepatic Biliary Cancer and Its Surgical Management: A Cross-Sectional Study From the National Cancer Database

Introduction

Biliary cancers are rare cancers with poor prognoses. In this study, we aimed to evaluate trends in early detection and surgical treatment and approaches in extra-hepatic biliary tract cancers (EBCs) over 13 years in the US.

Methods

The most recent data on patients diagnosed with EBC between 2004 and 2016 were extracted from the National Cancer Database (NCDB). The patients’ demographics (sex, age, race), primary tumor sites, tumor grades and stages, staging modalities, diagnostic confirmation, surgical treatment modalities and approaches, and 90-day mortality were analyzed to determine trends.

Results

Biopsy was the most common staging modality in 63.9% of total 60,291 patients. The bile duct was the primary tumor site (55.0%). Histologic examination was the most common confirmatory diagnostic modality (77.5%). The most common stage was stage II (23%). The most common surgical treatment modality was radical surgery (13.88%). The open surgical approach was used in 27.1% of patients, followed by a laparoscopic approach (4.3%).

Conclusion

EBC showed no significant change in the trends of the stage at diagnosis, treatment modality, and extent of surgical procedures despite advances in surgical diagnostic and therapeutic modalities; however, the total number of cases slightly increased between 2004 and 2016.

Clinical relevance of biomarkers in cholangiocarcinoma: critical revision and future directions

Cholangiocarcinoma (CCA) is a malignant tumour arising from the biliary system. In Europe, this tumour frequently presents as a sporadic cancer in patients without defined risk factors and is usually diagnosed at advanced stages with a consequent poor prognosis. Therefore, the identification of biomarkers represents an utmost need for patients with CCA. Numerous studies proposed a wide spectrum of biomarkers at tissue and molecular levels. With the present paper, a multidisciplinary group of experts within the European Network for the Study of Cholangiocarcinoma discusses the clinical role of tissue biomarkers and provides a selection based on their current relevance and potential applications in the framework of CCA. Recent advances are proposed by dividing biomarkers based on their potential role in diagnosis, prognosis and therapy response. Limitations of current biomarkers are also identified, together with specific promising areas (ie, artificial intelligence, patient-derived organoids, targeted therapy) where research should be focused to develop future biomarkers.

Artificial intelligence using advanced imaging techniques and cholangiocarcinoma: Recent advances and future direction

While cholangiocarcinoma represents only about 3% of all gastrointestinal tumors, it has a dismal survival rate, usually because it is diagnosed at a late stage. The utilization of Artificial Intelligence (AI) in medicine in general, and in gastroenterology has made gigantic steps. However, the application of AI for biliary disease, in particular for cholangiocarcinoma, has been sub-optimal. The use of AI in combination with clinical data, cross-sectional imaging (computed tomography, magnetic resonance imaging) and endoscopy (endoscopic ultrasound and cholangioscopy) has the potential to significantly improve early diagnosis and the choice of optimal therapeutic options, leading to a transformation in the prognosis of this feared disease. In this review we summarize the current knowledge on the use of AI for the diagnosis and management of cholangiocarcinoma and point to future directions in the field.

Current Perspectives on the Surgical Management of Perihilar Cholangiocarcinoma

Cholangiocarcinoma (CCA) represents nearly 15% of all primary liver cancers and 2% of all cancer-related deaths worldwide. Perihilar cholangiocarcinoma (pCCA) accounts for 50-60% of all CCA. First described in 1965, pCCAs arise between the second-order bile ducts and the insertion of the cystic duct into the common bile duct. CCA typically has an insidious onset and commonly presents with advanced, unresectable disease. Complete surgical resection is technically challenging, as tumor proximity to the structures of the central liver often necessitates an extended hepatectomy to achieve negative margins. Intraoperative frozen section can aid in assuring negative margins and complete resection. Portal lymphadenectomy provides important prognostic and staging information. In specialized centers, vascular resection and reconstruction can be performed to achieve negative margins in appropriately selected patients. In addition, minimally invasive surgical techniques (e.g., robotic surgery) are safe, feasible, and provide equivalent short-term oncologic outcomes. Neoadjuvant chemoradiation therapy followed by liver transplantation provides a potentially curative option for patients with unresectable disease. New trials are needed to investigate novel chemotherapies, immunotherapies, and targeted therapies to better control systemic disease in the adjuvant setting and, potentially, downstage disease in the neoadjuvant setting.

Machine learning in endoscopic ultrasonography and the pancreas: The new frontier?

Pancreatic diseases have a substantial burden on society which is predicted to increase further over the next decades. Endoscopic ultrasonography (EUS) remains the best available diagnostic method to assess the pancreas, however, there remains room for improvement. Artificial intelligence (AI) approaches have been adopted to assess pancreatic diseases for over a decade, but this methodology has recently reached a new era with the innovative machine learning algorithms which can process, recognize, and label endosonographic images. Our review provides a targeted summary of AI in EUS for pancreatic diseases. Included studies cover a wide spectrum of pancreatic diseases from pancreatic cystic lesions to pancreatic masses and diagnosis of pancreatic cancer, chronic pancreatitis, and autoimmune pancreatitis. For these, AI models seemed highly successful, although the results should be evaluated carefully as the tasks, datasets and models were greatly heterogenous. In addition to use in diagnostics, AI was also tested as a procedural real-time assistant for EUS-guided biopsy as well as recognition of standard pancreatic stations and labeling anatomical landmarks during routine examination. Studies thus far have suggested that the adoption of AI in pancreatic EUS is highly promising and further opportunities should be explored in the field.

Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery

Background and Objectives: The advancement of artificial intelligence (AI) based technologies in medicine is progressing rapidly, but the majority of its real-world applications has not been implemented. The establishment of an accurate diagnosis with treatment has now transitioned into an artificial intelligence era, which has continued to provide an amplified understanding of liver cancer as a disease and helped to proceed better with the method of procurement. This article focuses on reviewing the AI in liver-associated diseases and surgical procedures, highlighting its development, use, and related counterparts. Materials and Methods: We searched for articles regarding AI in liver-related ailments and surgery, using the keywords (mentioned below) on PubMed, Google Scholar, Scopus, MEDLINE, and Cochrane Library. Choosing only the common studies suggested by these libraries, we segregated the matter based on disease. Finally, we compiled the essence of these articles under the various sub-headings. Results: After thorough review of articles, it was observed that there was a surge in the occurrence of liver-related surgeries, diagnoses, and treatments. Parallelly, advanced computer technologies governed by AI continue to prove their efficacy in the accurate screening, analysis, prediction, treatment, and recuperation of liver-related cases. Conclusions: The continual developments and high-order precision of AI is expanding its roots in all directions of applications. Despite being novel and lacking research, AI has shown its intrinsic worth for procedures in liver surgery while providing enhanced healing opportunities and personalized treatment for liver surgery patients.

Ibuprofen and diclofenac differentially affect cell viability, apoptosis and morphological changes in human cholangiocarcinoma cell lines

Objectives

Cholangiocarcinoma is a malignant biliary epithelial duct neoplasm caused by chronic inflammation after liver fluke infection. It is a major public health concern in the Greater Mekong sub-region in northeast Thailand. Herein, the effects of the non-steroidal anti-inflammatory drugs (NSAIDs) ibuprofen and diclofenac on the cell proliferation activity of the human cholangiocarcinoma cell lines KKU-M139 and KKU-213B were studied.

Methods

Cell viability was assessed with MTT assays. Inverted phase-contrast light microscopy, scanning electron microscopy and transmission electron microscopy were used to investigate the cells’ morphological alterations. Caspase 3/7 and Annexin V/PI were detected with a multimode microplate reader.

Results

Ibuprofen and diclofenac decreased viability in both cell lines, and ibuprofen-treated cells exhibited reversible cell injury. In both KKU-M139 and KKU-213B cell lines, the diclofenac-treated cells had the greatest injury. The cells exhibited features of irreversible cell injury. In addition, caspase 3/7 and Annexin V/PI detection revealed early cell apoptotic characteristics.

Conclusion

These findings suggest that NSAIDs may potentially suppress cell viability. Ibuprofen and diclofenac both induced morphological changes and apoptosis.

Radioembolization of Intrahepatic Cholangiocarcinoma: Patient Selection, Outcomes, and Competing Therapies

Intrahepatic cholangiocarcinoma is the second most common primary hepatic malignancy and poses a therapeutic challenge owing to its late-stage presentation and treatment-resistant outcomes. Most patients are diagnosed with locally advanced, unresectable disease and are treated with a combination of systemic and local regional therapies. Transarterial radioembolization offers a survival benefit and a favorable side effect profile, with a growing body of evidence to support its use. Herein, we review patient selection and detail outcomes of radioembolization for intrahepatic cholangiocarcinoma, together with mention of competing treatments.

Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography

Background

The use of traditional techniques to evaluate breast cancer is restricted by the subjective nature of assessment, variation across radiologists, and limited data. Radiomics may predict axillary lymph node metastasis (ALNM) of breast cancer more accurately.

Purpose

The aim was to evaluate the diagnostic performance of a radiomics model based on ALNs themselves that used contrast-enhanced computed tomography (CECT) to detect ALNM of breast cancer.

Methods

We retrospectively enrolled 402 patients with breast cancer confirmed by pathology from January 2016 to October 2019. Three hundred and ninety-six features were extracted for all patients from axial CECT images of 825 ALNs using Artificial Intelligent Kit software (GE Medical Systems, Version V3.1.0.R). Next, the radiomics model was trained, validated, and tested for predicting ALNM in breast cancer by using a support vector machine algorithm. Finally, the performance of the radiomics model was evaluated in terms of its classification accuracy and the value of the area under the curve (AUC).

Results

The radiomics model yielded the best classification accuracy of 89.1% and the highest AUC of 0.92 (95% CI: 0.91-0.93, p=0.002) for discriminating ALNM in breast cancer in the validation cohorts. In the testing cohorts, the model also demonstrated better performance, with an accuracy of 88.5% and an AUC of 0.94 (95% CI: 0.93-0.95, p=0.005) for predicting ALNM in breast cancer.

Conclusion

The radiomics model based on CECT images can be used to predict ALNM in breast cancer and has significant potential in clinical noninvasive diagnosis and in the prediction of breast cancer metastasis.

Novel biomarkers for cholangiocarcinoma: how can it enhance diagnosis, prognostication, and investigational drugs? Part-1

INTRODUCTION

The development of novel biomarkers for cancer has exploded over the last decade with advances in novel technologies. Cholangiocarcinoma (CCA), a cancer of the bile ducts, has a dearth of strong disease and pathophysiology biomarkers, making early detection and prognostication a difficult task.

AREAS COVERED

In this comprehensive review, we discuss the spectrum of biomarkers for CCA diagnosis and prognostication. We elaborate on novel biomarker discovery through a comprehensive multi-omics approach. We also cover, how certain biomarkers may also serve as unique and potent targets for therapeutic development.

EXPERT OPINION

Despite the relatively poor diagnostic and prognostic performance of existing biomarkers for CCA, there is a vast range of novel biomarkers with exquisite diagnostic and prognostic performance for CCA in the pipeline. Moreover, these biomarkers may serve as potential targets for precision medicine. Existing strategies to target unique biomolecular classes are discussed, within the context of an overall 'omics' focused profiling strategy. Omics profiling will simultaneously allow for enhanced biomarker development and identification of unique subtypes of cholangiocarcinoma and how they are influenced by an individual's unique context. In this manner, patient management strategy and clinical trial design can be optimized to the individual.

Surgical Treatment of Intrahepatic Cholangiocarcinoma: Current and Emerging Principles

Intrahepatic cholangiocarcinoma (ICC) is a rare, aggressive cancer of the biliary tract. It often presents with locally advanced or metastatic disease, but for patients with early-stage disease, surgical resection with negative margins and portahepatis lymphadenectomy is the standard of care. Recent advancements in ICC include refinement of staging, improvement in liver-directed therapies, clarification of the role of adjuvant therapy based on new randomized controlled trials, and advances in minimally invasive liver surgery. In addition, improvements in neoadjuvant strategies and surgical techniques have enabled expanded surgical indications and reduced surgical morbidity and mortality. However, recurrence rates remain high and more effective systemic therapies are still necessary to improve recurrence-free and overall survival. In this review, we focus on current and emerging surgical principals for the management of ICC including preoperative evaluation, current indications for surgery, strategies for future liver remnant augmentation, technical principles, and the role of neoadjuvant and adjuvant therapies.

ADC cut points for chronic kidney disease in pathologically-proven cholangiocarcinoma

Purpose

Apparent diffusion coefficient (ADC) has been shown to indicate renal function in various conditions. As cholangiocarcinoma may have renal involvement due to immune complex-mediated glomerulonephritis, this study aimed to determine whether or not there is any association between ADC values and renal function in these patients.

Methods

This was a retrospective, analytical study. The inclusion criteria were age over 18 years, pathologically proven cholangiocarcinoma diagnosis and having undergone either 1.5 T or 3.0 T diffusion-weighted MRI. Chronic kidney disease (CKD) was defined as eGFR less than 60 mL/min/1.73m². Patients’ ADC levels in the CKD and non-CKD groups were compared, and subgroup analysis was performed by MRI field strength and type of cholangiocarcinoma.

Results

One hundred fifty-eight patients participated in the study. Most were male (66.46 %), and the average age (SD) was 61.59 years (7.91). Average ADC levels in the CDK and non-CDK group differed significantly, regardless of MRI field strength or type of cholangiocarcinoma (2.11 mm/s² in the ADC group vs 1.91 mm/s² in the non-ADC group; P < 0.001). An ADC cut-point of 1.75 mm/s² yielded sensitivities ranging from 66.67–90.00 in almost all study populations. The distal cholangiocarcinoma group had a perfect cut-point at 1.78 mm/s² with 100 % sensitivity and area under the ROC curve.

Conclusions

Radiologists can use ADC to detect CKD in cholangiocarcinoma patients regardless of MRI field strength or type of cholangiocarcinoma.