Improve discrimination power of serum markers for diagnosis of cholangiocarcinoma using data mining-based approach

Establishment and evaluation of Voting algorithm-based internal quality control (ViQC), a patient-based real-time quality control

BACKGROUND

Patient-Based Real-Time Quality Control (PBRTQC) has emerged as a supplementary programme to traditional internal quality control (iQC) mechanisms. Despite its growing popularity, practical applications in clinical settings reveal several challenges. The primary objective of this research is to introduce and develop an Artificial Intelligence (AI)-based method, named Voting algorithm based iQC (ViQC), designed to enhance the precision and reliability of existing PBRTQC systems.

METHODS

In this study, we conducted a retrospective analysis of 111,925 inpatient serum glucose test results from Nanjing Drum Tower Hospital, Nanjing, China, to provide an unbiased data set. The Voting iQC (ViQC) algorithm, established by the principles of the Voting algorithm, was then developed. Its analytical performance was evaluated through the calculation of random errors (RE). Subsequently, its clinical efficacy was assessed by comparison with five statistical algorithms: Moving Average (MA), Exponentially Weighted Moving Average (EWMA), Moving Median (movMed, MM), Moving Quartile (MQ), and Moving Standard Deviation (MovSD).

RESULTS

The ViQC model incorporates a variety of machine learning models, including logistic regression, Bayesian methods, K-Nearest Neighbor, decision trees, random forests, and gradient boosting decision trees, to establish a robust predictive framework. This model consistently maintains a false positive rate below 0.002 across all six evaluated error factors, showcasing exceptional precision. Notably, its performance further excels with an error factor of 3.0, where the false positive rate drops below 0.001, and achieves an accuracy rate as high as 0.965 at an error factor of 2.0. The classification effectiveness of ViQC model is evaluated by an area under the curve (AUC) exceeding 0.97 for all error factors. In comparison to five conventional PBRTQC statistical methods, ViQC significantly enhances error detection efficiency, maximum reducing the trimmed average number of patient samples required for detecting errors from 724 to 168, thereby affirming its superior error detection capability.

CONCLUSION

The new established PBRTQC using artificial intelligence yielded satisfactory performance compared to the traditional PBBTQC in real world setting.

Bile level of cytokeratin 7 as a diagnostic marker for cholangiocarcinoma: a case-control study in Egyptian patients

Background

Cholangiocarcinoma (CCA) is an aggressive malignancy with a poor prognosis of less than 20% five-year survival rate. Early diagnosis is typically challenging due to asymptomatic characteristics at the earliest stages of the disease. This study aims to assess the potential utility of cytokeratin 7 (CK7) as a CCA diagnostic biomarker in bile. In total, 100 participants were included in this case-control study. Moreover, Group I had 30 CCA patients with malignant obstruction, and Group II had 20 patients with malignant biliary obstruction other than CCA formed. Group III included 20 patients with benign biliary obstruction, and 30 individuals undergoing cholecystectomy with no evidence of biliary obstruction made up the control group (Group IV). Bile samples were collected during endoscopic retrograde cholangiopancreatography or cholecystectomy for the control group. The CK7 levels in bile samples were measured using the enzyme-linked immunosorbent assay.

Results

The bile level of CK7 was significantly higher in cholangiocarcinoma patients (1555.4 ± 302.7 pg/mL) than those of the patients with malignancies other than CCA (581.9 ± 227.5 pg/mL), patients with benign obstruction (439.5 ± 255.7 pg/mL), and the control group (53 ± 26.4 pg/mL) (p value < 0.001). Furthermore, CK7 was significantly higher in CCA patients than in those with other malignancies (p value < 0.001). Patients with CCA with hilar lesions had the highest values compared to those with distal lesions. ROC curve analysis revealed that bile CK7 at a cut point of >1030 pg/mL yielded an area under a curve of 1 (95% CI: 1.000–1.000) in differentiating CCA from other groups.

Conclusion

The bile level of CK7 demonstrates outstanding performance that could help in diagnosing CCA.

Prognostic value of plasma microRNAs for non-small cell lung cancer based on data mining models

BACKGROUND

As biomarkers, microRNAs (miRNAs) are closely associated with the occurrence, progression, and prognosis of non-small cell lung cancer (NSCLC). However, the prognostic predictive value of miRNAs in NSCLC has rarely been explored. In this study, the value in prognosis prediction of NSCLC was mined based on data mining models using clinical data and plasma miRNAs biomarkers.

METHODS

A total of 69 patients were included in this prospective cohort study. After informed consent, they filled out questionnaires and had their peripheral blood collected. The expressions of plasma miRNAs were examined by quantitative polymerase chain reaction (qPCR). The Whitney U test was used to analyze non-normally distributed data. Kaplan-Meier was used to plot the survival curve, the log-rank test was used to compare with the overall survival curve, and the Cox proportional hazards model was used to screen the factors related to the prognosis of lung cancer. Data mining techniques were utilized to predict the prognostic status of patients.

RESULTS

We identified that smoking (HR = 2.406, 95% CI = 1.256-4.611), clinical stage III + IV (HR = 5.389, 95% CI = 2.290-12.684), the high expression group of miR-20a (HR = 4.420, 95% CI = 1.760-11.100), the high expression group of miR-197 (HR = 3.828, 95% CI = 1.778-8.245), the low expression group of miR-145 ( HR = 0.286, 95% CI = 0.116-0.709), and the low expression group of miR-30a (HR = 0.307, 95% CI = 0.133-0.706) was associated with worse prognosis. Among the five data mining models, the decision trees (DT) C5.0 model performs the best, with accuracy and Area Under Curve (AUC) of 93.75% and 0.929 (0.685, 0.997), respectively.

CONCLUSION

The results showed that the high expression level of miR-20a and miR-197, the low expression level of miR-145 and miR-30a were strongly associated with poorer prognosis in NSCLC patients, and the DT C5.0 model may serve as a novel, accurate, method for predicting prognosis of NSCLC.

EDLM: Ensemble Deep Learning Model to Detect Mutation for the Early Detection of Cholangiocarcinoma

The most common cause of mortality and disability globally right now is cholangiocarcinoma, one of the worst forms of cancer that may affect people. When cholangiocarcinoma develops, the DNA of the bile duct cells is altered. Cholangiocarcinoma claims the lives of about 7000 individuals annually. Women pass away less often than men. Asians have the greatest fatality rate. Following Whites (20%) and Asians (22%), African Americans (45%) saw the greatest increase in cholangiocarcinoma mortality between 2021 and 2022. For instance, 60-70% of cholangiocarcinoma patients have local infiltration or distant metastases, which makes them unable to receive a curative surgical procedure. Across the board, the median survival time is less than a year. Many researchers work hard to detect cholangiocarcinoma, but this is after the appearance of symptoms, which is late detection. If cholangiocarcinoma progression is detected at an earlier stage, then it will help doctors and patients in treatment. Therefore, an ensemble deep learning model (EDLM), which consists of three deep learning algorithms-long short-term model (LSTM), gated recurrent units (GRUs), and bi-directional LSTM (BLSTM)-is developed for the early identification of cholangiocarcinoma. Several tests are presented, such as a 10-fold cross-validation test (10-FCVT), an independent set test (IST), and a self-consistency test (SCT). Several statistical techniques are used to evaluate the proposed model, such as accuracy (Acc), sensitivity (Sn), specificity (Sp), and Matthew's correlation coefficient (MCC). There are 672 mutations in 45 distinct cholangiocarcinoma genes among the 516 human samples included in the proposed study. The IST has the highest Acc at 98%, outperforming all other validation approaches.

High expression of protein tyrosine phosphatase receptor S (PTPRS) is an independent prognostic marker for cholangiocarcinoma

Cholangiocarcinoma (CCA) is an aggressive tumor of the bile duct with a high rate of mortality. Lymph node metastasis is an important factor facilitating the progression of CCA. A reliable biomarker for diagnosis, progression status, or prognosis of CCA is still lacking. To identify a novel and reliable biomarker for diagnosis/prognosis of CCA, liquid chromatography-mass spectrometry and tandem mass spectrometry (LC-MS/MS) in combination with bioinformatics analysis were applied for the representative serum samples of patients with CCA. The proteome results showed that protein tyrosine phosphatase receptor S (PTPRS) had the highest potential candidate. Then, a dot blot assay was used to measure the level of serum PTPRS in patients with CCA (n = 80), benign biliary disease patients (BBD; n = 39), and healthy controls (HC; n = 55). PTPRS level of CCA sera (14.38 ± 9.42 ng/ml) was significantly higher than that of BBD (10.7 ± 5.05 ng/ml) or HC (6 ± 3.73 ng/ml) (P < 0.0001). PTPRS was associated with serum albumin (P = 0.028), lymph node metastasis (P = 0.038), and the survival time of patients (P = 0.011). Using a log-rank test, higher serum PTPRS level was significantly (P = 0.031) correlated with a longer overall survival time of patients with CCA, and PTPRS was an independent prognostic marker for CCA superior to carbohydrate antigen 19-9 (CA19-9), carcinoembryonic antigen (CEA) or alkaline phosphatase (ALP). High expression of PTPRS could be a good independent prognostic marker for CCA.

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.

Artificial intelligence and cholangiocarcinoma: Updates and prospects

Artificial intelligence (AI) is the timeliest field of computer science and attempts to mimic cognitive function of humans to solve problems. In the era of “Big data”, there is an ever-increasing need for AI in all aspects of medicine. Cholangiocarcinoma (CCA) is the second most common primary malignancy of liver that has shown an increase in incidence in the last years. CCA has high mortality as it is diagnosed in later stages that decreases effect of surgery, chemotherapy, and other modalities. With technological advancement there is an immense amount of clinicopathologic, genetic, serologic, histologic, and radiologic data that can be assimilated together by modern AI tools for diagnosis, treatment, and prognosis of CCA. The literature shows that in almost all cases AI models have the capacity to increase accuracy in diagnosis, treatment, and prognosis of CCA. Most studies however are retrospective, and one study failed to show AI benefit in practice. There is immense potential for AI in diagnosis, treatment, and prognosis of CCA however limitations such as relative lack of studies in use by human operators in improvement of survival remains to be seen.

Establishment of a Potential Serum Biomarker Panel for the Diagnosis and Prognosis of Cholangiocarcinoma Using Decision Tree Algorithms

Potential biomarkers which include S100 calcium binding protein A9 (S100A9), mucin 5AC (MUC5AC), transforming growth factor β1 (TGF-β1), and angiopoietin-2 have previously been shown to be effective for cholangiocarcinoma (CCA) diagnosis. This study attempted to measure the sera levels of these biomarkers compared with carbohydrate antigen 19-9 (CA19-9). A total of 40 serum cases of CCA, gastrointestinal cancers (non-CCA), and healthy subjects were examined by using an enzyme-linked immunosorbent assay. The panel of biomarkers was evaluated for their accuracy in diagnosing CCA and subsequently used as inputs to construct the decision tree (DT) model as a basis for binary classification. The findings showed that serum levels of S100A9, MUC5AC, and TGF-β1 were dramatically enhanced in CCA patients. In addition, 95% sensitivity and 90% specificity for CCA differentiation from healthy cases, and 70% sensitivity and 83% specificity for CCA versus non-CCA cases was obtained by a panel incorporating all five candidate biomarkers. In CCA patients with low CA19-9 levels, S100A9 might well be a complementary marker for improved diagnostic accuracy. The high levels of TGF-β1 and angiopoietin-2 were both associated with severe tumor stages and metastasis, indicating that they could be used as a reliable prognostic biomarkers panel for CCA patients. Furthermore, the outcome of the CCA burden from the Classification and Regression Tree (CART) algorithm using serial CA19-9 and S100A9 showed high diagnostic efficiency. In conclusion, results have shown the efficacy of CCA diagnosis and prognosis of the novel CCA-biomarkers panel examined herein, which may prove be useful in clinical settings.

Potential value and impact of data mining and machine learning in clinical diagnostics

Data mining involves the use of mathematical sciences, statistics, artificial intelligence, and machine learning to determine the relationships between variables from a large sample of data. It has previously been shown that data mining can improve the prediction and diagnostic precision of type 2 diabetes mellitus. A few studies have applied machine learning to assess hypertension and metabolic syndrome-related biomarkers, as well as refine the assessment of cardiovascular disease risk. Machine learning methods have also been applied to assess new biomarkers and survival outcomes in patients with renal diseases to predict the development of chronic kidney disease, disease progression, and renal graft survival. In the latter, random forest methods were found to be the best for the prediction of chronic kidney disease. Some studies have investigated the prognosis of nonalcoholic fatty liver disease and acute liver failure, as well as therapy response prediction in patients with viral disorders, using decision tree models. Machine learning techniques, such as Sparse High-Order Interaction Model with Rejection Option, have been used for diagnosing Alzheimer's disease. Data mining techniques have also been applied to identify the risk factors for serious mental illness, such as depression and dementia, and help to diagnose and predict the quality of life of such patients. In relation to child health, some studies have determined the best algorithms for predicting obesity and malnutrition. Machine learning has determined the important risk factors for preterm birth and low birth weight. Published studies of patients with cancer and bacterial diseases are limited and should perhaps be addressed more comprehensively in future studies. Herein, we provide an in-depth review of studies in which biochemical biomarker data were analyzed using machine learning methods to assess the risk of several common diseases, in order to summarize the potential applications of data mining methods in clinical diagnosis. Data mining techniques have now been increasingly applied to clinical diagnostics, and they have the potential to support this field.

Cholangiocarcinoma Evaluation via Imaging and Artificial Intelligence

BACKGROUND

Cholangiocarcinoma (CCA) is a relatively rare malignant biliary system tumor, and yet it represents the second most common primary hepatic neoplasm, following hepatocellular carcinoma. Regardless of the type, location, or etiology, the survival prognosis of these tumors remains poor. The only method of cure for CCA is complete surgical resection, but part of patients with complete resection are still subject to local recurrence or distant metastasis.

SUMMARY

Over the last several decades, our understanding of the molecular biology of CCA has increased tremendously, diagnostic and evaluative techniques have evolved, and novel therapeutic approaches have been established. Key Messages: This review provides an overview of preoperative imaging evaluations of CCA. Furthermore, relevant information about artificial intelligence (AI) in medical imaging is discussed, as well as the development of AI in CCA treatment.

Multi-serum glycobiomarkers improves the diagnosis and prognostic prediction of cholangiocarcinoma

BACKGROUND

Aberrant glycosylation has been reported to play important roles in progression of cholangiocarcinoma (CCA) and hence the aberrant expressed glycans are beneficial markers for diagnosis and prognostic prediction of CCA.

METHODS

Five CCA-associated glycobiomarkers-carbohydrate antigen 19-9 (CA19-9), carbohydrate antigen-S27 (CA-S27), CCA-associated carbohydrate antigen (CCA-CA), WFA-positive MUC1 (WFA⁺-MUC1), and WFA-positive M2BP (WFA⁺-M2BP), in the sera from CCA patients (N = 138) were determined in comparison with non-CCA control subjects (N = 246).

RESULTS

Receiver operating characteristic analysis suggested the significance of each glycobiomarker in discriminating CCA from non-CCA with area under curve of 0.580-0.777. High levels of CA19-9, CCA-CA, CA-S27, or WFA⁺-MUC1 were associated with poor prognosis and poor survival of CCA patients. Combination of these glycobiomarkers and graded as a GlycoBiomarker (GB)-score could increase the power of the tests in diagnosis than an individual marker with 81% of sensitivity, specificity and accuracy.

CONCLUSIONS

According to the GB-score, these glycobiomarkers not only increased diagnostic power but also discriminated survival of patients indicating the diagnostic and prognostic values of GB-score.

HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving

Energy efficiency has aroused great interest in research worldwide, because energy consumption has increased in recent years, especially in the residential sector. The advances in energy conversion, along with new forms of communication, and information technologies have paved the way for what is now known as smart homes. The Internet of Things (IoT) is the convergence of various heterogeneous technologies from different application domains that are used to interconnect things through the Internet, thus allowing for the detection, monitoring, and remote control of multiple devices. Home automation systems (HAS) combined with IoT, big data technologies, and machine learning are alternatives that promise to contribute to greater energy efficiency. This work presents HEMS-IoT, a big data and machine learning-based smart home energy management system for home comfort, safety, and energy saving. We used the J48 machine learning algorithm and Weka API to learn user behaviors and energy consumption patterns and classify houses with respect to energy consumption. Likewise, we relied on RuleML and Apache Mahout to generate energy-saving recommendations based on user preferences to preserve smart home comfort and safety. To validate our system, we present a case study where we monitor a smart home to ensure comfort and safety and reduce energy consumption.

PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control

Overweight and obesity are affecting productivity and quality of life worldwide. The Internet of Things (IoT) makes it possible to interconnect, detect, identify, and process data between objects or services to fulfill a common objective. The main advantages of IoT in healthcare are the monitoring, analysis, diagnosis, and control of conditions such as overweight and obesity and the generation of recommendations to prevent them. However, the objects used in the IoT have limited resources, so it has become necessary to consider other alternatives to analyze the data generated from monitoring, analysis, diagnosis, control, and the generation of recommendations, such as machine learning. This work presents PISIoT: a machine learning and IoT-based smart health platform for the prevention, detection, treatment, and control of overweight and obesity, and other associated conditions or health problems. Weka API and the J48 machine learning algorithm were used to identify critical variables and classify patients, while Apache Mahout and RuleML were used to generate medical recommendations. Finally, to validate the PISIoT platform, we present a case study on the prevention of myocardial infarction in elderly patients with obesity by monitoring biomedical variables.

Serum pyruvate dehydrogenase kinase as a prognostic marker for cholangiocarcinoma

Pyruvate dehydrogenase kinase (PDK) is a Ser/Thr kinase that inactivates mitochondrial pyruvate dehydrogenase and serves a key role in aerobic glycolysis, which is a hallmark of cancer cells. The present study determined the PDK expression in cholangiocarcinoma (CCA) tissues and sera to evaluate their applicability as a biomarker for CCA. Using proteomic analysis, PDK was revealed to be the most overexpressed mitochondrial protein in CCA tissues. Then, the expression of PDK isoforms in CCA tissues was examined in 15 CCA cases by immunohistochemistry. The PDK3 isoform levels in the sera were measured using a dot blot assay for 39 patients with CCA, 20 patients with benign biliary disease and 19 healthy volunteers. The results revealed a 27-fold overexpression of PDK3 in cancerous tissues when compared with adjacent non-cancerous tissues. The immunohistochemical results demonstrated that the PDK1, 2 and 3, but not the PDK4, isoforms were overexpressed in cancerous tissues. When the PDK3 levels in the sera were examined, they were significantly higher in CCA when compared with the BBD and healthy groups. The specificity and sensitivity of PDK3 as a marker for CCA were 97.5 and 33.0%, respectively, and high PDK3 levels in the sera were correlated with a short survival time for CCA. In conclusion, PDK3 can be used as a diagnostic/prognostic marker for CCA.

Biomarkers for the Diagnosis of Cholangiocarcinoma: A Systematic Review

Cholangiocarcinoma (CCA), a malignant tumor of the bile duct, is a major public health problem in many Southeast Asian countries, particularly Thailand. The slow progression makes it difficult for early diagnosis and most patients are detected in advanced stages. This study aimed to review all relevant articles related to the biomarkers for the diagnosis of CCA and point out potential biomarkers. A thorough search was performed in PubMed and ScienceDirect for CCA biomarker articles. Required data were extracted. A total of 46 articles that fulfilled the inclusion and had none of the exclusion criteria were included in the analysis (17, 22, 3, 4, and 1 articles on blood, tissue, bile, both blood and tissue, and urine biomarkers, respectively). Carbohydrate antigen 19-9 (CA19-9) and carcinoembryonic antigen (CEA), either alone or in combination with other biomarkers, are the most commonly studied biomarkers in the serum. Their sensitivity and specificity ranged from 47.2% to 98.2% and 89.7% to 100%, respectively. However, in the tissue, gene methylations and DNA-related markers were the most studied CCA biomarkers. Their sensitivity and specificity ranged from 58% to 87% and 98% to 100%, respectively. Some articles investigated biomarkers both in blood and tissues, particularly CA19-9 and CEA, with sensitivity and specificity ranging from 33% to 100% and 50% to 97.7%, respectively. Although quite a number of biomarkers with a potential role in the early detection of CCA have been established, it is difficult to single out any particular marker that could be used in the routine clinical settings.

Diagnosis and Management of Intrahepatic and Extrahepatic Cholangiocarcinoma

Cholangiocarcinomas (CC) are rare tumors which usually present late and are often difficult to diagnose and treat. CCs are categorized as intrahepatic, hilar, or extrahepatic. Epidemiologic studies suggest that the incidence of intrahepatic CCs may be increasing worldwide. In this chapter, we review the risk factors, clinical presentation, and management of cholangiocarcinoma.

Clinical significance of GalNAcylated glycans in cholangiocarcinoma: Values for diagnosis and prognosis

Cancer cells exhibited the aberrant cancer-associated glycans that are potential biomarkers for diagnosis and monitoring of the cancer. In this study, Sophora japonica agglutinin (SJA) was used to detect SJA-specific N-acetylgalactosamine-associated glycans (SNAG) in liver tissues and sera from cholangiocarcinoma (CCA) patients. Whether SNAG could be the diagnostic and prognostic markers for CCA was evaluated. SJA-histochemistry revealed that SNAG was undetec2 in normal bile ducts but was highly expressed in hyperplastic/dysplastic bile ducts and CCA. SNAG was negative in hepatocytes and hepatoma tissues indicating SNAG as a differential marker of CCA and hepatoma. SJA-histochemistry of CCA hamster tissues revealed the involvement of SNAG in the early pathogenesis of bile duct epithelia and CCA development. A SJA-based ELISA was successfully developed to determine SNAG in serum. Serum-SNAG from CCA patients was significantly higher than those of non-CCA control groups with the diagnostic values of 59.5% sensitivity and 73.6% specificity, comparable to those of serum CA19-9. High levels of serum SNAG (≥69AU/ml) indicated poor survival of CCA patients. Taken together, SNAG was first demonstrated here to be a glycobiomarker for diagnosis and prognosis of CCA. Association of SNAG with pathogenesis of bile ducts and CCA development were suggested. (198).

Cholangiocarcinoma: Current opinion on clinical practice diagnostic and therapeutic algorithms: A review of the literature and a long-standing experience of a referral center

In the oncology landscape, cholangiocarcinoma is a challenging disease in terms of both diagnosis and treatment. Besides anamnesis and clinical examination, a definitive diagnosis of cholangiocarcinoma should be supported by imaging techniques (US, CT, MRI) and invasive investigations (ERC or EUS with brushing and FNA or US or CT-guided biopsy) followed by pathological confirmation. Surgery is the main curative option, so resectability of the tumour should be promptly assessed. Moreover, jaundice must be evaluated at the outset because biliary tract decompression with drainage and stent placement may be required. If the patient is resectable, pre-operative assessment of postoperative liver function is mandatory. After a curative resection, an adjuvant therapy may be administered. Otherwise, in cases with macroscopic residual disease after surgery or locally recurrent or unresectable cholangiocarcinoma at the diagnosis, first-line chemotherapy is the preferred strategy, possibly associated with radiotherapy and/or locoregional treatments. As the diagnostic and therapeutic pathway for cholangiocarcinoma can be declined in different modalities, patients should be promptly referred to a multidisciplinary team in a tertiary centre, familiar with this rare but lethal disease. Hence, the aim of the present paper is to focus on diagnostic and therapeutic algorithms based on the common guidelines and also on the clinical practice of multispecialist expert groups.

Diagnostic Accuracy of Serum CA19-9 in Patients with Cholangiocarcinoma: A Systematic Review and Meta-Analysis

BACKGROUND Cholangiocarcinoma (CCA) is a relatively rare cancer worldwide; however, its incidence is extremely high in Asia. Numerous studies reported that serum carbohydrate antigen 19-9 (CA19-9) plays a role in the diagnosis of CCA patients. However, published data are inconclusive. The aim of this meta-analysis was to provide a systematic review of the diagnostic performance of CA19-9 for CCA. MATERIAL AND METHODS We searched the public databases including PubMed, Web of Science, Embase, Chinese National Knowledge Infrastructure (CNKI), and WANFANG databases for articles evaluating the diagnostic accuracy of serum CA19-9 to predict CCA. The diagnostic sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and summary receiver operating characteristic curve (SROC) were pooled by Meta-DiSc 1.4 software. RESULTS A total of 31 articles met the inclusion criteria, including 1,264 patients and 2,039 controls. The pooled SEN, SPE, PLR, NLR, and DOR were 0.72 (95% CI: 0.70-0.75), 0.84 (95% CI: 0.82-0.85), 4.93 (95% CI, 3.67-6.64), 0.35 (95%CI, 0.30-0.41), and 15.10 (95% CI, 10.70-21.32), respectively. The area under SROC curve was 0.8300. The subgroup analyses based on different control type, geographical location, and sample size revealed that the diagnostic accuracy of CA19-9 tends to be same in different control type, but showed low sensitivity in European patients and small size group. CONCLUSIONS Serum CA19-9 is a useful non-invasive biomarker for CCA detection and may become a clinically useful tool to identify high-risk patients.