The diagnosis of cholangiocarcinoma (CCA) remains challenging. Although new technologies have been developed and validated, their routine use in clinical practice is needed. Conventional cytology obtained during endoscopic retrograde cholangiopancreatography-guided brushings is the first-line technique for the diagnosis of CCA, but it has shown limited sensitivity when combined with endoscopic ultrasound-guided biopsy. Other diagnostic tools have been proposed for the diagnosis of CCA, with their respective advantages and limitations. Cholangioscopy with biopsy or cytology combined with FISH analysis, intraductal biliary ultrasound and confocal laser microscopy have made significant advances in the last decade. More recently, developments in the analytical "omics" sciences have allowed the mapping of the microbiota of patients with CCA, and liquid biopsy with proteomic and extracellular vesicle analysis has allowed the identification of new biomarkers that can be incorporated into the predictive diagnostics. Furthermore, in the preoperative setting, radiomics, radiogenomics and the integrated use of artificial intelligence may provide new useful foundations for integrated diagnosis and personalized therapy for hepatobiliary diseases. This review aims to evaluate the current diagnostic approaches and innovative translational research that can be integrated for the diagnosis of CCA.

Cholangiocarcinoma (CCA) is a malignant biliary system tumor and the second most common primary hepatic neoplasm, following hepatocellular carcinoma. CCA still has an extremely high unfavorable prognosis, regardless of type and location, and complete surgical resection remains the only curative therapeutic option; however, due to the underhanded onset and rapid progression of CCA, most patients present with advanced stages at first diagnosis, with only 30 to 60% of CCA patients eligible for surgery. Recent innovations in medical imaging combined with the use of radiomics and artificial intelligence (AI) can lead to improvements in the early detection, characterization, and pre-treatment staging of these tumors, guiding clinicians to make personalized therapeutic strategies. The aim of this review is to provide an overview of how radiological features of CCA can be analyzed through radiomics and with the help of AI for many different purposes, such as differential diagnosis, the prediction of lymph node metastasis, the defining of prognostic groups, and the prediction of early recurrence. The combination of radiomics with AI has immense potential. Still, its effectiveness in practice is yet to be validated by prospective multicentric studies that would allow for the development of standardized radiomics models.

Background: Despite the significant advancements of liver surgery in the last few decades, the survival rate of patients with liver and pancreatic cancers has improved by only 10% in 30 years. Precision medicine offers a patient-centered approach, which, when combined with machine learning, could enhance decision making and treatment outcomes in surgical management of ihCC. This study aims to develop a decision support model to optimize treatment strategies for patients with ihCC, a prevalent primary liver cancer. Methods: The decision support model, named MedMax, was developed using three data sources: studies retrieved through a systematic literature review, expert opinions from HPB surgeons, and data from ihCC patients treated at Heidelberg University Hospital. Expert opinions were collected via surveys, with factors rated on a Likert scale, while patient data were used to validate the model's accuracy. Results: The model is structured into four decision-making phases, assessing diagnosis, treatment modality, surgical approach, and prognosis. Prospectively, 44 patients with ihCC were included for internal primary validation of the model. MedMax could predict the appropriate treatment considering the resectability of the lesions in 100% of patients. Also, MedMax could predict a decent surgical approach in 77% of the patients. The model proved effective in making decisions regarding surgery and patient management, demonstrating its potential as a clinical decision support tool. Conclusions: MedMax offers a transparent, personalized approach to decision making in HPB surgery, particularly for ihCC patients. Initial results show high accuracy in treatment selection, and the model's flexibility allows for future expansion to other liver tumors and HPB surgeries. Further validation with larger patient cohorts is required to enhance its clinical utility.

Biliary tract cancers are malignant neoplasms arising from bile duct epithelial cells. They include cholangiocarcinomas and gallbladder cancer. Gallbladder cancer has a marked geographical preference and is one of the most common cancers in women in northern India. Biliary tract cancers are usually diagnosed at an advanced, unresectable stage. Hence, the prognosis is extremely dismal. The five-year survival rate in advanced gallbladder cancer is < 5%. Hence, early detection and radical surgery are critical to improving biliary tract cancer prognoses. Radiological imaging plays an essential role in diagnosing and managing biliary tract cancers. However, the diagnosis is challenging because the biliary tract is affected by many diseases that may have radiological appearances similar to cancer. Artificial intelligence (AI) can improve radiologists' performance in various tasks. Deep learning (DL)-based approaches are increasingly incorporated into medical imaging to improve diagnostic performance. This paper reviews the AI-based strategies in biliary tract cancers to improve the diagnosis and prognosis.

Surgery represents the only cure for hilar cholangiocarcinoma (HC). However, laparoscopic radical resection remains technically challenging owing to the complex anatomy and reconstruction required during surgery. Therefore, reports on laparoscopic surgery (LS) for HC, especially for types III and IV, are limited. This study aimed to evaluate the safety and feasibility of laparoscopic radical surgery for Bismuth types III and IV HC.

The data of 16 patients who underwent LS and 9 who underwent open surgery (OS) for Bismuth types III and IV HC at Mianyang Central Hospital, School of Medicine, University of Electronic Science and Technology of China, between December 2017 and January 2022 were analyzed. Basic patient information, Bismuth-Corlette type, AJCC staging, postoperative complications, pathological findings, and follow-up results were evaluated.

Sixteen patients underwent LS and 9 underwent OS for HC. According to the preoperative imaging data, there were four cases of Bismuth type IIIa, eight of type IIIb, and four of type IV in the LS group and two of type IIIa, four of type IIIb, and three of type IV in the OS group (P>0.05). There were no significant differences in age, sex, ASA score, comorbidity, preoperative percutaneous transhepatic biliary drainage rate, history of abdominal surgery, or preoperative laboratory tests between the two groups (P>0.05). Although the mean operative time and mean intraoperative blood loss were higher in the LS group than in OS group, the differences were not statistically significant (P=0.121 and P=0.115, respectively). Four patients (25%) in the LS group and two (22.2%) in the OS group experienced postoperative complications (P>0.05). No significant differences were observed in other surgical outcomes and pathologic findings between the two groups. Regarding the tumor recurrence rate, there was no difference between the groups (P>0.05) during the follow-up period (23.9 ± 13.3 months vs. 17.8 ± 12.3 months, P=0.240).

Laparoscopic radical resection of Bismuth types III and IV HC remains challenging, and extremely delicate surgical skills are required when performing extended hemihepatectomy followed by complex bilioenteric reconstructions. However, this procedure is generally safe and feasible for hepatobiliary surgeons with extensive laparoscopy experience.

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.

This paper offers an assessment of radiomics tools in the evaluation of intrahepatic cholangiocarcinoma.

The PubMed database was searched for papers published in the English language no earlier than October 2022.

We found 236 studies, and 37 satisfied our research criteria. Several studies addressed multidisciplinary topics, especially diagnosis, prognosis, response to therapy, and prediction of staging (TNM) or pathomorphological patterns. In this review, we have covered diagnostic tools developed through machine learning, deep learning, and neural network for the recurrence and prediction of biological characteristics. The majority of the studies were retrospective.

It is possible to conclude that many performing models have been developed to make differential diagnosis easier for radiologists to predict recurrence and genomic patterns. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.

Pancreatic ductal adenocarcinoma (PDAC) and distal cholangiocarcinoma (dCCA) are very aggressive tumors with a high mortality rate. Pancreas and distal bile ducts share a common embryonic development. Hence, PDAC and dCCA exhibit similar histological features that make a differential diagnosis during routine diagnostic practice challenging. However, there are also significant differences, with potential clinical implications. Even if PDAC and dCCA are generally associated with poor survival, patients with dCCA seem to present a better prognosis. Moreover, although precision oncology-based approaches are still limited in both entities, their most important targets are different and include alterations affecting BRCA1/2 and related genes in PDAC, as well as HER2 amplification in dCCA. Along this line, microsatellite instability represents a potential contact point in terms of tailored treatments, but its prevalence is very low in both tumor types. This review aims at defining the most important similarities and differences in terms of clinicopathological and molecular features between these two entities, also discussing the main theranostic implications derived from this challenging differential diagnosis.

Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare primary liver cancer which displays clinicopathologic features of both hepatocellular (HCC) and cholangiocellular carcinoma (CCA). The similarity to HCC and CCA makes the diagnostic workup particularly challenging. Alpha-fetoprotein (AFP) and carbohydrate antigen 19-9 (CA 19-9) are blood tumour markers related with HCC and CCA, respectively. They can be used as diagnostic markers in cHCC-CCA as well, albeit with low sensitivity. The imaging features of cHCC-CCA overlap with those of HCC and CCA, dependent on the predominant histopathological component. Using the Liver Imaging and Reporting Data System (LI-RADS), as many as half of cHCC-CCAs may be falsely categorised as HCC. This is especially relevant since the diagnosis of HCC may be made without histopathological confirmation in certain cases. Thus, in instances of diagnostic uncertainty (e.g., simultaneous radiological HCC and CCA features, elevation of CA 19-9 and AFP, HCC imaging features and elevated CA 19-9, and vice versa) multiple image-guided core needle biopsies should be performed and analysed by an experienced pathologist. Recent advances in the molecular characterisation of cHCC-CCA, innovative diagnostic approaches (e.g., liquid biopsies) and methods to analyse multiple data points (e.g., clinical, radiological, laboratory, molecular, histopathological features) in an all-encompassing way (e.g., by using artificial intelligence) might help to address some of the existing diagnostic challenges.

A novel mathematical diagnostic analysis using Fourier transform (FT) algorithm of the extrahepatic bile duct (BD) using magnetic resonance-cholangiography (MRC) was performed to evaluate irregularities of the bile duct lumen indicating BD cancer (BDC) extension compared to pancreatic head malignancies controls.

BD lumen was automatically traced, and a 2D-diagram cross-section was measured and a FT-based integrated-power-spectral-density-function value (FTV) of both diameter and area (mm ²  and mm ⁴ /Hz) was calculated for cancerous and non-cancerous parts utilizing a computer workstation.

FT analysis that was achieved in 59 patients consisted of BDC in 31, pancreatic cancer with biliary stenosis (PC) in 10 and pancreatic neoplasm without stenosis (PN) in 18. FTV-diameter and -area of non-tumorous proximal BD were larger compared to tumor involving BD (p<0.01), and those of the entire BD in BDCs were significantly larger than those in PN (p<0.01). FTV-diameter and -area in proximal BDC-positive were smaller than those in BDC-negative (p<0.05). BDC part was significantly discriminated by the cutoff value (286 mm ² Hz ^-1  in diameter and 10,311 mm ⁴ Hz ^-1 in area) compared to PC and diagnostic accuracy was over 70% (p<0.01).

Novel mathematical MRC FT-analysis is promising for differentiating between BDC and PC with biliary stenosis and can be utilized as an objective diagnostic tool in the future.