Immunotherapy and targeted therapy for cholangiocarcinoma: Artificial intelligence research in imaging

Machine learning prediction of perineural invasion in intrahepatic cholangiocarcinoma

OBJECTIVE

Perineural invasion (PNI) significantly influences postoperative recurrence and survival in intrahepatic cholangiocarcinoma (ICC) patients. This study aims to develop and validate an interpretable model that can be used to predict PNI in ICC cases before surgery.

METHODS

Retrospective clinical information was gathered from ICC patients (n = 250) at our hospital, covering the period from January 2012 to January 2022. The patients were randomly assigned to the training group (n = 176, 70.4 %) and validation group (n = 74, 29.6 %). We employed four machine learning algorithms to establish prediction models, each model's performance was assessed via a receiver operating characteristic (ROC) curve. Decision Curve Analysis (DCA) was performed to evaluate the models' risks and benefits. SHapley Additive exPlanations (SHAP) were used to quantify the contributions of model features, providing both global and local interpretations.

RESULTS

Significant differences in tumor size, tumor number, lymph node metastasis, CA199, distant metastasis ratio, HBsAg, PLR, and NLR were observed between the PNI[-] (n = 172, 68.8 %) and PNI[+](n = 78, 31.2 %) groups. The PFS and OS rates in the PNI[-] group were better than those in the PNI[+] group. Based on the evaluation of the validation group, the XGBoost model demonstrated the best predictive performance. SHAP analysis identified tumor number, tumor size, and lymph node metastasis as the top three factors predicting PNI in ICC patients.

CONCLUSION

We developed a reliable predictive model that effectively predicts PNI status in patients with ICC and facilitates personalized clinical decision-making.

Artificial Intelligence for Drug Discovery: An Update and Future Prospects

Artificial intelligence (AI) has become a pivotal tool for medical image analysis, significantly enhancing drug discovery through improved diagnostics, staging, prognostication, and response assessment. At a high level, AI-driven image analysis enables the quantification and synthesis of previously qualitative imaging characteristics, facilitating the identification of novel disease-specific biomarkers, patient risk stratification, prognostication, and adverse event prediction. In addition, AI can assist in response assessment by capturing changes in imaging "phenotype" over time, allowing for optimized treatment plans based on real-time analysis. Integrating this emerging technology into drug discovery pipelines has the potential to accelerate the identification and development of new pharmaceuticals by assisting in target identification and patient selection, as well as reducing the incidence, and therefore cost, of failed trials through high-throughput, reproducible, and data-driven insights. Continued progress in AI applications will shape the future of medical imaging, ultimately fostering more efficient, accurate, and tailored drug discovery processes. Herein, we offer a comprehensive overview of how AI enhances medical imaging to inform drug development and therapeutic strategies.

Gut Microbiome and Bile Acid Interactions: Mechanistic Implications for Cholangiocarcinoma Development, Immune Resistance, and Therapy

Cholangiocarcinoma (CCA) is a rare but highly malignant carcinoma of bile duct epithelial cells with a poor prognosis. The major risk factors of CCA carcinogenesis and progression are cholestatic liver diseases. The key feature of primary sclerosing cholangitis and primary biliary cholangitis is chronic cholestasis. It indicates a slowdown of hepatocyte secretion of biliary lipids and metabolites into bile as well as a slowdown of enterohepatic circulation (bile acid recirculation) of bile acids with dysbiosis of the gut microbiome. This leads to enterohepatic recirculation and an increase of toxic secondary bile acids. Alterations of serum and liver bile acid compositions via the disturbed enterohepatic circulation of bile acids and the disturbance of the gut microbiome then activate a series of hepatic and cancer cell signaling pathways that promote CCA carcinogenesis and progression. This review focuses on the mechanistic roles of bile acids and the gut microbiome in the pathogenesis and progression of CCA. It also evaluates the therapeutic potential of targeting the gut microbiome and bile acid-mediated signaling pathways for the therapy and prophylaxis of CCA.

Research trends and hotspots evolution of artificial intelligence for cholangiocarcinoma over the past 10 years: a bibliometric analysis

Objective

To analyze the research hotspots and potential of Artificial Intelligence (AI) in cholangiocarcinoma (CCA) through visualization.

Methods

A comprehensive search of publications on the application of AI in CCA from January 1, 2014, to December 31, 2023, within the Web of Science Core Collection, was conducted, and citation information was extracted. CiteSpace 6.2.R6 was used for the visualization analysis of citation information.

Results

A total of 736 publications were included in this study. Early research primarily focused on traditional treatment methods and care strategies for CCA, but since 2019, there has been a significant shift towards the development and optimization of AI algorithms and their application in early cancer diagnosis and treatment decision-making. China emerged as the country with the highest volume of publications, while Khon Kaen University in Thailand was the academic institution with the highest number of publications. A core group of authors involved in a dense network of international collaboration was identified. HEPATOLOGY was found to be the most influential journal in the field. The disciplinary development pattern in this domain exhibits the characteristic of multiple disciplines intersecting and integrating.

Conclusion

The current research hotspots primarily revolve around three directions: AI in the diagnosis and classification of CCA, AI in the preoperative assessment of cancer metastasis risk in CCA, and AI in the prediction of postoperative recurrence in CCA. The complementarity and interdependence among different AI applications will facilitate future applications of AI in the CCA field.

Strategies for treating the cold tumors of cholangiocarcinoma: core concepts and future directions

Cholangiocarcinoma (CCA) is a rare type of digestive tract cancer originating from the epithelial cells of the liver and biliary tract. Current treatment modalities for CCA, such as chemotherapy and radiation therapy, have demonstrated limited efficacy in enhancing survival rates. Despite the revolutionary potential of immunotherapy in cancer management, its application in CCA remains restricted due to the minimal infiltration of immune cells in these tumors, rendering them cold and unresponsive to immune checkpoint inhibitors (ICIs). Cancer cells within cold tumors deploy various mechanisms for evading immune attack, thus impeding clinical management. Recently, combination immunotherapy has become increasingly essential to comprehend the mechanisms underlying cold tumors to enhance a deficient antitumor immune response. Therefore, a thorough understanding of the knowledge on the combination immunotherapy of cold CCA is imperative to leverage the benefits of immunotherapy in treating patients. Moreover, gut microbiota plays an essential role in the immunotherapeutic responses in CCA. In this review, we summarize the current concepts of immunotherapy in CCA and clarify the intricate dynamics within the tumor immune microenvironment (TIME) of CCA. We also delve into the evasion mechanisms employed by CCA tumors against the anti-tumor immune responses. The context of combination immunotherapies in igniting cold tumors of CCA and the critical function of gut microbiota in prompting immune responses have also been annotated. Furthermore, we have proposed future directions in the realm of CCA immunotherapy, aiming to improve the clinical prognosis of CCA patients.