Advances in Artificial Intelligence and Biomarker Detection for Early Diagnosis and Management of Gallbladder Cancer

Gallbladder cancer (GBC) is a highly lethal malignancy with a poor prognosis, often due to the absence of specific and sensitive biomarkers, leading to delayed diagnosis. The development of artificial intelligence (AI) models capable of analyzing and predicting molecular data on a microscopic level offers a promising approach for early, cost-effective, and non-invasive diagnosis. Although GBC is rare, it is a frequently fatal biliary tract cancer, typically diagnosed at advanced stages. With an aging population and rising obesity rates, a significant increase in GBC incidence is projected by ۲۰۴۰. Surgical resection is currently the only curative option, but it is viable only in the early stages of the disease. For advanced-stage patients, multimodal treatment strategies are required. Early and precise preoperative evaluation is crucial for determining the most appropriate surgical approach, the extent of resection needed, and the potential use of novel adjuvant or neoadjuvant therapies. Due to non-specific symptoms, the absence of reliable diagnostic markers, and the lack of effective treatments, GBC is considered one of the most aggressive abdominal tumors. One study successfully applied a deep neural network (DNN)-based classification model to a comprehensive dataset, enabling the simultaneous detection of nine diseases and identification of the specific condition through a user interface. Given the lack of reliable diagnostic methods, GBC is often not detected until it reaches an advanced stage, further contributing to its poor prognosis. Recent research has shown that combining multiple tumor markers with deep learning algorithms provides excellent diagnostic and prognostic performance for GBC. Tumor markers such as CA۲۴۲, CA۱۹۹, CEA, and CA۱۲۵ have been identified as relevant, with the combination of CA۲۴۲ and CA۱۹۹ offering improved detection capabilities. Additionally, TNM stage IV, gallbladder cancer located in the neck region, and elevated CA۱۹۹ levels have been identified as independent risk factors for reduced survival rates in GBC patients. Results indicate that surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms holds promise as a future diagnostic tool for GBC. Gallbladder carcinoma is a rare yet aggressive malignancy with a poor prognosis, often detected at advanced stages. While surgical resection offers the only curative option, it is viable only in early-stage cases, necessitating multimodal treatment for advanced patients. Despite recent advancements, there remains a critical need for more effective diagnostic tools. Emerging research suggests that integrating tumor biomarkers with deep learning algorithms significantly enhances diagnostic accuracy, offering potential improvements in prognosis and treatment outcomes.

کلیدواژه ها:

Gallbladder cancer (GBC) ، Artificial intelligence (AI) ، Early diagnosis ، Tumor markers ، Deep neural networks (DNN)