Gallbladder carcinoma in the era of artificial intelligence: Early diagnosis for better treatment

Abstract

Gallbladder carcinoma (GBC) is the most common malignant tumor of biliary tract, with poor prognosis due to its aggressive nature and limited therapeutic options. Early detection of GBC is a major challenge, with most GBCs being detected accidentally during cholecystectomy procedures for gallbladder stones. This letter comments on the recent article by Deqing et al in the World Journal of Gastrointestinal Oncology , which summarized the various current methods used in early diagnosis of GBC, including endoscopic ultrasound (EUS) examination of the gallbladder for high-risk GBC patients, and the use of EUS-guided elastography, contrast-enhanced EUS, trans-papillary biopsy, natural orifice transluminal endoscopic surgery, magnifying endoscopy, choledochoscopy, and confocal laser endomicroscopy when necessary for early diagnosis of GBC. However, there is a need for novel methods for early GBC diagnosis, such as the use of artificial intelligence and non-coding RNA biomarkers for improved screening protocols. Additionally, the use of in vitro and animal models may provide critical insights for advancing early detection and treatment strategies of this aggressive tumor.