Multimodal integration of liquid biopsy and radiology for the noninvasive diagnosis of gallbladder cancer and benign disorders.

Gallbladder cancer (GBC) frequently mimics gallbladder benign lesions (GBBLs) in radiological images, leading to preoperative misdiagnoses. To address this challenge, we initiated a prospective, multicenter clinical trial (ChicCTR2100049249) and proposed a multimodal, non-invasive diagnostic model to distinguish GBC from GBBLs. A total of 301 patients diagnosed with gallbladder-occupying lesions (GBOLs) from 11 medical centers across 7 provinces in China were enrolled and divided into a discovery cohort and an independent external validation cohort. An artificial intelligence (AI)-based integrated model, GBCseeker, is created using cell-free DNA (cfDNA) genetic signatures, radiomic features, and clinical information. It achieves high accuracy in distinguishing GBC from GBBL patients (93.33% in the discovery cohort and 87.76% in the external validation cohort), reduces surgeons' diagnostic errors by 56.24%, and reclassifies GBOL patients into three categories to guide surgical options. Overall, our study establishes a tool for the preoperative diagnosis of GBC, facilitating surgical decision-making.