Deep Learning Radiogenomics For Individualized Therapy in Unresectable Gallbladder Cancer

The goal of this observational study is to learn about deep learning radiogenomics for individualized therapy in unresectable gallbladder cancer. The main questions it aims to answer are:(i) whether a deep learning radiomics (DLR) model can be used for identification of HER2status and prediction of response to anti-HER2 directed therapy in unresectable GBC.(ii) validation of the deep learning radiomics (DLR) model for identification of HER2 status and prediction of response to anti-HER2 directed therapy in unresectable GBC.Participants will be asked toUndergo biopsy of the gallbladder mass after a baseline CT scanBased on the results of the biopsy, patients will be given chemotherapy either targeted (if Her2 positive) or non-targetedResponse to treatment will be assessed with a CT scan at 12 weeks of chemotherapy

Inclusion Criteria:Patients with unresectable mass-forming GBCPatients willing to give informed consentExclusion Criteria:Patients with prior chemotherapy for GBCPatients with deranged RFTsPatients with contrast allergy