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 to 1. Undergo biopsy of the gallbladder mass after a baseline CT scan 2. Based on the results of the biopsy, patients will be given chemotherapy either targeted (if Her2 positive) or non-targeted 3. Response to treatment will be assessed with a CT scan at 12 weeks of chemotherapy

Sex

All

Minimum age

18 Years

Maximum age

70 Years

Inclusion Criteria:

1. Patients with unresectable mass-forming GBC
2. Patients willing to give informed consent

Exclusion Criteria:

1. Patients with prior chemotherapy for GBC
2. Patients with deranged RFTs
3. Patients with contrast allergy

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