Gallbladder Cancer
Conditions
Keywords
Gallbladder cancer, HER2, Radiogenomics, CT scan
Brief summary
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
Detailed description
This study aimed at investigating the treatment option for patients with unresectable GB cancer. Presently the treatment of unresectable GB cancer mainly palliative with chemotherapy regime limited to generic form of chemotherapy offer to patients with other GI cancer. There is evolving data regarding the role of genetic mutation in cancers. Recent studies have also shown multiple somatic and germline mutation in GB cancer. Some of these mutations are amiable to targeted therapy. The era of precision medicine assured new hopes for patient with unresectable cancer. There is some preliminary data that shows benefit of precision medicine in GB cancer as well. The estimation of targeted therapy relies on obtaining biopsy therapy on cancer which can often be challenging, associated with complication and less acceptable by the patients. Studies in some other cancer shows that genetic mutation can be predicted based on imaging characteristics, however no such study has been done in GB cancer. The fundamental hypothesis is that prediction of HER2 status and response to anti-HER2 directed therapy using deep learning radiomic models in unresectable GBC will allow researchers to fully harness the potential of targeted therapy in clinical trials.
Interventions
DIAGNOSTIC_TESTCT scan
Biphasic CT scan including arterial phase and portal venous phase after intravenous injection of 80-100 mL of non-ionic iodinated contrast at rate of 4ml/s using pressure injector.
Sponsors
Radiological Society of North America
Post Graduate Institute of Medical Education and Research, Chandigarh
Study design
Observational model
CASE_ONLY
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 70 Years
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Develop and validate a deep learning radiomics (DLR) model for identification of HER2 status in unresectable gallbladder cancer (GBC) on computed tomography (CT) | 8 months | The DLR model identifying HER2 status in unresectable GBC will be developed using contrast enhanced CT scans of 150 patients (retrospective data). The accuracy of DLR will be validated a in a prospective contrast enhanced CT data of 75 patients. |
| Predict response to anti-HER2 directed therapy using DLR | 12 weeks | DLR will be used to predict response to targeted therapy in prospective cohort of HER2+ GBC patients on follow up CT at 12 weeks using RECIST 1.1 |
Countries
India
Contacts
Primary ContactPankaj Gupta
Outcome results
None listed