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Trial Title:
Deep Learning Radiogenomics For Individualized Therapy in Unresectable Gallbladder Cancer
NCT ID:
NCT05718115
Condition:
Gallbladder Cancer
Conditions: Official terms:
Gallbladder Neoplasms
Conditions: Keywords:
Gallbladder cancer, HER2, Radiogenomics, CT scan
Study type:
Observational
Overall status:
Recruiting
Study design:
Time perspective:
Prospective
Intervention:
Intervention type:
Diagnostic Test
Intervention name:
CT scan
Description:
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.
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.
Criteria for eligibility:
Study pop:
Patients with unresectable mass-forming GBC
Sampling method:
Probability Sample
Criteria:
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
Gender:
All
Minimum age:
18 Years
Maximum age:
70 Years
Healthy volunteers:
No
Locations:
Facility:
Name:
Post Graduate Institute of Medical Education and Research
Address:
City:
Chandigarh
Zip:
160012
Country:
India
Status:
Recruiting
Contact:
Last name:
Pankaj Gupta
Phone:
0172-2756508
Start date:
February 15, 2023
Completion date:
December 31, 2023
Lead sponsor:
Agency:
Post Graduate Institute of Medical Education and Research, Chandigarh
Agency class:
Other
Collaborator:
Agency:
Radiological Society of North America
Agency class:
Other
Source:
Post Graduate Institute of Medical Education and Research, Chandigarh
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05718115
