Clinical trial NCT05718115: Deep Learning Radiogenomics For Individualized Therapy in Unresectable Gallbladder Cancer
Official title: Deep Learning Radiogenomics For Individualized Therapy in Unresectable Gallbladder Cancer
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Gallbladder Cancer
Keywords: Gallbladder cancer, HER2, Radiogenomics, CT scan
Lead sponsor: Post Graduate Institute of Medical Education and Research, Chandigarh
Sponsor class: OTHER
Collaborators: name: Radiological Society of North America
First posted: 2023-02-08
Enrollment: 75
Sex: ALL
Minimum age (years): 18
Maximum age (years): 70
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients with unresectable mass-forming GBC

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.

Eligibility 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

Primary outcomes:
measure: Develop and validate a deep learning radiomics (DLR) model for identification of HER2 status in unresectable gallbladder cancer (GBC) on computed tomography (CT)
timeFrame: 8 months
measure: Predict response to anti-HER2 directed therapy using DLR
timeFrame: 12 weeks
Secondary outcomes:
Other outcomes:
Locations:
city: Chandigarh
country: India
facility: Post Graduate Institute of Medical Education and Research
state: Punjab
Study officials:
affiliation: PGIMER, CHANDIGARH
name: Pankaj Gupta
role: PRINCIPAL_INVESTIGATOR
References:
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