Deep Learning Radiogenomics For Individualized Therapy in Unresectable Gallbladder Cancer

February 6, 2023 updated by: Pankaj Gupta, Postgraduate Institute of Medical Education and Research, Chandigarh

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

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

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.

Study Type

Observational

Enrollment (Anticipated)

75

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • India
    • Punjab
      • Chandigarh, Punjab, India, 160012
        • Recruiting
        • Post Graduate Institute of Medical Education and Research
        • Contact:
          • Pankaj Gupta
          • Phone Number: 0172-2756508

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 70 years (ADULT, OLDER_ADULT)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Probability Sample

Study Population

Patients with unresectable mass-forming GBC

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Develop and validate a deep learning radiomics (DLR) model for identification of HER2 status in unresectable gallbladder cancer (GBC) on computed tomography (CT)
Time Frame: 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.
8 months
Predict response to anti-HER2 directed therapy using DLR
Time Frame: 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
12 weeks

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Postgraduate Institute of Medical Education and Research, Chandigarh

Collaborators

Radiological Society of North America

Investigators

  • Principal Investigator: Pankaj Gupta, PGIMER, Chandigarh

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (ANTICIPATED)

February 15, 2023

Primary Completion (ANTICIPATED)

December 31, 2023

Study Completion (ANTICIPATED)

December 31, 2023

Study Registration Dates

First Submitted

January 29, 2023

First Submitted That Met QC Criteria

February 6, 2023

First Posted (ACTUAL)

February 8, 2023

Study Record Updates

Last Update Posted (ACTUAL)

February 8, 2023

Last Update Submitted That Met QC Criteria

February 6, 2023

Last Verified

February 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 31875

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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