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Deep Learning Radiogenomics For Individualized Therapy in Unresectable Gallbladder Cancer

NCT05718115 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 75

Last updated 2023-02-08

No results posted yet for this study

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

Conditions

  • Gallbladder Cancer

Interventions

DIAGNOSTIC_TEST

CT 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 & Collaborators

  • Radiological Society of North America

    collaborator OTHER

  • Post Graduate Institute of Medical Education and Research, Chandigarh

    lead OTHER

Principal Investigators

  • Pankaj Gupta · PGIMER, CHANDIGARH

Eligibility

Min Age
18 Years
Max Age
70 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-02-15
Primary Completion
2023-12-31
Completion
2023-12-31

Countries

  • India

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05718115 on ClinicalTrials.gov