Gallbladder CancerNCT05718115
Deep Learning Radiogenomics For Individualized Therapy in Unresectable Gallbladder Cancer
An observational study of Gallbladder Cancer, run by Post Graduate Institute of Medical Education and Research, Chandigarh at 1 site in India.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 to 70 years
- Conditions the study is for
- Gallbladder Cancer
- What is being tested?
- CT scan
- Type of treatment
- Injection or infusion
- How many people
- 75 participants
- Where
- 1 siteIndia
- Study dates
- 15 Feb 2023to 31 Dec 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 70 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with unresectable mass-forming GBC
Who may be able to join
- Patients with unresectable mass-forming GBC
- Patients willing to give informed consent
Who cannot join
- Patients with prior chemotherapy for GBC
- Patients with deranged RFTs
- Patients with contrast allergy
Trial locations and contacts
1 site in 1 country.
India1 site, 1 recruiting
- Recruiting
Post Graduate Institute of Medical Education and Research
Chandigarh, Punjab, 160012
Pankaj Gupta, 0172-2756508
About this study
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.
Read more
(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
- Undergo biopsy of the gallbladder mass after a baseline CT scan
- Based on the results of the biopsy, patients will be given chemotherapy either targeted (if Her2 positive) or non-targeted
- Response to treatment will be assessed with a CT scan at 12 weeks of chemotherapy
Read the 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.
- Conditions
- Gallbladder Cancer
- Being tested
- CT scan
- Keywords
- Gallbladder cancer, HER2, Radiogenomics, CT scan
- Medical subject headings
- Gallbladder Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CT scanDiagnostic test
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.
What is being measured
The primary outcome is the main question the study is designed to answer.
Develop and validate a deep learning radiomics (DLR) model for identification of HER2 status in unresectable gallbladder cancer (GBC) on computed tomography (CT)
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.
Measured: 8 months
Predict response to anti-HER2 directed therapy using DLR
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
Measured: 12 weeks
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Feb 2023
- Study start15 Feb 2023 (estimated)
- Primary completion31 Dec 2023 (estimated)
- Study completion31 Dec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (12)Papers the sponsor has linked to this study.
- Other trials for Gallbladder CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 75 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Post Graduate Institute of Medical Education and Research, Chandigarh
- Sponsor type
- University, hospital or other
- Collaborators
- Radiological Society of North America
- Responsible party
- Principal investigator, Pankaj Gupta, Post Graduate Institute of Medical Education and Research, Chandigarh
- Investigators
- Pankaj Gupta, Principal investigator, PGIMER, CHANDIGARH
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05718115
- Sponsor's protocol ID
- 31875
- Status last verified
- Feb 2023
- First submitted
- 29 Jan 2023
- Last updated
- 8 Feb 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Boddapati SB, Lal A, Gupta P, Kalra N, Yadav TD, Gupta V, Dass A, Srinivasan R, Singhal M. Contrast enhanced ultrasound versus multiphasic contrast enhanced computed tomography in evaluation of gallbladder lesions. Abdom Radiol (NY). 2022 Feb;47(2):566-575. doi: 10.1007/s00261-021-03364-6. Epub 2021 Dec 7.
PubMed 34874479Gupta P, Dutta U, Rana P, Singhal M, Gulati A, Kalra N, Soundararajan R, Kalage D, Chhabra M, Sharma V, Gupta V, Yadav TD, Kaman L, Irrinki S, Singh H, Sakaray Y, Das CK, Saikia U, Nada R, Srinivasan R, Sandhu MS, Sharma R, Shetty N, Eapen A, Kaur H, Kambadakone A, de Haas R, Kapoor VK, Barreto SG, Sharma AK, Patel A, Garg P, Pal SK, Goel M, Patkar S, Behari A, Agarwal AK, Sirohi B, Javle M, Garcea G, Nervi F, Adsay V, Roa JC, Han HS. Gallbladder reporting and data system (GB-RADS) for risk stratification of gallbladder wall thickening on ultrasonography: an international expert consensus. Abdom Radiol (NY). 2022 Feb;47(2):554-565. doi: 10.1007/s00261-021-03360-w. Epub 2021 Dec 1.
PubMed 34851429Rana P, Gupta P, Kalage D, Soundararajan R, Kumar-M P, Dutta U. Grayscale ultrasonography findings for characterization of gallbladder wall thickening in non-acute setting: a systematic review and meta-analysis. Expert Rev Gastroenterol Hepatol. 2022 Jan;16(1):59-71. doi: 10.1080/17474124.2021.2011210. Epub 2022 Jan 17.
PubMed 34826262Gupta P, Rana P, Ganeshan B, Kalage D, Irrinki S, Gupta V, Yadav TD, Kumar R, Das CK, Gupta P, Endozo R, Nada R, Srinivasan R, Kalra N, Dutta U, Sandhu M. Computed tomography texture-based radiomics analysis in gallbladder cancer: initial experience. Clin Exp Hepatol. 2021 Dec;7(4):406-414. doi: 10.5114/ceh.2021.111173. Epub 2021 Dec 2.
PubMed 35402717Gupta P, Marodia Y, Bansal A, Kalra N, Kumar-M P, Sharma V, Dutta U, Sandhu MS. Imaging-based algorithmic approach to gallbladder wall thickening. World J Gastroenterol. 2020 Oct 28;26(40):6163-6181. doi: 10.3748/wjg.v26.i40.6163.
PubMed 33177791Gupta P, Kumar M, Sharma V, Dutta U, Sandhu MS. Evaluation of gallbladder wall thickening: a multimodality imaging approach. Expert Rev Gastroenterol Hepatol. 2020 Jun;14(6):463-473. doi: 10.1080/17474124.2020.1760840. Epub 2020 Apr 30.
PubMed 32323586Gupta P, Meghashyam K, Marodia Y, Gupta V, Basher R, Das CK, Yadav TD, Irrinki S, Nada R, Dutta U. Locally advanced gallbladder cancer: a review of the criteria and role of imaging. Abdom Radiol (NY). 2021 Mar;46(3):998-1007. doi: 10.1007/s00261-020-02756-4. Epub 2020 Sep 18.
PubMed 32945922Kalra N, Gupta P, Singhal M, Gupta R, Gupta V, Srinivasan R, Mittal BR, Dhiman RK, Khandelwal N. Cross-sectional Imaging of Gallbladder Carcinoma: An Update. J Clin Exp Hepatol. 2019 May-Jun;9(3):334-344. doi: 10.1016/j.jceh.2018.04.005. Epub 2018 Apr 30.
PubMed 31360026Javle M, Borad MJ, Azad NS, Kurzrock R, Abou-Alfa GK, George B, Hainsworth J, Meric-Bernstam F, Swanton C, Sweeney CJ, Friedman CF, Bose R, Spigel DR, Wang Y, Levy J, Schulze K, Cuchelkar V, Patel A, Burris H. Pertuzumab and trastuzumab for HER2-positive, metastatic biliary tract cancer (MyPathway): a multicentre, open-label, phase 2a, multiple basket study. Lancet Oncol. 2021 Sep;22(9):1290-1300. doi: 10.1016/S1470-2045(21)00336-3. Epub 2021 Jul 30.
PubMed 34339623Javle M, Churi C, Kang HC, Shroff R, Janku F, Surapaneni R, Zuo M, Barrera C, Alshamsi H, Krishnan S, Mishra L, Wolff RA, Kaseb AO, Thomas MB, Siegel AB. HER2/neu-directed therapy for biliary tract cancer. J Hematol Oncol. 2015 May 29;8:58. doi: 10.1186/s13045-015-0155-z.
PubMed 26022204Albrecht T, Rausch M, Roessler S, Geissler V, Albrecht M, Halske C, Seifert C, Renner M, Singer S, Mehrabi A, Vogel MN, Pathil-Warth A, Busch E, Kohler B, Rupp C, Weiss KH, Springfeld C, Rocken C, Schirmacher P, Goeppert B. HER2 gene (ERBB2) amplification is a low-frequency driver with potential predictive value in gallbladder carcinoma. Virchows Arch. 2020 Jun;476(6):871-880. doi: 10.1007/s00428-019-02706-6. Epub 2019 Dec 14.
PubMed 31838585May M, Raufi AG, Sadeghi S, Chen K, Iuga A, Sun Y, Ahmed F, Bates S, Manji GA. Prolonged Response to HER2-Directed Therapy in Three Patients with HER2-Amplified Metastatic Carcinoma of the Biliary System: Case Study and Review of the Literature. Oncologist. 2021 Aug;26(8):640-646. doi: 10.1002/onco.13800. Epub 2021 May 11.
PubMed 33896096
Study at a glance
From the registry record, last updated by the sponsor on 8 Feb 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep Learning Radiogenomics For Individualized Therapy in Unresectable Gallbladder Cancer
- ClinicalTrials.gov ID
- NCT05718115
- Acronym / sponsor ID
- 31875
- Conditions
- Gallbladder Cancer
- Keywords
- Gallbladder cancer, HER2, Radiogenomics, CT scan
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 75 participants (estimated)
- Lead sponsor
- Post Graduate Institute of Medical Education and Research, ChandigarhUniversity, hospital or other
- Collaborators
- Radiological Society of North America
- Study start
- 15 Feb 2023 (estimated)
- Primary completion
- 31 Dec 2023 (estimated)
- Study completion
- 31 Dec 2023 (estimated)
- First posted
- 8 Feb 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
- Undergo biopsy of the gallbladder mass after a baseline CT scan
- Based on the results of the biopsy, patients will be given chemotherapy either targeted (if Her2 positive) or non-targeted
- 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.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 70 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with unresectable mass-forming GBC
Key inclusion criteria (2 listed)
- Patients with unresectable mass-forming GBC
- Patients willing to give informed consent
Key exclusion criteria (3 listed)
- Patients with prior chemotherapy for GBC
- Patients with deranged RFTs
- Patients with contrast allergy
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| CT scanBiphasic 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. | Diagnostic test | – |
Primary outcome
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.
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
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Feb 2023.
- Background: PubMed 34874479Boddapati SB, Lal A, Gupta P, Kalra N, Yadav TD, Gupta V, Dass A, Srinivasan R, Singhal M. Contrast enhanced ultrasound versus multiphasic contrast enhanced computed tomography in evaluation of gallbladder lesions. Abdom Radiol (NY). 2022 Feb;47(2):566-575. doi: 10.1007/s00261-021-03364-6. Epub 2021 Dec 7.
- Background: PubMed 34851429Gupta P, Dutta U, Rana P, Singhal M, Gulati A, Kalra N, Soundararajan R, Kalage D, Chhabra M, Sharma V, Gupta V, Yadav TD, Kaman L, Irrinki S, Singh H, Sakaray Y, Das CK, Saikia U, Nada R, Srinivasan R, Sandhu MS, Sharma R, Shetty N, Eapen A, Kaur H, Kambadakone A, de Haas R, Kapoor VK, Barreto SG, Sharma AK, Patel A, Garg P, Pal SK, Goel M, Patkar S, Behari A, Agarwal AK, Sirohi B, Javle M, Garcea G, Nervi F, Adsay V, Roa JC, Han HS. Gallbladder reporting and data system (GB-RADS) for risk stratification of gallbladder wall thickening on ultrasonography: an international expert consensus. Abdom Radiol (NY). 2022 Feb;47(2):554-565. doi: 10.1007/s00261-021-03360-w. Epub 2021 Dec 1.
- Background: PubMed 34826262Rana P, Gupta P, Kalage D, Soundararajan R, Kumar-M P, Dutta U. Grayscale ultrasonography findings for characterization of gallbladder wall thickening in non-acute setting: a systematic review and meta-analysis. Expert Rev Gastroenterol Hepatol. 2022 Jan;16(1):59-71. doi: 10.1080/17474124.2021.2011210. Epub 2022 Jan 17.
- Background: PubMed 35402717Gupta P, Rana P, Ganeshan B, Kalage D, Irrinki S, Gupta V, Yadav TD, Kumar R, Das CK, Gupta P, Endozo R, Nada R, Srinivasan R, Kalra N, Dutta U, Sandhu M. Computed tomography texture-based radiomics analysis in gallbladder cancer: initial experience. Clin Exp Hepatol. 2021 Dec;7(4):406-414. doi: 10.5114/ceh.2021.111173. Epub 2021 Dec 2.
- Background: PubMed 33177791Gupta P, Marodia Y, Bansal A, Kalra N, Kumar-M P, Sharma V, Dutta U, Sandhu MS. Imaging-based algorithmic approach to gallbladder wall thickening. World J Gastroenterol. 2020 Oct 28;26(40):6163-6181. doi: 10.3748/wjg.v26.i40.6163.
- Background: PubMed 32323586Gupta P, Kumar M, Sharma V, Dutta U, Sandhu MS. Evaluation of gallbladder wall thickening: a multimodality imaging approach. Expert Rev Gastroenterol Hepatol. 2020 Jun;14(6):463-473. doi: 10.1080/17474124.2020.1760840. Epub 2020 Apr 30.
- Background: PubMed 32945922Gupta P, Meghashyam K, Marodia Y, Gupta V, Basher R, Das CK, Yadav TD, Irrinki S, Nada R, Dutta U. Locally advanced gallbladder cancer: a review of the criteria and role of imaging. Abdom Radiol (NY). 2021 Mar;46(3):998-1007. doi: 10.1007/s00261-020-02756-4. Epub 2020 Sep 18.
- Background: PubMed 31360026Kalra N, Gupta P, Singhal M, Gupta R, Gupta V, Srinivasan R, Mittal BR, Dhiman RK, Khandelwal N. Cross-sectional Imaging of Gallbladder Carcinoma: An Update. J Clin Exp Hepatol. 2019 May-Jun;9(3):334-344. doi: 10.1016/j.jceh.2018.04.005. Epub 2018 Apr 30.
- Background: PubMed 34339623Javle M, Borad MJ, Azad NS, Kurzrock R, Abou-Alfa GK, George B, Hainsworth J, Meric-Bernstam F, Swanton C, Sweeney CJ, Friedman CF, Bose R, Spigel DR, Wang Y, Levy J, Schulze K, Cuchelkar V, Patel A, Burris H. Pertuzumab and trastuzumab for HER2-positive, metastatic biliary tract cancer (MyPathway): a multicentre, open-label, phase 2a, multiple basket study. Lancet Oncol. 2021 Sep;22(9):1290-1300. doi: 10.1016/S1470-2045(21)00336-3. Epub 2021 Jul 30.
- Background: PubMed 26022204Javle M, Churi C, Kang HC, Shroff R, Janku F, Surapaneni R, Zuo M, Barrera C, Alshamsi H, Krishnan S, Mishra L, Wolff RA, Kaseb AO, Thomas MB, Siegel AB. HER2/neu-directed therapy for biliary tract cancer. J Hematol Oncol. 2015 May 29;8:58. doi: 10.1186/s13045-015-0155-z.
- Background: PubMed 31838585Albrecht T, Rausch M, Roessler S, Geissler V, Albrecht M, Halske C, Seifert C, Renner M, Singer S, Mehrabi A, Vogel MN, Pathil-Warth A, Busch E, Kohler B, Rupp C, Weiss KH, Springfeld C, Rocken C, Schirmacher P, Goeppert B. HER2 gene (ERBB2) amplification is a low-frequency driver with potential predictive value in gallbladder carcinoma. Virchows Arch. 2020 Jun;476(6):871-880. doi: 10.1007/s00428-019-02706-6. Epub 2019 Dec 14.
- Background: PubMed 33896096May M, Raufi AG, Sadeghi S, Chen K, Iuga A, Sun Y, Ahmed F, Bates S, Manji GA. Prolonged Response to HER2-Directed Therapy in Three Patients with HER2-Amplified Metastatic Carcinoma of the Biliary System: Case Study and Review of the Literature. Oncologist. 2021 Aug;26(8):640-646. doi: 10.1002/onco.13800. Epub 2021 May 11.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Pankaj Gupta principal investigator
PGIMER, CHANDIGARH
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gallbladder Cancer, run by Post Graduate Institute of Medical Education and Research, Chandigarh at 1 site in India.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 8 Feb 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in India. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Pankaj Gupta (0172-2756508, pankajgupta959@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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 study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05718115, which also lists 12 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Feb 2023.