Deep Learning Radiogenomics For Individualized Therapy in Unresectable Gallbladder Cancer
Study Details
Study Description
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
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Undergo biopsy of the gallbladder mass after a baseline CT scan
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Based on the results of the biopsy, patients will be given chemotherapy either targeted (if Her2 positive) or non-targeted
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Response to treatment will be assessed with a CT scan at 12 weeks of chemotherapy
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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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
Outcome Measures
Primary Outcome Measures
- Develop and validate a deep learning radiomics (DLR) model for identification of HER2 status in unresectable gallbladder cancer (GBC) on computed tomography (CT) [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 [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
Eligibility Criteria
Criteria
Inclusion Criteria:
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Patients with unresectable mass-forming GBC
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Patients willing to give informed consent
Exclusion Criteria:
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Patients with prior chemotherapy for GBC
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Patients with deranged RFTs
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Patients with contrast allergy
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Post Graduate Institute of Medical Education and Research | Chandigarh | Punjab | India | 160012 |
Sponsors and Collaborators
- Postgraduate Institute of Medical Education and Research, Chandigarh
- Radiological Society of North America
Investigators
- Principal Investigator: Pankaj Gupta, PGIMER, CHANDIGARH
Study Documents (Full-Text)
None provided.More Information
Publications
- Albrecht 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.
- 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.
- Gupta 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.
- Gupta 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.
- Gupta 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.
- Gupta 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.
- Gupta 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.
- Javle 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.
- Javle 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.
- Kalra 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.
- May 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.
- Rana 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.
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