Precision Imaging for Early Detection and Targeted Treatment Monitoring in Pancreatic Cancer (PANC-O-MICS)
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: MOUSSION Aurore
- Phone Number: +33 0467613102
- Email: aurore.moussion@icm.unicancer.fr
Study Contact Backup
- Name: Texier Emmanuelle
- Phone Number: +33 0467613102
- Email: emmanuelle.texier@icm.unicancer.fr
Study Locations
-
-
-
Montpellier, France, 34298
- Recruiting
- NOUGARET Stephanie
-
Contact:
- NOUGARET Stephanie, MD
- Email: stephanie.nougaret@icm.unicancer.fr
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- Patient aged >18 2.
- Pathologically proven pancreatic cancer which can beneficiate of upfront surgery or delayed surgery followed by neoadjuvant chemotherapy.
- Negative pregnancy test for women of childbearing potential
- Patients affiliated to a social protection system
- Written informed consent signed before project onset.
Exclusion Criteria:
- presence of metastases,
- Patient who will not have surgery
- Pregnant or breastfeeding women
- Mental or psychological state, physical or legal incapacity preventing participation in the project.
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Other
- Allocation: N/A
- Interventional Model: Single Group Assignment
- Masking: None (Open Label)
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Experimental: Blood sample and tissue sample
|
During the surgery : Tissus sample : primary tumor and metastasis blood sample : 3 EDTA tubes ex vivo MRI data |
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
the integration of in vivo and ex vivo MRI with histology and molecular caracteristic in order to increase the pancreatic cancer detection and therapeutic response monitoring
Time Frame: The day of the surgery
|
The diagnostic performance of the radiomic and multiomic algorithm in pancreatic cancer detection and therapeutic response monitoring.
|
The day of the surgery
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
the imaging phenotype of tumor heterogeneity with a multi-scale radiomic approach by obtaining the image mirror tumor at the in vivo scale
Time Frame: The day of the surgery
|
Correlation between radiomic maps and pathogenic maps of heterogeneity,
|
The day of the surgery
|
|
tumor heterogeneity in artificial intelligence-based imaging reflects and can predict underlying histology (proportion of tumor stroma and density of tumor-infiltrating lymphocytes) (tumor detection and response) and genomics,
Time Frame: The day of the surgery
|
Correlation between radiomic algorithms and i/underlying histology (proportion of tumor stroma and density of tumor-infiltrating lymphocytes) (tumor detection and response) ii/ genomics
|
The day of the surgery
|
|
the heterogeneity of tumor biology via non-invasive imaging of different portions of the tumor,
Time Frame: The day of the surgery
|
Correlation between radiomic maps and tumour biology (CYTOF, proteomics and transcriptomics),
|
The day of the surgery
|
|
Correlate MRI results with hematological molecular biology results.
Time Frame: The day of the surgery
|
Correlation between radiomic algorithms for tumor detection and cDNA assay
|
The day of the surgery
|
Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Study Director: NOUGARET Stephanie, INSTITUT REGIONAL DU CANCER DE MONTPELLIER Cancer de Montpellier
Publications and helpful links
General Publications
- Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016 Feb;278(2):563-77. doi: 10.1148/radiol.2015151169. Epub 2015 Nov 18.
- Dextraze K, Saha A, Kim D, Narang S, Lehrer M, Rao A, Narang S, Rao D, Ahmed S, Madhugiri V, Fuller CD, Kim MM, Krishnan S, Rao G, Rao A. Spatial habitats from multiparametric MR imaging are associated with signaling pathway activities and survival in glioblastoma. Oncotarget. 2017 Dec 5;8(68):112992-113001. doi: 10.18632/oncotarget.22947. eCollection 2017 Dec 22.
- Tardieu M, Lakhman Y, Khellaf L, Cardoso M, Sgarbura O, Colombo PE, Crispin-Ortuzar M, Sala E, Goze-Bac C, Nougaret S. Assessing Histology Structures by Ex Vivo MR Microscopy and Exploring the Link Between MRM-Derived Radiomic Features and Histopathology in Ovarian Cancer. Front Oncol. 2022 Jan 19;11:771848. doi: 10.3389/fonc.2021.771848. eCollection 2021.
- Himoto Y, Veeraraghavan H, Zheng J, Zamarin D, Snyder A, Capanu M, Nougaret S, Vargas HA, Shitano F, Callahan M, Wang W, Sala E, Lakhman Y. Computed Tomography-Derived Radiomic Metrics Can Identify Responders to Immunotherapy in Ovarian Cancer. JCO Precis Oncol. 2019 Aug 15;3:PO.19.00038. doi: 10.1200/PO.19.00038. eCollection 2019.
- Weigelt B, Vargas HA, Selenica P, Geyer FC, Mazaheri Y, Blecua P, Conlon N, Hoang LN, Jungbluth AA, Snyder A, Ng CKY, Papanastasiou AD, Sosa RE, Soslow RA, Chi DS, Gardner GJ, Shen R, Reis-Filho JS, Sala E. Radiogenomics Analysis of Intratumor Heterogeneity in a Patient With High-Grade Serous Ovarian Cancer. JCO Precis Oncol. 2019 Jun 6;3:PO.18.00410. doi: 10.1200/PO.18.00410. eCollection 2019. No abstract available.
- Nougaret S, Lakhman Y, Gourgou S, Kubik-Huch R, Derchi L, Sala E, Forstner R; European Society of Radiology (ESR) and the European Society of Urogenital Radiology (ESUR). MRI in female pelvis: an ESUR/ESR survey. Insights Imaging. 2022 Mar 28;13(1):60. doi: 10.1186/s13244-021-01152-w.
- Soyer P, Revel MP, Dohan A, Vernhet-Kovacsik H, Nougaret S, Hoeffel C. Gender diversity in authorship in Diagnostic & Interventional Imaging: Where are we now? Diagn Interv Imaging. 2022 May;103(5):237-239. doi: 10.1016/j.diii.2022.02.001. Epub 2022 Feb 17. No abstract available.
- Shinagare AB, Sadowski EA, Park H, Brook OR, Forstner R, Wallace SK, Horowitz JM, Horowitz N, Javitt M, Jha P, Kido A, Lakhman Y, Lee SI, Manganaro L, Maturen KE, Nougaret S, Poder L, Rauch GM, Reinhold C, Sala E, Thomassin-Naggara I, Vargas HA, Venkatesan A, Nikolic O, Rockall AG. Ovarian cancer reporting lexicon for computed tomography (CT) and magnetic resonance (MR) imaging developed by the SAR Uterine and Ovarian Cancer Disease-Focused Panel and the ESUR Female Pelvic Imaging Working Group. Eur Radiol. 2022 May;32(5):3220-3235. doi: 10.1007/s00330-021-08390-y. Epub 2021 Nov 30.
- Tibermacine H, Rouanet P, Sbarra M, Forghani R, Reinhold C, Nougaret S; GRECCAR Study Group. Radiomics modelling in rectal cancer to predict disease-free survival: evaluation of different approaches. Br J Surg. 2021 Oct 23;108(10):1243-1250. doi: 10.1093/bjs/znab191.
- Nougaret S, Vargas HA, Sala E. BJR female genitourinary oncology special feature: introductory editorial. Br J Radiol. 2021 Sep 1;94(1125):20219003. doi: 10.1259/bjr.20219003. No abstract available.
- Rouanet P, Rullier E, Lelong B, Maingon P, Tuech JJ, Pezet D, Castan F, Nougaret S; GRECCAR Study Group*. Tailored Strategy for Locally Advanced Rectal Carcinoma (GRECCAR 4): Long-term Results From a Multicenter, Randomized, Open-Label, Phase II Trial. Dis Colon Rectum. 2022 Aug 1;65(8):986-995. doi: 10.1097/DCR.0000000000002153. Epub 2022 Jul 5.
- Nougaret S, Tibermacine H, Tardieu M, Sala E. Radiomics: an Introductory Guide to What It May Foretell. Curr Oncol Rep. 2019 Jun 25;21(8):70. doi: 10.1007/s11912-019-0815-1.
- Chu LC, Park S, Kawamoto S, Fouladi DF, Shayesteh S, Zinreich ES, Graves JS, Horton KM, Hruban RH, Yuille AL, Kinzler KW, Vogelstein B, Fishman EK. Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue. AJR Am J Roentgenol. 2019 Aug;213(2):349-357. doi: 10.2214/AJR.18.20901. Epub 2019 Apr 23.
- Zhang Z, Li S, Wang Z, Lu Y. A Novel and Efficient Tumor Detection Framework for Pancreatic Cancer via CT Images. Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:1160-1164. doi: 10.1109/EMBC44109.2020.9176172.
- Chu LC, Park S, Kawamoto S, Wang Y, Zhou Y, Shen W, Zhu Z, Xia Y, Xie L, Liu F, Yu Q, Fouladi DF, Shayesteh S, Zinreich E, Graves JS, Horton KM, Yuille AL, Hruban RH, Kinzler KW, Vogelstein B, Fishman EK. Application of Deep Learning to Pancreatic Cancer Detection: Lessons Learned From Our Initial Experience. J Am Coll Radiol. 2019 Sep;16(9 Pt B):1338-1342. doi: 10.1016/j.jacr.2019.05.034. No abstract available.
- Meier A, Veeraraghavan H, Nougaret S, Lakhman Y, Sosa R, Soslow RA, Sutton EJ, Hricak H, Sala E, Vargas HA. Association between CT-texture-derived tumor heterogeneity, outcomes, and BRCA mutation status in patients with high-grade serous ovarian cancer. Abdom Radiol (NY). 2019 Jun;44(6):2040-2047. doi: 10.1007/s00261-018-1840-5.
- Vargas HA, Veeraraghavan H, Micco M, Nougaret S, Lakhman Y, Meier AA, Sosa R, Soslow RA, Levine DA, Weigelt B, Aghajanian C, Hricak H, Deasy J, Snyder A, Sala E. A novel representation of inter-site tumour heterogeneity from pre-treatment computed tomography textures classifies ovarian cancers by clinical outcome. Eur Radiol. 2017 Sep;27(9):3991-4001. doi: 10.1007/s00330-017-4779-y. Epub 2017 Mar 13.
- Sadowski EA, Thomassin-Naggara I, Rockall A, Maturen KE, Forstner R, Jha P, Nougaret S, Siegelman ES, Reinhold C. O-RADS MRI Risk Stratification System: Guide for Assessing Adnexal Lesions from the ACR O-RADS Committee. Radiology. 2022 Apr;303(1):35-47. doi: 10.1148/radiol.204371. Epub 2022 Jan 18. Erratum In: Radiology. 2023 Jul;308(1):e239017. doi: 10.1148/radiol.239017.
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- PROICM 2023-03 PAN
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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