Precision Imaging For Early Detection And Targeted Treatment Monitoring In Pancreatic Cancer

Study Overview

Precision Imaging for Early Detection and Targeted Treatment Monitoring in Pancreatic Cancer

Specifically, in this project, the objective will be developped a model to capture imaging-based tumor heterogeneity with multiscale radiomics approach by obtaining the mirror tumor image at in vivo MRI, ex vivo MRI at histology. This imaging model giving a perfect virtual histology tumor representation will be secondary implemented on routine in vivo clinical MRI for early cancer detection and treatment monitoring. Successful completion of this proposal will lead to a comprehensive non invasive characterisation of pancreatic cancer and will be a game changer in patient management.

With a five-year survival rate of only 3% for the majority of patients, pancreatic cancer is a global healthcare challenge. By the time of diagnosis over half of pancreatic cancers are metastasized. The dire disease situation reflects our inability to diagnose pancreatic cancer early and to effectively treat it. Our failure to diagnose the disease early results in part from the inaccessibility of the organ, difficulties in detecting small pancreatic lesions by conventional imaging approaches, and a poor understanding of the spectrum of heterogeneity in pancreatic cancer. Single time point, single site biopsies cannot assess entire tumor while multiple biopsies at several time points are not feasible in clinical routine. Limitations of invasive sampling may be addressed with non-invasive imaging that captures morphologic and functional information about the entire tumor in space and, if repeated, in time. Radiomics has the potential for "whole tumour virtual sampling" using a single or serial non-invasive examinations in place of biopsies. By approaching images as data able to be mined, instead of merely pictures in conventional radiology, quantitative imaging allows for further information to be extracted from medical images as well as for global assessments across large patient populations. Therefore, these new quantitative approaches hold the promise of detecting pancreatic cancer characteristics that the naked eye alone cannot perceive from conventional medical imaging, opening new doors for personalized medicine in pancreatic cancer. To date, no study has evaluated the value of radiomics at macroscopic (in vivo 1.5T/3TMRI) and microscopic (ex vivo 9.4TMRI) scale for early cancer detection and targeted treatment monitoring. Specifically, in this project, the objective will be developpe a model to capture imaging-based tumor heterogeneity with multiscale radiomics approach by obtaining the mirror tumor image at in vivo MRI, ex vivo MRI at histology. This imaging model giving a perfect virtual histology tumor representation will be secondary implemented on routine in vivo clinical MRI for early cancer detection and treatment monitoring. Successful completion of this proposal will lead to a comprehensive non invasive characterisation of pancreatic cancer and will be a game changer in patient management.

Pancreas Cancer

BIOLOGICAL: Biological/Vaccine: Blood sample and tissue sample

PROICM 2023-03 PAN

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 Registration Dates	Results Reporting Dates	Study Record Updates
First Submitted 2023-11-13	Results First Submitted N/A	Last Update Submitted that met QC Criteria 2025-02-11
First Submitted that Met QC Criteria 2023-11-17	Results First Submitted that Met QC Criteria N/A	Last Update Posted (ACTUAL) 2025-02-12
First Posted (ACTUAL) 2023-11-22	Results First Posted N/A	Last Verified 2025-02

Study Plan

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

Design Details

Primary Purpose:
Other

Allocation:
Na

Interventional Model:
Single Group

Masking:
None

Arms and Interventions

Participant Group/Arm	Intervention/Treatment
EXPERIMENTAL: Blood sample and tissue sample Blood sample and tissue sample	BIOLOGICAL: Biological/Vaccine: Blood sample and tissue sample During the surgery : Tissus sample : primary tumor and metastasis blood sample : 3 EDTA tubes ex vivo MRI data

Participant Group/Arm

Intervention/Treatment

EXPERIMENTAL: Blood sample and tissue sample

Blood sample and tissue sample

BIOLOGICAL: Biological/Vaccine: Blood sample and tissue sample

During the surgery : Tissus sample : primary tumor and metastasis blood sample : 3 EDTA tubes ex vivo MRI data

Primary Outcome Measures	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	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	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	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,	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,	Correlation between radiomic maps and tumour biology (CYTOF, proteomics and transcriptomics),	The day of the surgery
Correlate MRI results with hematological molecular biology results.	Correlation between radiomic algorithms for tumor detection and cDNA assay	The day of the surgery

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

Name: MOUSSION Aurore

Phone Number: 0467613102

Email: aurore.moussion@icm.unicancer.fr

Study Contact Backup

Name: Texier Emmanuelle

Phone Number: 0467613102

Email: emmanuelle.texier@icm.unicancer.fr

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.

Ages Eligible for Study:
ALL

Sexes Eligible for Study:
18 Years

Accepts Healthy Volunteers:

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.

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.

Collaborators and Investigators

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

Investigators

STUDY_DIRECTOR: NOUGARET Stephanie, INSTITUT REGIONAL DU CANCER DE MONTPELLIER Cancer de Montpellier

Publications

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

General Publications

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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

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