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

Brief summary public domain (ClinicalTrials.gov)

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.

Detailed description public domain (ClinicalTrials.gov)

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.

Intervention public domain (ClinicalTrials.gov)

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

Arm public domain (ClinicalTrials.gov)

Blood sample and tissue sample (experimental): Blood sample and tissue sample

Primary outcome public domain (ClinicalTrials.gov)

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. Time frame: The day of the surgery.

Secondary outcome public domain (ClinicalTrials.gov)

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.

Secondary outcome public domain (ClinicalTrials.gov)

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.

Secondary outcome public domain (ClinicalTrials.gov)

the heterogeneity of tumor biology via non-invasive imaging of different portions of the tumor,. Time frame: The day of the surgery.

Secondary outcome public domain (ClinicalTrials.gov)

Correlate MRI results with hematological molecular biology results.. Time frame: The day of the surgery.

Eligibility public domain (ClinicalTrials.gov)

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.

Enrollment public domain (ClinicalTrials.gov)

Enrollment: 150 participants (estimated).