Starting in 2013 with the development of a translational research data platform dedicated to cancer research, the SIRIC CARPEM has gained expertise in several methodological and technical aspects. These development have been transferred to the data repository developed in the Paris Cancer Institute: CARPEM program which integrates electronic health records (EHR) data from the participating AP-HP departments along with research data and biobank information to support studies on various kinds of cancers, including renal lung, blood, colorectal, and gynecological cancers (Rance 2016). State-of-the art approaches have been implemented to integrate heterogeneous data (e.g., Zapletal 2018), to link such data with open access environmental information, to find patient cohorts for studies, to identify subpopulations of patients, Solutions based on common data models have been proposed to harmonize data from disparate observational databases. The concept behind this approach is to transform data contained within the data sources into a common model, namely the OSIRIS model developed by the inter-SIRIC group and the French National Cancer Institute (INCa), and the OMOP Common Data Model developed by the Observational Health Data Sciences and Informatics (OHDSI) Oncology Subgroup (Belenkaya 2019) (Warner 2019). Common vocabularies and ontologies have also been proposed such as the Radiation Oncology Structures ontology designed to standardize description in radiation therapy (Bibault 2018). All the research activities are structured within the Cordeliers Research Center, which gathers groups doing basic research in cancer and researchers with backgrounds in mathematics, statistics, and computer science, from the “Information Science to support Personalized Medicine”.
Moreover, following the data warehousing step, researchers and physicians involved in Paris Cancer Institute CARPEM are now developing Artificial Intelligence for precision oncology. This initiative is supported by the PaRis Artificial Intelligence Research InstitutE (Prairie). Prairie is an institute for interdisciplinary Research and Education in AI, founded by academic and industrial members, with focus on health applications, specifically in oncology. Four members of the AI for oncology program of the Cancer Institute are also fellows of Prairie (S. Allassonniere, A. Burgun, L. Fournier, E. Letouzé). They will develop AI to support cancer detection, optimize the care trajectory of cancer patients, suggest optimal therapies, reduce medical errors, and improve subject enrollment into clinical trials.
A major achievement in the Paris Cancer institute CARPEM program has been the translational data warehouse. This data warehouse can be used to accelerate prospective clinical research. These data can be used to generate hypotheses for testing in traditional trials, identify potential biomarkers, perform feasibility studies, identify eligible patients, and assess the safety of drugs or devices after they are approved. With the objective of accelerating patient enrollment in clinical trials, the eligibility criteria can be aligned with EHR data models and mapped to common terminologies. However, several studies demonstrated that a significant percentage of those criteria could not be mapped to structured EHR data but were present only in text or images (e.g., (Girardeau 2017)). By leveraging natural language processing and image processing, IT technologies can dramatically increase the trial screening efficiency of oncologists
In addition, data collected in the Paris Cancer Institute will be reused to generate real world evidence (RWE) and, finally, fill knowledge gaps related to effectiveness, safety, and cost of treatment in “real life”. Regarding the medicines regulatory system, the European Medicines Agency (EMA) has recognized the importance of RWE, especially in oncology where they claimed that it was “our only hope to come to grips with combinatorial complexity”. The “AI in oncology” group has developed expertise on this field.
Regarding drug safety, we plan to mine to other data sources like social media to analyze adverse events associated with cancer treatments. We will rely on the expertise gained during projects like ADR-PRISM (Adverse drug Reactions from Patient Reports in Social Media). Indeed, ADR-PRISM was not focused on cancer but the results were quite encouraging, therefore similar methods can be applied to detect adverse events and analyze compliance to drug treatment in oncology.
In oncology, several authors (including (Bibault 2018) REF FOURNIER) showed that machine learning algorithms, especially convolutional neural networks and radiomics, could be used for detecting and evaluating cancer lesions, identify subgroups of patients (REF LETOUZE), facilitating treatment, and predicting treatment response. All machine learning algorithms require huge sets of high-quality data for training. The most successful applications of deep learning until now have been in image classification and text mining, with performance equivalent to expert. More precisely, in the Institute, research has focused on:
More recently, other topics have been investigated, including explainable hybrid models and guarantees for unbiased systems. The integrity of unbiased, clinically useful data depends upon the reliability of the data sources. Yet, data quality must be systematically assessed, and other risks related to sampling bias (data sets) and observation bias (measurement errors) must be systematically evaluated. All these considerations show that an interdisciplinary approach is needed to realize the potential of AI for precision oncology.
The “AI for oncology” group has developed several courses on AI and health. All are part of the Université de Paris curriculum and some benefit from the support of Prairie Institute:
Besides data collection and data integration, the Cancer Institute program will develop algorithms based on these data as well as frameworks that enable external validation of AI.
|Name Surname||Title/Position||Speciality||Research Unit||Resarch Team|
|Stéphanie Allassonniere||Full Prof||Applied mathematics||UMRS 1138 Centre de Recherche des Cordeliers – Prairie Institute||Information science to support personalized medicine|
|Guillaume Assié||Full Prof||Endocrinology||Paris Descartes AI program|
|Cécile Badoual||Full Prof||Pathology|
|Anita Burgun||Full Prof||Biomedical informatics||UMRS 1138 Centre de Recherche des Cordeliers – Prairie Institute||Information science to support personalized medicine|
|Michaela Fontenay||Full Prof||Haematology|
|Laure Fournier||Full Prof||Radiology||Prairie Institute|
|Philippe Giraud||Full Prof||Radiation oncology|
|Anne-Sophie Jannot||Associate Prof||Biostatistics||UMRS 1138 Centre de Recherche des Cordeliers||Information science to support personalized medicine|
|Sandrine Katsahian||Full Prof||Biostatistics||UMRS 1138 Centre de Recherche des Cordeliers||Information science to support personalized medicine|
|Eric Letouzé||Researcher||Bioinformatics||UMRS 1138 Centre de Recherche des Cordeliers – Prairie Institute||Functional Genomics of Solid Tumors (FunGeST)|
|Marie-France Mamzer||Full Prof||Medical ethics||UMRS 1138 Centre de Recherche des Cordeliers|
|Raphael Porcher||Full Prof||Biostatistics||CRESS – Prairie Institute|
|Bastien Rance||Associate prof||Bioinformatics||UMRS 1138 Centre de Recherche des Cordeliers||Information science to support personalized medicine|
|Brigitte Sabatier||Full time Pharmacist||UMRS 1138 Centre de Recherche des Cordeliers||Information science to support personalized medicine|
|Sarah Zohar||Researcher||Biostatistics||UMRS 1138 Centre de Recherche des Cordeliers||Information science to support personalized medicine|
|Juliette Djadi-Prat||Full time Physician||Clinical research||URC EGP|
The selected following publications highlight the strength of clinical and translational research developed in the AI for Oncology program:
Centre Universitaire des Saints-Pères Etage 4 – Pièce 446B 45 rue des Saints-Pères -75006 Paris
Carina Binet : Secrétaire Général du CARPEM
Tél. : 01 76 53 43 85 – email@example.com
Aurore Hattabi, PhD : Coordinatrice Scientifique du CARPEM
Tél : 01 76 53 43 85 – firstname.lastname@example.org