To give concrete expression to its approach, the AI & Cancers Association examines and carries out various projects : some optimize a sequence of treatments, other target breaking points in patient journey to make them more efficient or develop innovative methods for evaluating the effectiveness of treatments so that patients have access to innovations earlier.
Data collected in the context of early access have certain limitations, in particular a short collection period or insufficient completion of data collection forms. The aim of this project is to standardize the methologies for matching the databases created in the context of early access with the French National Health Data System (SNDS), within the INCa Cancer Data Platform, in order to complete the data collected in the context of early access.
Reducing diagnostic delays from the moment a cancer is suspected is a key issue to take full advantage of the innovations available to patients. However, understanding diagnostic pathways, which is a prerequisite for working on their optimization, is constrained by the heterogeneity and complexity of the pathways. The application of the new analysis techniques such as “process mining” could provide new answers. The aim of this project is to describe and categorize the diagnostic pathways of patients who have received systemic treatment for primary lung cancer from the first event suggestive of cancer to the initiation of a first treatment. The project will also focus on explaining the variations in the observed pathways and developing a predictive model for optimizing the diagnostic pathway. To do this, data from the Cancer Data Platform (French National Health Data System, cancer registries, molecular genetic reports and multidisciplinary consultation meetings) wil be analyzed using innovative methodologies.
In France, prostate cancer is the most common cancer in men. The prognosis of patients depends on the stage of the disease but, to date, the data concerning the different stages and the proportion of patients progressing from one stage to another remain approximate. The aim of this project is to estimate the incidence and prevalence of patients treated for non-metastatic or metastatic prostate cancer in France, to describe and analyze the care pathway, its possible disruptions and the therapeutic sequences of patients. To do this, data from the Cancer Data Plateform (French National Health Data System, molecular genetic reports and multidisciplinary consultation meetings), as well as data from a clinical cohort carried by GETUG (cooperative group for urogenital cancers) will be analyzed.
Solid tumors are classified according to their biomolecular profile in order to identify groups of tumors that are assumed to be more homogeneous in their natural history of desease and their sensitivity to treatments. The aim of this project is to describe the characteristics and care pathway of lung cancer patients with a specific molecular alteration (BRAF, cMET et KRAS) by matching data from molecular genetic reports to the French National Health Data System (SNDS) within the INCa Cancer Data Platform.
Data collected in the framework of early access are subject to certain limitations, in particular a short collection period or insufficient completion of the data collection forms. The aim of this project is to develop a methodology for matching the databases created in the framework of early access with the data from the French National Health Data System (SNDS) within the Cancer Data Platform. This matching will make it possible to complete the data collected in the framework of early access.