Challenge for Epidemium Seasion 2How to formulate challenges that can be comprehensible for doctors, patients, data scientistis & be novel and useful to advance research on cancer?
With the help of STIM and Mines ParisTech, Epidemium used CK method to identify what has been done so far and build a set of promising challenges to focus on.
CK is a collaborative method that allows to identify relevant knowledge and use it to generate new ideas and concepts. CK method is based on a theory of innovative design developed by Mines ParisTech. CK method was applied to :
1. Explore the universe of possibilities related to data analysis & cancer 2. Identify limits of the current approaches 3. Generate a set of innovative conceptsIn order to create common understanding for a concept of using data to advance cancer research, several workshops were organized with specialists of data analysis and cancer, and completed by literature review.
CK map provided a better understanding of what is cancer and its treatment, which data and data analysis technics' are available today and how they are used.
This exploration helped us identify limits of current approaches, shape sparsely populated areas and propose several exploration axes to be used as challenges for Epidemium 2.
Cancer based studies should be more global, less invasive and lead to prediction models and new instruments to study cancer
To detect genetic predispositions for each patient
like tobacco expenses, pollution
For each organ to understand which type of cancer can occur (in a logic of a personalized medicine)
To predict a particular type of cancer or cancer susceptibility recurrence survival
To build right instruments to study cancer
To model the progression and treatment of cancerous conditions
To discover new types of cancer
To find general or abnormal correlation
To understand correlations between gene expression profiles to disease states or different developmental stages of a cell
The complexity of risk factors and their impact on cancer development is a multi-dimensional challenge to be addressed
To understand the cancer within its complex environment, from a cell perspective to large-scale human environment
To find correlations between heterogeneous data sets
To determine weak signals across different data sets-accumulation of weak signals or a new type of interdependency
To estimate unknown factors that influence interdependencies
To reduce information inequalities
To study cancer epidemiology (distribution of cancer in population) at the different scales (country, hospitals...)
To create a common dialogue between different disciplines (sociology, patients)
To predict cancer incidence/ mortality/ survival rate using risk factors
More complex and personalized treatments, their prescription and consequences should be analyzed
To reveal how treatment can be combined with other treatments to improve overall efficiency
To assess treatment efficiency or failure expost including risk & environmental data
To rethink the cancer treatment by taking into account new factors (beyond traditional medical treatment)
To anticipate the efficiency of treatment and side effects according to the patient profile
To improve/develop new treatments or screening techniques for cancer
To analyze the secondary effects of treatments, complication
To study the impact of a particular type of treatment for different organs
To identify cases similar to a patient case to find a better treatment and better care services adapted to the patient profile
To assess more precisely patients’ survival rate and adapt the treatment according to cancer profile
Advances on cancer related research should become more accessible and comprehensible by any individual
To develop patient literacy related to cancer
To develop a continuous data sharing on cancer related data from various sources and communicate these information in a comprehensive way
To create a shared vision on cancer research (database, dataviz)
To develop dynamic visualization for data cancer
To display evolution of different types of cancer and variation of different socio-environmental factors like tobacco expenses, pollution
To train non-experts (ex., data scientists, product managers) to better understand cancer
Collaborative approaches across different fields can foster out-of-the-box ideas and lead to new discoveries
To get inspired by cancer cells mechanisms for other purposes
To help research treatment of others diseases
To identify ’markers’
Sociological and combined approaches to epidemiology of cancer for patients and their environment should be pursued
To improve patient-oncologists-general doctors collaboration
To access to the list of support/care services (built from collected data)
To automatically assign patients to different departments based no a type of cancer, socio, treatments
To get information about cancer on its own & to create an awarencess
To get information about cancer on its own (patient) (risks: bad use, bad qualification, education) & to create an awareness
To develop a comprehensive framework to all the clinical studies available & communicate to patients
More powerful decision-making tools for patient advocacy, improving patients care services are necessary
To improve patient care services, for patient advocacy, for shared decision making
To improve the effectiveness of research paths (balance benefit/cost)
To improve patient care services