In the last decade, a wide range of new treatments have been proposed to tackle lung cancer, the leading cause of cancer-related death in the world and its most frequent type. Still, the response to these new options strongly varies between patients, and few guidelines are available on how to optimise the treatment choice.
P4-LUCAT proposes to tackle this issue by developing an ICT solution supporting oncologists in the selection of the most appropriate lung cancer treatment.
The project will develop a Big Data analytics dashboard, integrating patient data, public repositories and literature evidence.
Such a tool will provide the practitioner with information about
- the efficacy of a treatment, tailored to the geno- and phenotypical characteristics of the patient
- the expected adverse effects and toxicities
- relevant literature supporting these findings
This can only be achieved by combining different sources of information, including Electronic Health Records, results from liquid biopsies, scientific literature and open structured data. It also requires the integration of techniques spanning from Natural Language Processing to knowledge graphs.
P4-LUCAT will impact the healthcare system by supporting better treatment selection decisions, thus reducing toxicities and adverse effects. Better decisions will have a direct effect on the cost of the treatment, e.g. through a reduced number of visits and unnecessary tests, and through the increased quality of life and employability of the patients.
In summary, P4-LUCAT will yield a novel scenario, in which an evidence- and data-based information system will support oncologists in their decision-making process.
Taking into account the strong need to use Big Data in healthcare, and specifically in lung cancer treatment, as well as the experience of the five members of the consortium, P4-LUCAT’s objective is:
To develop an ICT solution supporting oncologists in the selection of the most appropriate lung cancer treatment. The solution will provide a dashboard based on Big Data analytics integrating both patient data, public repositories and literature evidence.
In particular, the practitioner will be able to load the patient’s data onto the system and obtain a list of possible treatments. For each of these treatments, the software will present estimates of effectiveness (survival curves) and adverse effects, by matching the patient’s profile with all available data; and the most important papers supporting (or opposing) the use of the treatment. The envisioned solution will thus help the practitioner to make a more informed decision, by presenting in a synthesis of all available evidence and data, filtered according to the characteristics of the patient.