"Improving clinical decisions in cancer"
In Europe, over 40 000 women die of ovarian cancer every year. In addition to surgery, most patients are treated with platinum-based chemotherapy. Unfortunately, the effect of chemotherapy often decreases during the treatment cycles, and currently, there are few effective treatments for those patients who develop resistance to platinum-based drugs. The survival of these patients has not improved much in the past decades, and new solutions are in urgent need.
In the DECIDER project, a patient's response to treatments is predicted using methods that use, among others, histopathological and genomic data from the patient. Genomic changes and aberrations in gene functions are used to find effective, personalized treatments. This point will be made by developing an open-source program to integrate and visualize all relevant data from a patient. Doctors can more easily identify effective drugs for their patients using this information. All patients participating in the research are treated in Finland, and Finnish patient organizations have an essential advisory role in the project.
DECIDER is a multidisciplinary research project bringing together expertise from 16 research groups and companies in 14 organizations located in 7 European countries to develop diagnostic tools and improve the treatment options for high-grade serous ovarian cancer. The partners' expertise ranges from clinical medicine, genomics, molecular biology, computer science, and Artificial Intelligence to biomedical and privacy laws.
The main goals of DECIDER are to develop diagnostic tools and treatments for high-grade serous ovarian cancer with the help of AI methods. In addition, the aim is to identify those patients who do not respond well to the first-line treatments and find effective treatments for patients with drug-resistant cancer.
We also study the legal issues that impede or slow down the use of new treatments to facilitate the commercialization and availability of personalized therapies ethically and legally sustainable.
|1||Lovino, Marta; Randazzo, Vincenzo; Ciravegna, Gabriele; Barbiero, Pietro; Ficarra, Elisa; Cirrincione, Giansalvo "A survey on data integration for multi-omics sample clustering" NEUROCOMPUTING, vol. in stampa, pp. 1 -15 , 2021 | DOI: 10.1016/j.neucom.2021.11.094 Journal|