Jesus García-Donas, Head of Gynecological and Genitourinary Tumors at HM CIOCC Madrid
A Artificial Intelligence (AI) Research. used in ovarian cancer This allows to advance the knowledge of the predictive and prognostic molecular biomarkers of this type of tumor, which will contribute to a better understanding of the evolution of the disease and therefore to making personalized diagnoses more accurate and efficient. The Clara Campal Complex Oncology Center (HM CIOOC), in collaboration with the Juan Carlos I University, designed and developed this important collaboration.
So far, some genetic changes have been identified as predictive and prognostic biomarkers in ovarian cancer, such as inactivating mutations in the BRCA1 and BRCA2 genes, but they are not sufficient to understand the global evolution of the disease. Thus,
HM CIOCC and Universidad Juan Carlos I collaborated to conduct this multicenter observational study focusing on identification of biomarkers with potential impact in clinical practice.
Jesús García-Donas, Head of Gynecological and Genitourinary Tumors at HM CIOCC Madrid and co-author of the study, explains that “at the moment they know that crab is a complex disease, in the evolution of which not only genetic changes are important, but also influence of microenvironmental conditions, the regulation of gene expression and, of course, the condition of the person suffering from it. This forces them to change the previous, somewhat simplistic approach, in which the tumor was conceived as a mere collection of mutations, and it is essential to integrate multiple data to understand the neoplasm and predict its evolution.”
More than 300 patients
To conduct this study, the clinical and genomic data of 300 patients with advanced ovarian cancer were entered in order to establish a relationship between them and the variables that determine the progression of the disease, which represents “a new line of work in which they create artificial intelligence algorithms capable of integrating genomic data, with the clinical and pathological characteristics of the disease, in order to approach it comprehensively and comprehensively. We think that this approach can give us a vision closer to reality than classical approaches focused on specific point changes,” García-Donas points out.
In this sense, AI algorithms identify patterns common to those cases that have responded well to treatment versus those that have been resistant. If confirmed in independent cohorts, we will face a new and promising line of work whereby our accuracy in predicting the development of a given case can increase exponentially.
As regardsl predictive and prognostic role of certain variables, the results confirm that subjecting patients with a high tumor burden at the time of diagnosis to neoadjuvant treatment followed by maximal effort surgery favors the reduction of said burden. The association between these variables results in longer survival consistent with previous data in the literature.
Several previous studies have shown the relationship between some genetic changes (BRCA1/2 and RAD51C) and the evolution of the disease. The application of artificial intelligence allowed define other gene correlations to be confirmed by increasing the number of data, that is, of the cases to be ordered. García-Donas believes that “this work is pioneering in ovarian cancer and will require a coordinated effort between medical oncologists, basic researchers and computer engineers to achieve a real impact on patient management.”
Agreement with Microsoft
HM Hospitales’ participation in this study exemplifies the clear commitment the Group has made since the beginning of the year to the application of artificial intelligence in cancer research. As a result of the agreement signed with Microsoft, work is being done on the analysis of diagnostic images and on the automation of the collection of data from clinical histories with the dual purpose of guiding clinical activity and supporting research, accelerating the development of new procedures and drugs.
For Garcia-Donas, “working in the field of artificial intelligence applied to health is always a difficult challenge.” “Now that we have established the best training methodology, we want to advance in predicting response to specific therapies so that we can personalize the treatment of patients using artificial intelligence”. Thus, he believes that “when we can understand the disease at an advanced stage, we will be able to apply the same methodology to an early disease where the chances of cure are much greater. It is likely that in this context, the impact of this technology could decisively change the course of the disease.”
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