Sistema de Apoio `a Análise e ao Tratamento de Doentes com Carcinoma Hepatocelular

158 Pages·2014·5 MB·English

MIRIAM RAQUEL SEOANE PEREIRA SEGURO SANTOS
Sistema de Apoio `a An´alise e ao Tratamento de
Doentes com Carcinoma Hepatocelular
Disserta¸ca˜o apresentada `a Universidade de Coimbra para
cumprimento dos requisitos necess´arios `a obten¸ca˜o
do grau de Mestre em Engenharia Biom´edica
Orientadores:
Professor Doutor Alberto Cardoso
Professor Doutor Pedro H. Abreu
Coimbra, 2014

Este trabalho foi desenvolvido em colabora¸ca˜o com:
Centro de Inform´atica e Sistemas da Universidade de Coimbra
(CISUC)
Centro Hospitalar e Universit´ario de Coimbra
(CHUC)

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Abstract
Liver cancer is the sixth most frequently diagnosed cancer and the third cause of cancer-
related deaths worldwide. Hepatocellular Carcinoma (HCC) represents more than 90% of
primary liver cancers and it’s a major global health problem. Clinical guidelines aim to assist
clinicians in their decision-making process, under the assumptions of Evidence-Based Medicine
(EBM). However, clinical practice often deals with the mismatch between EBM and the desired
Personalized Medicine (PM), adjusted to a given patient. In order to make a reasoned decision,
clinicians frequently need to access the patient’s information, which is a difficult quest in the
great majority of hospital contexts. The patient’s clinical files are often dispersed in physical
files, subjected to loss and inconsistency. Furthermore, such scenario also makes patient’s
clinical data susceptible to missing data.
In this work, we present a Clinical Decision Support System (CDSS) for managing clinical
data of HCC patients, and an Artificial Intelligence (AI) module to be integrated with the
developed CDSS. We have conducted several clustering approaches to profile a HCC patients
database with heterogeneous and missing data. Our analysis led to the patients division into
two groups, G1 and G2, with statistically significant overall survivals. HCC stage C patients
were present in both groups, which suggested some heterogeneity between these patients. We
have also performed some classification studies in order to access group assignment for a new
patient presented to our CDSS.
In brief, we have developed a framework that allows cancer data management in the HCC
context. Our results show that it is possible to develop a CDSS for HCC patients which integ-
rates clinical data management with AI techniques, targeting the treatment of these patients
within the paradigms of PM. We have demonstrated that CDSSs allow the clinicians access to
the patients’ clinical data at all times, while supporting them in their daily decisions.
Keywords: Hepatocellular Carcinoma (HCC), Evidence-Based Medicine (EBM), Person-
alized Medicine (PM), Missing Data (MD), Imputation, Clinical Decision Support System
(CDSS), Profiling Prognostic Groups, Cancer Data, Clustering, Artificial Intelligence (AI),
clinical data
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