Browsing by Author "Halonen, Pyry"

Prostate cancer is the second most common cancer among men and the risk evaluation of the cancer prior the treatment can be critical. Risk evaluation of the prostate cancer is based on multiple factors such as clinical assessment. Biomarkers are studied as they would also be beneficial in the risk evaluation. In this thesis we assess the predictive abilities of biomarkers regarding the prostate cancer relapse. The statistical method we utilize is logistic regression model. It is used to model the probability of a dichotomous outcome variable. In this case the outcome variable indicates if the cancer of the observed patient has relapsed. The four biomarkers AR, ERG, PTEN and Ki67 form the explanatory variables. They are the most studied biomarkers in prostate cancer tissue. The biomarkers are usually detected by visual assessment of the expression status or abundance of staining. Artificial intelligence image analysis is not yet in common clinical use, but it is studied as a potential diagnostic assistance. The data contains for each biomarker a visually obtained variable and a variable obtained by artificial intelligence. In the analysis we compare the predictive power of these two differently obtained sets of variables. Due to the larger number of explanatory variables, we seek the best fitting model. When we are seeking the best fitting model, we use an algorithm glmulti for the selection of the explanatory variables. The predictive power of the models is measured by the receiver operating characteristic curve and the area under the curve. The data contains two classifications of the prostate cancer whereas the cancer was visible in the magnetic resonance imaging (MRI). The classification is not exclusive since a patient could have had both, a magnetic resonance imaging visible and an invisible cancer. The data was split into three datasets: MRI visible cancers, MRI invisible cancers and the two datasets combined. By splitting the data we could further analyze if the MRI visible cancers have differences in the relapse prediction compared to the MRI invisible cancers. In the analysis we find that none of the variables from MRI invisible cancers are significant in the prostate cancer relapse prediction. In addition, all the variables regarding the biomarker AR have no predictive power. The best biomarker for predicting prostate cancer relapse is Ki67 where high staining percentage indicates greater probabilities for the prostate cancer relapse. The variables of the biomarker Ki67 were significant in multiple models whereas biomarkers ERG and PTEN had significant variables only in a few models. Artificial intelligence variables show more accurate predictions compared to the visually obtained variables, but we could not conclude that the artificial intelligence variables are purely better. We learn instead that the visual and the artificial intelligence variables complement each other in predicting the cancer relapse.