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Identifier 000457825
Title Developing a comprehensive radiomic analysis workflow for the detection of prostate cancer aggressiveness on T2weighted MR prostate data
Alternative Title Ανάπτυξη μοντέλων ραδιομικής ανάλυσης για την ανίχνευση της επιθετικότητας του καρκίνου του προστάτη σε Τ2w εικόνες προστάτη μαγνητικής τομογραφίας
Author Ζαφείρης, Στυλιανός
Thesis advisor Μαριάς, Κωνσταντίνος
Μανίκης, Γεώργιος
Ζερβάκης, Μιχάλης
Abstract Prostate cancer (PCa) is the second most common cancer diagnosed in male population worldwide, affecting 1.4 million men annually. Early assessment of the malignancy is crucial for treatment planning and extending patients’ life expectancy. Imaging modalities such as Magnetic Resonance Imaging (MRI) are used for the non-invasive classification of patients in order to prevent overtreating indolent malignancies and undertreating those who warrant immediate treatment. The field of radiomics offers a large quantity of imaging features that describe the cancer phenotype and can be used in training machine learning (ML) models for predicting cancer aggressiveness. Effective model training necessitates feature selection, decreasing the high dimensionality and ensuring the inclusion of pertinent and non- redundant features. The objective of this study is to investigate the most commonly used feature selection methods and classifiers in order to predict the tumor’s aggressiveness and analyze how various image preprocessing techniques affect the performance of the models. A publicly available multivendor dataset consisting of 225 samples with clinically significant PCa (csPCa) from 220 patients was used for the analysis. Samples were split in two cohorts based on ISUP score provided by clinicians. The first cohort (n = 135) contains samples with an assigned ISUP score equal to 2 (low aggressiveness csPCa) and the second cohort (n = 90) comprise samples with an assigned ISUP score of 3, 4 and 5 (high aggressiveness csPCa). Samples with ISUP score equal to 2 tend to have cancer cells that grow slowly, as opposed to the moderate and quick growth of cancer cells in samples of the second cohort. Thus, early detection of the tumor grade could prevent an unnecessary intervention or accelerate biopsy. A comprehensive search for the optimal pipeline was conducted for classifying the aggressiveness of csPCa. Intensity normalization methods and the N4 bias field correction method were used to investigate whether these preprocessing steps affect the performance of the models. For the original and each pre-processed dataset, a cross-combination strategy leveraging 6 classifiers and 13 feature selection methods was used for determining an optimal pipeline that reduces overfitting and best determines the tumor grade. Furthermore, hybrid feature selection methods were also investigated, using the optimal parameter set extracted from the pipeline. Methods investigated in this study demonstrated a balanced accuracy of 70% in determining the tumor’s aggressiveness, providing promising results in early detection of aggressiveness of csPCa.
Language English
Subject Dynamic pipeline optimization
Feature selection
Image preprocessing
Ανάλυση με χρήση ραδιομικών χαρακτηριστικών
Δυναμική βελτιστοποίηση μοντέλων μηχανικής μάθησης
Επεξεργασία εικόνας
Issue date 2023-07-28
Collection   School/Department--School of Medicine--Department of Medicine--Post-graduate theses
  Type of Work--Post-graduate theses
Permanent Link https://elocus.lib.uoc.gr//dlib/5/3/1/metadata-dlib-1693642352-754369-6727.tkl Bookmark and Share
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