MRI prostate cancer radiomics

Title

MRI prostate cancer radiomics: Assessment of effectiveness and perspectives

 Author

Chatzoudis, Pavlos (TU Delft Mechanical, Maritime and Materials Engineering)

Contributor

Niessen, Wiro (mentor)
Veenland, Jifke (mentor)

Degree granting institution

Delft University of Technology

 Programme

Biomedical Engineering

 Date

2018-02-02

 Abstract

Prostate cancer is a disease with very high prevalence and mortality in the western world. An early accurate diagnosis can increase treatment efficiency. Current diagnosing techniques consist in systematic biopsy sampling. Radiomics can infer tumor's phenotypic differentiations from medical images, providing an accurate guide for biopsy sampling and making personalized treatment plans possible. Radiomics are various features that are extracted from medical images. Subsequently they are applied to train machine learning models that distinguish between healthy or cancerous tissue.
In this thesis a software routine that extracts the most commonly reported MRI prostate cancer radiomic features was built. Then, several classification methods were tried. Results were validated on T2 MRI patient images with confirmed histopathology from two different clinics.

 Subject

Radiomics
MRI
prostate cancer
personalized medicine
Random Forest
SVM

 To reference this document use:

http://resolver.tudelft.nl/uuid:b8459bdb-1761-4f17-8807-e3b1cf7da629

 Part of collection

Student theses

 Document type

master thesis

 Rights

© 2018 Pavlos Chatzoudis
Files
PDF report.pdf 5.79 MB
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