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Classifying patients' response to tumour treatment from PET/CT data: a machine learning approach
KTH, School of Technology and Health (STH).
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Klassificering av patienters respons på tumörbehandling från PET/CT-data med hjälp av maskininlärning (Swedish)
Abstract [en]

Early assessment of tumour response has lately acquired big interest in the medical field, given the possibility to modify treatments during their delivery. Radiomics aims to quantitatively describe images in radiology by automatically extracting a large number of image features. In this context, PET/CT (Positron Emission Tomography/Computed Tomography) images are of great interest since they encode functional and anatomical information, respectively. In order to assess the patients' responses from many image features appropriate methods should be applied. Machine learning offers different procedures that can deal with this, possibly high dimensional, problem.

The main objective of this work was to develop a method to classify lung cancer patients as responding or not to chemoradiation treatment, relying on repeated PET/CT images. Patients were divided in two groups, based on the type of chemoradiation treatment they underwent (sequential or concurrent radiation therapy with respect to chemotherapy), but image features were extracted using the same procedure. Support vector machines performed classification using features from the Radiomics field, mostly describing tumour texture, or from handcrafted features, which described image intensity changes as a function of tumour depth. Classification performance was described by the area under the curve (AUC) of ROC (Receiving Operator Characteristic) curves after leave-one-out cross-validation. For sequential patients, 0.98 was the best AUC obtained, while for concurrent patients 0.93 was the best one. Handcrafted features were comparable to those from Radiomics and from previous studies, as for classification results. Also, features from PET alone and CT alone were found to be suitable for the task, entailing a performance better than random.

Place, publisher, year, edition, pages
2017. , 86 p.
Series
TRITA-STH, 2017:4
Keyword [en]
treatment response, PET/CT, Radiomics, feature extraction, support vector machines
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-200916OAI: oai:DiVA.org:kth-200916DiVA: diva2:1071918
Subject / course
Medical Engineering
Educational program
Master of Science - Medical Engineering
Presentation
2017-01-20, 13:00 (English)
Supervisors
Examiners
Available from: 2017-02-17 Created: 2017-02-04 Last updated: 2017-02-17Bibliographically approved

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