This project will investigate three-main task in soft-tissue sarcomas. The first is the prediction of disease progression or survival given different time point scans of the patient. The second consist of grading the aggressiveness of the sarcomas in a three-class classification task. The third task is related to the medical image segmentation to automate the treatment planning.
Figure 1: Project description illustration. The system must first segment the soft-tissue sarcoma, followed by a grading prediction of the aggressiveness of the tumor and the disease survival or progression.
Soft-tissue sarcomas are an aggressive type of tumor that develops in patient’s soft-tissue without noticeable side effects until it is detected in advance stages. Once it is detected, patients receive MRI studies for treatment planning and estimation of patient's outcome. To assess the aggressiveness of a tumor, invasive biopsies are additionally necessary. Using current prognostic models and therapies about half of all patients still suffer from tumor spread throughout the body. With a novel computer-aided system to automatically analyze and segment soft-tissue sarcomas based solely on the MRI scan, which is a non-invasive imaging modality, may help to advance patient care.
Disease progression and survival is a well-known topic and widely investigated in brain tumor [1]. Most research focuses on analyzing radiomic features and their relationship to MRI features as described in [2,3]. Nevertheless, extraction of radiomic features is an expensive and time-consuming task leading to an unsuitable solution for real medical applications. As an alternative MRI features are a potential surrogate solution, where the final goal is to be able to analyze soft-tissue sarcomas given an MRI of the patient. To the best of our knowledge, this is the first work in this direction and the one we would like to explore in this research project.
The student task would be to perform the following tasks:
Depending on the desired thesis type and student's preferences, the tasks will be adapted to suit the scope of the project.
[2] Vallières, M., Freeman, C.R., Skamene, S.R. and El Naqa, I., 2015. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Physics in Medicine & Biology, 60(14), p.5471.
[3] Corino, V.D., Montin, E., Messina, A., Casali, P.G., Gronchi, A., Marchianò, A. and Mainardi, L.T., 2018. Radiomic analysis of soft tissues sarcomas can distinguish intermediate from high‐grade lesions. Journal of Magnetic Resonance Imaging, 47(3), pp.829-840.
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Title: | Computer-aided survival, grading prediction and segmentation of soft tissue sarcomas in MRI. |
Abstract: | This project will investigate three-main task in soft-tissue sarcomas. The first is the prediction of disease progression or survival given different time point scans of the patient. The second consist of grading the aggressiveness of the sarcomas in a three-class classification task. The third task is related to the medical image segmentation to automate the treatment planning. |
Student: | |
Director: | Prof. Bjoern Menze |
Supervisor: | Fernando Navarro |
Type: | DA/MA/BA |
Area: | |
Status: | finished |
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Finish: | |
Thesis (optional): | |
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