Master's · Research project
Resilient predictive models based on quantitative imaging to guide prostate cancer treatment
Robust imaging-based models for prostate cancer treatment guidance using CT and PET modalities.
Status
Completed (2021-2024)
Type
Master's
Project goal
Objective: To develop resilient predictive models based on quantitative imaging and clinical features to guide treatment for advanced prostate cancer using images from different modalities such as computed tomography (CT) and positron emission tomography (PET).
3 specific objectives:
- Generation of synthetic data to impute missing data.
- Automatic segmentation of organs of interest and detection of failed segmentations.
- Robust prediction of clinical outcomes based on stable radiomic features.
