Deep Learning MRI-based Model for Prediction of
Clinically Significant Prostate Cancer
Name
yang-janicey-meng-eecs-2023-thesis.pdf
Description
Thesis PDF
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Author(s)
Yang, Janice
Advisor(s)
Barzilay, Regina
Date Issued
June 2023
Publisher
Massachusetts Institute of Technology
Abstract
Prostate cancer is one of the leading causes of death for men globally, despite many men being diagnosed with indolent tumors that do not warrant treatment. Increasingly, magnetic resonance imaging (MRI) is being used as a risk assessment tool, before more invasive prostate biopsies are performed for patients at suspicion of prostate cancer. We hypothesize that we can train a deep learning model that combines multi-parametric MRI images with clinical factors to accurately predict patient risk of developing clinically significant prostate cancer. We train an image model and combined image and clinical factors model on a set of 9391 MRIs from the Massachusetts General Brigham (MGB) hospital system, which achieved an area under the receiver-operator curve (AUROC) of 0.80 and 0.84, respectively, for 1-year prediction of clinically significant prostate cancer, surpassing current human baselines and existing risk models’ performance.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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