2026-09-22T04:32:19Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1516632023-08-01T03:59:33Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Barzilay, Regina
Yang, Janice
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-07-31T19:57:19Z
2023-07-31T19:57:19Z
2023-06
2023-06-06T16:35:04.621Z
https://hdl.handle.net/1721.1/151663
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.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Deep Learning MRI-based Model for Prediction of
Clinically Significant Prostate Cancer
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<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.</Abstract>
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