Background: Prostatic adenocarcinoma is the most common newly diagnosed cancer and second deadliest
cancer in American men. There is a large discrepancy between the incidence of the disease and its mortality rate.
Thus, the development of screening tools to identify prostate cancer and determine if it is aggressive or indolent
is an area of considerable interest. Current methods rely on the use of serum biomarkers and follow-up biopsies
for screening. However, there is substantial debate as to the appropriate methodology for screening. The goal of
this proposal is the development of: 1) new imaging biomarkers (i.e., “features”) for prostate cancer; and 2) a
novel predictive model for the presence of aggressive prostatic adenocarcinoma. These tools will enable more
effective use of mp-MRI in prostate cancer screening in the future and thus enable a future improvement in the
sensitivity and specificity of screening, reducing the rates of overdiagnosis and underdiagnosis.
Aim 1: To implement a deep learning algorithm for clinical prostate mp-MRI sequences, creating a cancer prob-
ability map that is predictive of biopsy results.
Aim 2: To create a multimodal framework that will combine discovered imaging features with clinical data
points from the medical record (e.g., age, risk factors, medical history, biomarkers) to predict the presence and
aggressiveness of prostatic adenocarcinoma.
Methods: In Aim 1, a deep convolutional neural network (CNN) will be trained on a clinical dataset comprised
of patches extracted from pre-prostatectomy mp-MRI sequences from patients with prostate cancer, using his-
topathology analysis of whole-mount radical prostatectomy specimens as ground truth. The innovations in this
aim will be the development of a CNN that can simultaneously learn from three different imaging sequence types,
the use of patches for data augmentation, and the proper alignment of mp-MRI sequences and prostatectomy
specimens for machine learning. The result of the work of this aim will be the creation of an algorithm for gen-
erating imaging biomarkers (features) and cancer probability maps from mp-MRI data. In Aim 2, a multimodal
learning framework that will integrate mp-MRI sequence data with clinical parameters in order to predict the
presence of aggressive prostatic adenocarcinoma will be developed. The innovation in this aim will be the devel-
opment of a framework that can integrate information from multiple modalities (imaging, serum, history, etc.)
in order to generate a high confidence prediction of the presence of aggressive prostate cancer without the use of
Long-term Objective: The development of a novel predictive model for the presence of aggressive prostatic
adenocarcinoma in prostate mp-MRI data that will enable better future use of this data for the early detection of