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 invasive testing. 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 prostate cancer.

Public Health Relevance

/ Public Health Relevance Prostatic adenocarcinoma is the most common newly diagnosed cancer and second deadliest cancer in American men. This research aims to develop imaging features and a predictive model that will enable better use of mul- tiparametric MRI for noninvasive prostate cancer screening, which could reduce over- and underdiagnosis in order to provide better care at lower cost.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Individual Predoctoral NRSA for M.D./Ph.D. Fellowships (ADAMHA) (F30)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Damico, Mark W
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of California Los Angeles
Schools of Medicine
Los Angeles
United States
Zip Code
Li, Jiayun; Speier, William; Ho, King Chung et al. (2018) An EM-based semi-supervised deep learning approach for semantic segmentation of histopathological images from radical prostatectomies. Comput Med Imaging Graph 69:125-133
Garcia-Gathright, Jean I; Matiasz, Nicholas J; Adame, Carlos et al. (2018) Evaluating Casama: Contextualized semantic maps for summarization of lung cancer studies. Comput Biol Med 92:55-63
Li, Jiayun; Sarma, Karthik V; Chung Ho, King et al. (2017) A Multi-scale U-Net for Semantic Segmentation of Histological Images from Radical Prostatectomies. AMIA Annu Symp Proc 2017:1140-1148