Colorectal cancer is the 3rd most common newly diagnosed cancer and the 3rd most common cause of cancer death among US men and women. Neoadjuvant chemoradiation therapy (CRT) followed by total mesorectal excision is the standard of care for locally advanced rectal cancer. Preoperative CRT has clearly improved rates of local disease control and colostomy free survival; however the response to therapy is heterogeneous. It would be very useful to be able to predict the individual risk of each patient, so that their therapy can be personalized. The goal of our study is to derive clinically useful radiomic signatures from multimodal imaging data for the early prediction of treatment outcomes in rectal cancer patients. Central to our methodology are 1) an improved deep learning model for automatically segmenting tumors from multimodal imaging data with high accuracy; and 2) a multi-task deep learning model for robustly learning informative radiomic features to predict survival and recurrence. These methods will be used to derive individualized predictive indices of treatment outcomes based on a multimodal imaging dataset of rectal cancer patients who have received preoperative CRT. Our methods are generally applicable to radiomic studies of cancer patients. All methods will be made publicly available and form an important new resource for the broader radiomics community.

Public Health Relevance

This project will develop multimodal imaging data analytic tools for the early prediction of treatment outcomes in rectal cancer patients who have received preoperative neoadjuvant chemoradiation therapy.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21CA223358-01
Application #
9438989
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Redmond, George O
Project Start
2018-01-01
Project End
2019-12-31
Budget Start
2018-01-01
Budget End
2018-12-31
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Pennsylvania
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Li, Hongming; Fan, Yong (2018) Brain Decoding from Functional MRI Using Long Short-Term Memory Recurrent Neural Networks. Med Image Comput Comput Assist Interv 11072:320-328
Li, Hongming; Fan, Yong (2018) Identification of Temporal Transition of Functional States Using Recurrent Neural Networks from Functional MRI. Med Image Comput Comput Assist Interv 11072:232-239
Zheng, Qiang; Fan, Yong (2018) INTEGRATING SEMI-SUPERVISED LABEL PROPAGATION AND RANDOM FORESTS FOR MULTI-ATLAS BASED HIPPOCAMPUS SEGMENTATION. Proc IEEE Int Symp Biomed Imaging 2018:154-157
Li, Hongming; Fan, Yong (2018) NON-RIGID IMAGE REGISTRATION USING SELF-SUPERVISED FULLY CONVOLUTIONAL NETWORKS WITHOUT TRAINING DATA. Proc IEEE Int Symp Biomed Imaging 2018:1075-1078
Men, Kuo; Boimel, Pamela; Janopaul-Naylor, James et al. (2018) Cascaded atrous convolution and spatial pyramid pooling for more accurate tumor target segmentation for rectal cancer radiotherapy. Phys Med Biol 63:185016
Li, Hongming; Satterthwaite, Theodore D; Fan, Yong (2018) BRAIN AGE PREDICTION BASED ON RESTING-STATE FUNCTIONAL CONNECTIVITY PATTERNS USING CONVOLUTIONAL NEURAL NETWORKS. Proc IEEE Int Symp Biomed Imaging 2018:101-104
Zhu, Xiaofeng; Zhang, Weihong; Fan, Yong et al. (2018) A Robust Reduced Rank Graph Regression Method for Neuroimaging Genetic Analysis. Neuroinformatics 16:351-361
Zheng, Qiang; Wu, Yihong; Fan, Yong (2018) Integrating Semi-supervised and Supervised Learning Methods for Label Fusion in Multi-Atlas Based Image Segmentation. Front Neuroinform 12:69
Zheng, Qiang; Tasian, Gregory; Fan, Yong (2018) TRANSFER LEARNING FOR DIAGNOSIS OF CONGENITAL ABNORMALITIES OF THE KIDNEY AND URINARY TRACT IN CHILDREN BASED ON ULTRASOUND IMAGING DATA. Proc IEEE Int Symp Biomed Imaging 2018:1487-1490
Zheng, Qiang; Warner, Steven; Tasian, Gregory et al. (2018) A Dynamic Graph Cuts Method with Integrated Multiple Feature Maps for Segmenting Kidneys in 2D Ultrasound Images. Acad Radiol 25:1136-1145

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