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.
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.
|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|
|Li, Hongming; Galperin-Aizenberg, Maya; Pryma, Daniel et al. (2018) Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. Radiother Oncol 129:218-226|
|Li, Hongming; Zhu, Xiaofeng; Fan, Yong (2018) Identification of Multi-scale Hierarchical Brain Functional Networks Using Deep Matrix Factorization. Med Image Comput Comput Assist Interv 11072:223-231|
|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|
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