Prediction of brain tumor progression using a machine learning technique

Yuzhong Shen; Debrup Banerjee; Jiang Li; Adam Chandler; Yufei Shen; Frederic D. McKenzie; Jihong Wang

doi:10.1117/12.844035

9 March 2010 Prediction of brain tumor progression using a machine learning technique

Yuzhong Shen, Debrup Banerjee, Jiang Li, Adam Chandler, Yufei Shen, Frederic D. McKenzie, Jihong Wang

Proceedings Volume 7624, Medical Imaging 2010: Computer-Aided Diagnosis; 762425 (2010) https://doi.org/10.1117/12.844035
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

A machine learning technique is presented for assessing brain tumor progression by exploring six patients' complete MRI records scanned during their visits in the past two years. There are ten MRI series, including diffusion tensor image (DTI), for each visit. After registering all series to the corresponding DTI scan at the first visit, annotated normal and tumor regions were overlaid. Intensity value of each pixel inside the annotated regions were then extracted across all of the ten MRI series to compose a 10 dimensional vector. Each feature vector falls into one of three categories:normal, tumor, and normal but progressed to tumor at a later time. In this preliminary study, we focused on the trend of brain tumor progression during three consecutive visits, i.e., visit A, B, and C. A machine learning algorithm was trained using the data containing information from visit A to visit B, and the trained model was used to predict tumor progression from visit A to visit C. Preliminary results showed that prediction for brain tumor progression is feasible. An average of 80.9% pixel-wise accuracy was achieved for tumor progression prediction at visit C.

Citation Download Citation

Yuzhong Shen, Debrup Banerjee, Jiang Li, Adam Chandler, Yufei Shen, Frederic D. McKenzie, and Jihong Wang "Prediction of brain tumor progression using a machine learning technique", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 762425 (9 March 2010); https://doi.org/10.1117/12.844035

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