We are developing new machine learning methods for image reconstruction, classification and treatment outcome prediction. Early prediction of treatment outcome is important in clinical decision for patient management in radiotherapy. We have developed a number of new radiomics features and different machine learning algorithms such as kernelled support tensor vector machine for distant failure prediction of NSCLC and cervical cancer patients after radiation therapy. We have also developed a multi-modality radiomics model coupled with an immune based optimization strategy to improve the performance of these prediction models.
- Zhou, M. Folkert, N. Cannon, P. Iyengar, K. Westover, Y. Zhang, H. Choy, R. Timmerman, S. Jiang, J. Yan, X-J Xie, and J. Wang, Predicting distant failure in early stage NSCLC treated with SBRT using clinical parameters, Radiotherapy and Oncology, vol. 119, pp. 501-504, 2016
- Zhou, M. Folkert, P. Iyengar, K. Westover, Y. Zhang, H. Choy, R. Timmerman, S. Jiang, and J. Wang, “Multi-objective radiomics model for predicting distant failure in lung SBRT", Physics in Medicine and Biology, vol. 62, pp. 4460-4478, 2017
- Hao, Z. Zhou, S. Li, G. Maquilan, M. Folkert, P. Iyengar, K. Westover, K. Albuquerque, F. Liu, H. Choy, R. Timmerman, L. Yang, and J. Wang, Shell: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervical cancer, Physics in Medicine and Biology, vol. 63, 095007 (17pp), 2018
- Li, B. Li, Z. Zhou, N. Yang, H. Hao, M. Folkert, P. Iyengar, K. Westover, H. Choy, R. Timmerman, S. Jiang, and J. Wang, A pilot study using kernelled support tensor machine for distant failure prediction in lung SBRT, Medical Imaging Analysis, vol. 50, pp.106-116, 2018
- Chen, K. Xiang, Z. Gong, J. Wang*, and S. Tan, Statistical Iterative CBCT Reconstruction Based on Neural Network, IEEE Trans. on Medical Imaging, vol. 37, pp. 1511-1521, 2018 (*co-corresponding author), Code for this work can be found here.