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Abstract #2604

The feasibility of model-free machine learning with variable features of MRI images on the prostate cancer application 

Feng-Mao Chiu1,2, Ting Chun Lin1, Queenie Chan3, Cheng-Chun Li4, Jen-I Hwang4, and You Yin Chen1
1Department of Biomedical Engineering, National Yang-Ming University, Taipei, Taiwan, 2Health system, Philips, Taipei, Taiwan, 3Healthcare, Philips, Hong Kong, China, 4Department of Medical Imaging, Tungs’ Taichung Metroharbor Hospital, Taichung, Taiwan

Several advanced post-processing methods were used to assess prostate cancer, including diffusion model of IVIM, T2 mapping with multi-echo turbo spin echo , and permeability analysis with Tofts model. We found that it is not easy to overcome the fitting error, and it is not reasonable to directly integrate IVIM, T2 mapping and permeability together as well. The aim of this study is to evaluate the feasibility of the prostate cancer screening with model-free machine leaning , and to test different combinations of input data, including multiple b-value diffusion weighted imaging (DWI), multi-echo T2w TSE, and dynamic contrast enhanced (DCE) images.

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