Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance

口咽癌（OPC）是国内为数不多的发病率上升的癌症之一，主要是由于

Oropharyngeal cancer (OPC) is one of the few domestic cancers that is rising in incidence, primarily due to increased human papillomavirus (HPV) infection rates. Radiographic images are crucial for assessment of OPC and aid in disease detection and radiotherapy (RT) treatment. However, RT planning with conventional imaging requires operator-dependent tumor segmentation, which is the primary source of treatment error leading to unintended dose to normal tissues and subsequent debilitating oro-dental sequelae. Further, HPV+ OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Moreover, for HPV+ OPC patients with intra-treatment resistant sub-volumes, the degree of normal tissue sparing is dependent on the location of residual active disease. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous high-dimensional anatomical and functional information coupled to artificial intelligence (AI) approaches could improve clinical decision support for OPC by providing immediately actionable clinical rationale for adaptive RT planning. The hypothesis of this F31 project is that mpMRI techniques and AI algorithms will facilitate segmentation, rapid response prediction, and intra-treatment resistance classification of OPC. To test this hypothesis, I will first develop an AI model using mpMRI to accurately segment primary tumors and metastatic cervical lymph nodes and benchmark the model against human experts (Specific Aim 1). Next, I will investigate the differences in mpMRI between primary tumors/nodes of rapid therapy responders and non-responders and subsequently use AI to build a response prediction model (Specific Aim 2). Finally, I will characterize areas of primary tumor treatment resistance at the regional and voxel level on mpMRI and subsequently use AI to build a resistance classification model (Specific Aim 3). Through dedicated training proposed in this F31 award, I will gain expertise in clinical decision support tool implementation and design (Training Goal 1), develop methodological innovations for deep learning in medical imaging (Training Goal 2), gain expertise in statistical modeling and clinical informatics approaches (Training Goal 3), and transition from graduate research to mentored post-graduate research and eventual independent principal investigator status (Training Goal 4). To successfully complete my proposed specific aims and achieve my training goals, I have assembled a dedicated group of mentors and collaborators that will provide me with excellent guidance throughout this project period. Moreover, this project will take place at MD Anderson Cancer Center, an internationally renowned cancer institution that is home to some of the largest imaging datasets of head and neck cancer patients in the world. Therefore, I am uniquely positioned to conduct pioneering work in this research space through this F31 award.