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

口咽癌(OPC)是少数几种发病率上升的国内癌症之一，主要原因是 人类乳头瘤病毒(HPV)感染率增加。放射学图像对于评估 OPC和辅助疾病检测和放射治疗(RT)治疗。然而，传统的RT规划 成像需要依赖于操作员的肿瘤分割，这是治疗错误的主要来源 导致对正常组织的意外剂量和随后衰弱的口腔牙齿后遗症。此外，人乳头瘤病毒 OPC表达不同的肿瘤/结节中期RT反应(快速反应)率，导致显著 计划和提供的RT剂量之间的差异。此外，对于HPV OPC患者进行腔内治疗 抵抗子体积，正常组织的保留程度取决于残存活动的位置 疾病。多参数磁共振成像(MpMRI)技术结合了同时的高维解剖 与人工智能(AI)方法相结合的功能信息可以改善临床决策 通过为适应性RT计划提供立即可操作的临床理论来支持OPC。这个 这个F31项目的假设是mpMRI技术和人工智能算法将促进分割，快速 OPC的疗效预测和治疗内耐药分类。为了检验这一假设，我首先 利用mpMRI建立人工智能模型，准确分割原发灶和转移性颈淋巴结 并以人类专家为基准对模型进行基准测试(具体目标1)。接下来，我将调查 快速治疗应答者和无应答者原发肿瘤/结节之间的mpMRI，随后使用 人工智能建立响应预测模型(具体目标2)。最后，我将描述原发肿瘤的区域 在mpMRI的区域和体素水平上的治疗阻力，并随后使用AI来建立阻力 分类模型(具体目标3)。通过这个F31奖项中提出的专门培训，我将获得 临床决策支持工具实施和设计方面的专业知识(培训目标1)，制定方法学 创新医学成像深度学习(培训目标2)，获得统计建模方面的专业知识和 临床信息学方法(培训目标3)，以及从研究生研究过渡到有指导的研究生研究和最终的独立首席研究员地位(培训目标4)。为了成功 完成我提出的具体目标，实现我的培训目标，我已经组建了一个专门的小组 导师和合作者，他们将在整个项目期间为我提供出色的指导。 此外，这个项目将在国际知名的癌症MD安德森癌症中心进行 该机构拥有世界上一些最大的头颈部癌症患者的成像数据集。 因此，通过这个F31奖项，我处于独特的地位，可以在这个研究领域开展开创性的工作。

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.