Diversity Supplement: Radiation-specific Automated Dental Dose Distributions via Machine-learning based Mapping for Accurate Predictions of (Peri)odontal Problems (RADMAP)

项目总结 在美国，口腔和口咽癌(OC/OPC)困扰着超过53,000人 每年一次。尽管在肿瘤治疗方面取得了进步，但大多数患者将经历显著的 治疗期间和治疗后的毒性负担，包括中-重度口干，张口度降低(即 Trismus)、牙周病和放射性骨坏死。到目前为止，急性和慢性口腔并发症有 主要由临床医生和牙医根据经验知识进行管理，并有广泛的提供者之间 管理的可变性受提供者的经验和可用的临床信息的影响，通常 不完整的、不正确的或不存在的为了进一步复杂化对OC/OPC幸存者的长期护理，没有 与牙医沟通辐射剂量的范围和强度的标准化方法 牙齿支撑区，这是准确评估牙科手术相关风险的重要信息。 因此，开发一个标准化的放射治疗牙科信息工具和数据驱动、算法 OC/OPC加强沟通和个性化用药的毒性风险预测模型 幸存者仍然是一个未得到满足的公共卫生需求。针对NIDCR的NOT-DE-20-006，我们在此建议 信息学和计算方法和途径的严格和可重复的应用 机器学习《基于ML/AI的精密牙科临床流程优化》， 新的和强大的数据分析算法，以处理发病和进展的因果作用机制 与治疗后口腔并发症有关的疾病，以及用于治疗的计算机模型 规划和评估治疗结果。“在具体目标1中，我们将训练和验证深度 学习轮廓线(DLC)神经网络用于自动划定牙齿承载区域。我们的合作者， Van Dijk博士，有DLC设计和应用于自动描绘非牙科的经验 头颈部器官危险(OAR)。她的研究发表在同行评议期刊上，显示出与 与基于地图集的等高线绘制相比，使用DLC显著改进了桨自动绘制，将作为 我们提议的项目的可重复使用的模型。使用基于DLC的下颌和牙桨勾画(SA 1)，我们将开发一种新型的放射牙片，它将自动生成准确的总结 用于有效数据传输和通信的放射治疗剂量分布映射报告 供应商(SA 2)。高发病率、高患病率患者治疗后的准确预测和处理 口腔后遗症将通过开发基于统计稳健的机器学习来实现 结合患者和提供的数据的毒性风险预测模型(目标3)。总而言之， RADMAP提案促进了创新的信息学和计算建模方法，以解决 OC/OPC幸存者在多学科沟通和精确牙科护理方面存在的挑战， 在临床环境和口腔、牙科和颅面研究中改变实践的意义。

PROJECT SUMMARY Oral cavity and oropharyngeal (OC/OPC) cancers afflict more than 53,000 individuals in the United States annually. Despite advancements in oncologic therapies, the majority of patients will experience significant toxicity burden during and after therapy, including moderate-severe xerostomia, reduced mouth opening (i.e. trismus), periodontal disease, and osteoradionecrosis. To date, acute and chronic orodental complications are largely managed by clinicians and dentists based on empirical knowledge, with wide inter-provider management variability influenced by provider experience and available clinical information which is often incomplete, incorrect, or nonexistent. To further complicate long-term care of OC/OPC survivors, there is no standardized method for communicating with dentists the extent and intensity of radiation doses delivered to tooth bearing areas which is vital information for accurate assessment of risks related to dental procedures. Therefore, development of a standardized radiotherapy dental information tool and data-driven, algorithmic toxicity risk prediction models for enhanced communication and personalized medicine for OC/OPC survivors remains an unmet public health need. In response to NIDCR’s NOT-DE-20-006, we herein propose a rigorous and reproducible application of informatics and computational methods and approaches for the development of machine learning “ML/AI based optimization of clinical procedures for precision dental care”, “novel and robust data analysis algorithms to tackle causal mechanisms of action for onset and progression of disease” related to posttherapy orodental complications, and “computational modeling for treatment planning and assessment of treatment outcomes.” In Specific Aim 1, we will train and validate a deep learning contouring (DLC) neural network for automatic delineation of tooth-bearing regions. Our collaborator, Dr. van Dijk, has previous experience with DLC design and application for auto- delineation of non-dental head and neck organs at risk (OAR). Her research, published in a peer- reviewed journal showing an equal or significantly improved OAR automatic delineation using DLC over atlas-based contouring, will serve as a reproducible model for our proposed project. Using DLC-based mandibular and dental OAR delineation (SA 1), we will develop a novel “radiation odontogram” which will generate automated and accurate summative radiotherapy dose distribution mapping reports for effective data transmission and communication among providers (SA 2). Accurate prognosis and management of high-morbidity high-prevalence post- therapy orodental sequelae will be enabled through the development of a statistically robust machine-learning based model of toxicity risk predictions that incorporates patient- and provide- generated data (Aim 3). In summary, the RADMAP proposal fosters innovative informatics and computational modeling approaches to address existing challenges in multidisciplinary communication and precision dental care for OC/OPC survivors, with practice-changing implications in the clinical setting and for oral, dental, and craniofacial research.