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

项目摘要 在美国，口腔和口咽（OC/OPC）癌症折磨着超过53，000人 每年。尽管肿瘤治疗取得了进展，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，包括中度-重度口干症，张口度降低（即， 牙关紧闭）、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是 主要由临床医生和牙医根据经验知识进行管理， 管理可变性受提供者经验和可用临床信息的影响， 不完整、不正确或不存在。为了进一步复杂化OC/OPC幸存者的长期护理， 与牙医沟通的辐射剂量范围和强度的标准化方法， 牙齿承载区域，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的算法， 用于OC/OPC增强沟通和个性化医疗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。作为对NIDCR的NOT-DE-20-006的回应，我们在此建议 一个严格的和可重复的应用信息学和计算方法和方法， 开发机器学习“基于ML/AI的精准牙科护理临床程序优化”， “新颖而强大的数据分析算法，以解决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模型 计划和评估治疗结果。”在具体目标1中，我们将训练和验证一个深入的 学习轮廓（DLC）神经网络的自动划定牙齿轴承区域。我们的合作者， 博士货车Dijk，具有DLC设计和应用于非牙科自动描绘的经验 头颈部危险器官（OAR）她的研究发表在一份同行评议的杂志上， 使用DLC而不是基于地图集的轮廓绘制，大大改进了OAR自动描绘， 为我们提出的项目提供可重复的模型。使用基于DLC的下颌骨和牙齿OAR描绘（SA 1），我们将开发一种新的“放射性牙科照片”，它将产生自动和准确的总结 放射治疗剂量分布绘图报告，用于有效的数据传输和通信 供应商（SA 2）。治疗后高发病率高患病率的准确预后和管理 口腔后遗症将通过开发一个统计上强大的机器学习为基础， 毒性风险预测模型，结合患者和提供者生成的数据（目标3）。总的来说， RADMAP建议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，牙科和颅面研究的实践改变的影响。

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.