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，但大多数患者仍会出现严重的毒性反应。 治疗期间和治疗后的负担，包括中度-重度口干症，张口减少（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主要是 由临床医生和牙医根据经验知识进行管理，并进行广泛的供应商间管理 受提供者经验和通常不完整的可用临床信息影响的可变性， 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，没有标准化的 与牙医沟通传递到牙齿支承的辐射剂量的范围和强度的方法 这是准确评估牙科手术相关风险的重要信息。因此，我们认为， 开发标准化放射治疗牙科信息工具和数据驱动的算法毒性风险 为OC/OPC幸存者提供增强沟通和个性化医疗的预测模型仍然是一个挑战。 未满足的公共卫生需求。作为对NIDCR的NOT-DE-20-006的回应，我们在此提出了一个严格且 信息学和计算方法和方法的可重复应用， 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 数据分析算法，以解决与疾病的发病和进展有关的因果作用机制， 治疗后口腔并发症，以及“治疗计划和评估的计算建模”， 治疗效果”。在具体目标1中，我们将训练和验证深度学习轮廓（DLC）神经网络 用于自动描绘牙齿承载区域的网络。我们的合作者，货车迪克博士， DLC设计和应用于非牙科头颈部危险器官自动描绘的经验 （OAR）。她的研究发表在一份同行评议的期刊上，显示OAR有同等或显著改善 自动划定使用DLC的地图集为基础的轮廓，将作为一个可重复的模型，我们 拟议项目。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 “放射牙科图”将自动生成准确的总结性放射治疗剂量分布 映射报告，以便在提供者之间进行有效的数据传输和通信（SA 2）。准确预测 治疗后高发病率高患病率的口腔牙齿后遗症的管理将通过 开发基于统计学的稳健机器学习的毒性风险预测模型， 合并患者和供应商生成的数据（目标3）。总之，RADMAP提案促进了创新 信息学和计算建模方法，以解决现有的挑战，在多学科 OC/OPC幸存者的沟通和精确牙科护理， 临床设置和口腔，牙科和颅面研究。

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 datatransmission 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.