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

通过基于机器学习的映射实现特定辐射的自动牙科剂量分布，以准确预测牙周问题 (RADMAP)

• 批准号: 10285226
• 负责人: Amy Catherine Moreno
• 金额: $ 20.89万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-02 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10285226
• 关键词: Acute; Address; Adoption; Aftercare; Agreement; Algorithmic Analysis; Algorithms; Anatomy; Area; Artificial Intelligence; Atlases; Award; Bioinformatics; Cancer Patient; Cancer Survivor; Caring; Chronic; Clinical; Clinical Management; Combined Modality Therapy; Communication; Computer Models; Computing Methodologies; Consensus; Data; Data Aggregation; Data Analyses; Decision Making; Delphi Technique; Dental; Dental Care; Dentistry; Dentists; Development; Disease; Disease Progression; Documentation; Dose; Exploratory/Developmental Grant for Diagnostic Cancer Imaging; FAIR principles; Fostering; Foundations; Future; Goals; Head and neck structure; Health; High Prevalence; Individual; Informatics; Intensity modulated proton therapy; Intensity-Modulated Radiotherapy; Interdisciplinary Communication; Journals; Knowledge; Label; Late Effects; Long-Term Care; Machine Learning; Mandible; Manuals; Manuscripts; Medical; Methodology; Methods; Modeling; Monitor; Morbidity - disease rate; National Institute of Dental and Craniofacial Research; Needs Assessment; Operative Surgical Procedures; Oral; Oral cavity; Oral health; Organ; Osteoradionecrosis; Outcome; Outcome Assessment; Parotid Gland; Patients; Peer Review; Periodontal Diseases; Phase; Pilot Projects; Procedures; Prognosis; Provider; Public Health; Publishing; Radiation; Radiation Dose Unit; Radiation Oncologist; Radiation therapy; Reporting; Reproducibility; Research; Resolution; Resources; Risk; Risk Assessment; Selection for Treatments; Severities; Standardization; Structure; Survivors; Symptoms; System; Techniques; Time; Tooth structure; Toxic effect; Training; Treatment outcome; Trismus; United States; Validation; X-Ray Computed Tomography; Xerostomia; base; cohort; convolutional neural network; craniofacial; deep learning; design; experience; improved; innovation; interest; learning strategy; machine learning method; malignant oropharynx neoplasm; neural network; novel; persistent symptom; personalized management; personalized medicine; prospective; response; risk prediction; risk prediction model; survival outcome; symptom management; tool; treatment optimization; treatment planning

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.

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。尽管肿瘤治疗取得了进步，但大多数患者仍将经历严重的毒性反应。 治疗期间和治疗后的负担，包括中到重度口干症，张口度降低(即三张嘴)， 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，并具有广泛的提供者间管理 受提供者经验和可用临床信息影响的可变性通常是不完整的， 不正确或不存在。为了进一步使对OC/OPC幸存者的长期护理复杂化，没有标准化的 与牙科医生沟通对牙齿轴承辐射剂量的范围和强度的方法 对于准确评估与牙科手术相关的风险而言，这是至关重要的信息。因此， 开发标准化的放射治疗牙科信息工具和数据驱动的算法毒性风险 OC/OPC幸存者加强沟通和个性化医疗的预测模型仍是一个 未得到满足的公共卫生需求。为了回应NIDCR的NOT-DE-20-006，我们在此提出了一个严格和 发展信息学和计算方法和途径的可重复应用 机器学习《基于ML/AI的精准牙科临床流程优化》，《新颖而稳健》 处理疾病发生和发展的因果作用机制的数据分析算法“与 治疗后口腔并发症，以及“用于治疗计划和评估的计算模型” 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(DLC)神经网络 用于自动划定牙齿承载区域的网络。我们的合作者范·迪克博士之前 DLC设计和应用于非牙科头颈部高危器官自动勾画的经验 (划桨)。她的研究发表在同行评议期刊上，显示出同等或显著改善的Oar 在基于地图集的等高线上使用DLC进行自动描绘，将作为我们的 建议的项目。使用基于DLC的下颌和牙桨描绘(SA 1)，我们将开发一种新的 将自动生成精确的终结性放射治疗剂量分布的“放射牙片” 为供应商之间的有效数据传输和通信绘制报告图(SA 2)。准确预测 和管理高发病率、高患病率的治疗后口腔后遗症将能够通过 基于统计稳健机器学习的毒性风险预测模型的开发 合并患者和提供者生成的数据(目标3)。总而言之，RADMAP提案促进了创新 解决多学科现有挑战的信息学和计算建模方法 对OC/OPC幸存者的沟通和精确牙科护理，具有改变实践的影响 临床环境和口腔、牙科和颅面研究。