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.

项目摘要 在美国，口腔和口咽（OC/OPC）癌症遭受了53,000多人的困扰 尽管肿瘤学疗法的进步，大多数患者将经历重大毒性 伯恩在治疗期间和之后，包括中期骨静脉曲菌，口腔张开（即三义）， 牙周疾病和骨ado骨。迄今为止，急性和慢性发育并发症主要是 由临床医生和牙医基于经验知识管理，并具有广泛的管理人员管理 受提供者经验和可用临床信息影响的可变性，通常不完整， 不正确或不存在。为了进一步复杂化OC/OPC幸存的长期护理，没有标准化 与牙医交流辐射剂量的程度和强度的方法 对于准确评估与牙科程序相关的风险的重要信息的区域。所以， 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 增强沟通和个性化医学的预测模型OC/OPC存活仍然是一个 未满足的公共卫生需求。为了响应NIDCR的非DE-20-006，我们在这里提出了一个严格的和 可重现信息和计算方法和方法的应用 机器学习“基于ML/AI的精确牙科护理临床程序的优化”，“新颖而健壮 数据分析算法以解决与疾病发作和进展的因果关系机制 治疗后的并发症，以及“用于治疗计划和评估的计算建模 在特定的目标1中，我们将训练和验证深度学习轮廓（DLC）中性 自动描绘牙齿牙齿区域的网络。我们的合作者Van Dijk博士以前 具有DLC设计和应用的经验 （桨）。她的研究发表在同行评审的期刊上，显示了相等或显着改善的桨 使用基于Atlas的轮廓上的DLC自动描述将作为我们的可再现模型 拟议项目。使用基于DLC的下颌和牙科划分（SA 1），我们将开发一种新颖 “辐射探针图”将产生自动化和准确的夏季放射治疗剂量分布 绘制报告提供有效的数据传输和提供商之间的通信报告（SA 2）。准确的提示 并通过治疗后疗法后的后遗症的高质量管理。 基于统计坚固的机器学习模型的毒性风险预测模型的发展 合并患者和提供的数据（AIM 3）。总而言之，Radmap提案促进了创新 信息和计算建模方法，以应对多学科的现有挑战 OC/OPC生存的沟通和精确牙科护理，对实践改变了 临床环境以及口腔，牙齿和颅面研究。