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.

项目概要 在美国，超过 53,000 人患有口腔癌和口咽癌 (OC/OPC) 尽管肿瘤治疗取得了进步，但大多数患者仍会经历显着的毒性。 治疗期间和治疗后的负担，包括中度至重度口干症、张口减少（即牙关紧闭）、 迄今为止，急性和慢性口腔牙齿并发症主要是牙周病和放射性骨坏死。 由主教和牙医根据经验知识进行管理，具有广泛的供应商间管理 受提供者经验和可用临床信息影响的变异性，这些信息通常不完整， 不正确或不存在，使 OC/OPC 幸存者的长期护理进一步复杂化。 与牙医沟通传递到牙齿轴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。 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 OC/OPC 幸存者加强沟通和个性化医疗的预测模型仍然是一个 未满足的公共卫生需求 为了回应 NIDCR 的 NOT-DE-20-006，我们在此提出严格且有效的建议。 信息学和计算方法的可重复应用以及开发方法 机器学习“基于 ML/AI 的临床程序优化，实现精准牙科护理”、“新颖而稳健 数据分析算法来解决疾病发生和进展的因果机制” 治疗后口腔并发症，以及“治疗计划和评估的计算模型” 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 我们的合作者 van Dijk 博士之前有过自动描绘牙齿区域的网络。 具有 DLC 设计和应用经验，用于自动描绘处于危险中的非牙科头颈器官 (OAR)。她的研究发表在同行评审期刊上，显示 OAR 具有同等或显着改善。 在基于图集的轮廓上使用 DLC 进行自动描绘，将作为我们的可重复模型 我们将使用基于 DLC 的下颌和牙齿 OAR 轮廓（SA 1）开发一种新颖的项目。 “放射牙齿图”将生成自动且准确的总结性放射治疗剂量分布 提供者之间有效数据传输和通信的映射报告（SA 2）。 通过以下方式可以管理高发病率、高患病率的治疗后口腔后遗症 开发了一种基于机器学习的惊人稳健的毒性风险预测模型 纳入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来解决多学科领域现有的挑战 OC/OPC 幸存者的沟通和精准牙科护理，对改变实践产生影响 临床环境以及口腔、牙科和颅面研究。