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

多样性补充：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）牙周问题 (RADMAP)

• 批准号: 10602003
• 负责人: Amy Catherine Moreno
• 金额: $ 7.87万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10602003
• 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; Parents; 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; data communication; data exchange; deep learning; design; experience; improved; innovation; interest; learning strategy; machine learning algorithm; machine learning method; machine learning model; 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 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.

项目总结

项目成果

National Institutes of Health Diversity Supplement Awards: Experience of Radiation Oncology Principal Investigators and Trainees.

美国国立卫生研究院多样性补充奖：放射肿瘤学首席研究员和实习生的经验。

• DOI: 10.1016/j.ijrobp.2023.03.034
• 发表时间: 2023
• 期刊: International journal of radiation oncology, biology, physics
• 作者: Yorke,AfuaA;Rooney,MichaelK;Rigert,Jillian;Moreno,AmyC;Fuller,CliftonD;Ford,EricC
• 通讯作者: Ford,EricC