Administrative Supplement to Support Collaborations to Improve AIML-Readiness of NIH-Supported Data for Parent Award SCH: Personalized Rescheduling of Adaptive Radiation Therapy for Head & Neck Cancer

支持合作的行政补充，以提高 NIH 支持的家长奖数据的 AIML 就绪性 SCH：头部自适应放射治疗的个性化重新安排

• 批准号: 10594327
• 负责人: Clifton David Fuller
• 金额: $ 32.04万
• 依托单位: RICE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10594327
• 关键词: Acute; Administrative Supplement; Adverse event; Aftercare; Agreement; Artificial Intelligence; Award; Barium swallow; Benchmarking; Collaborations; Collection; Common Terminology Criteria for Adverse Events; Communities; Complication; Consensus; Data; Data Pooling; Data Set; Databases; Deglutition; Deposition; Development; Digital Imaging and Communications in Medicine; Dose; Enteral Feeding; Equipment and supply inventories; Frequencies; Functional disorder; Funding; Head Cancer; Head and neck structure; Image; Individual; Information Dissemination; Institution; Label; Machine Learning; Magnetic Resonance Imaging; Manuscripts; Measures; Medical Imaging; Modeling; Neck Cancer; Nomenclature; Normal tissue morphology; Ontology; Organ; Outcome; Parents; Pathologic; Patient Outcomes Assessments; Patients; Physicians; Physics; Prevalence; Probability; Procedures; Process; Protocols documentation; Publications; Radiation Oncology; Radiation therapy; Radiology Specialty; Readiness; Registries; Reporting; Research; Risk; Roentgen Rays; Serial Magnetic Resonance Imaging; Statistical Models; Symptoms; Testing; The Cancer Imaging Archive; Therapeutic; Time; Toxic effect; Tumor Tissue; United States National Institutes of Health; Validation; automated segmentation; base; cancer imaging; cancer therapy; cohort; crowdsourcing; data curation; data integrity; data repository; design; experience; head and neck cancer patient; imaging Segmentation; improved; interest; large-scale database; learning community; machine learning model; medical attention; parent grant; personalized medicine; predicting response; predictive modeling; prospective; radiomics; repository; response; serial imaging; therapy outcome; treatment planning; treatment response; tumor

Project Summary We have collected, under our parent award (1R01CA257814-01), a database of serial multi-parametric magnetic resonance (MR) images as well as patient-reported and objective toxicity measures for more than 400 head and neck (HNC) patients, at pre-, on-, and post-therapy. We plan to utilize this data, in complete alignment with the first specific aim of the parent award, to effectively quantify treatment-related response on tumor/node and normal tissue in order to develop personalized treatment planning adaptations for individual HNC patients. As the most data-rich image toxicity cohort to the best of our knowledge, however, this database necessitates rigorous curation to be utilized for artificial intelligence/machine learning (AI/ML) approaches to predict, for example, tumor complication probability (TCP) and normal tissue complication probability (NTCP). Specifically, multi-observer segmentation of tumor and normal tissue regions of interest is required. Additionally, dissemination efforts are necessary to engage experts from AI/ML communities to develop AI/ML-approaches for auto-segmentation models, and TCP/NTCP predictions. To this end, we plan to undertake three specific aims. Through our first specific aim, we plan to curate our serial multi-parametric, multi time-point MRI dataset (accompanied with extracted radiomics) for therapeutic response and TCP prediction through assembling a team of three physicians to obtain the ground-truth segmented images. We further plan to deposit the curated segmented images as a dataset to The Cancer Imaging Archive (TCIA). As our second specific aim, we plan for curation and public deposition of matched image-dose multi-time-point acute and late toxicity metrics to be disseminated to both AI/ML experts for NTCP modeling. We will particularly include patient-reported MD Anderson Symptom Inventory-Head and Neck (MDASI-HN) toxicity outcomes, Common Toxicity Criteria- Adverse Events (CTC-AE) physician-ranked toxicity, and objective measures of swallowing dysfunction such as modified barium swallowing and tube-feeding assessments. In the third specific aim, we plan to design and execute a public crowdsourced challenge for serial image dose- response prediction for both TCP and NTCP prediction modeling tasks. Based on the test dataset that we plan to release after the execution of the challenge, we will conduct a post-challenge analysis on the submitted models (e.g., false-positive, and false-negative cases), and disseminate the best results as manuscripts to be submitted for publications and presentations. If successful, the proposed efforts are directly responsive to the need for AI/ML-ready datasets to be utilized for cancer treatment.

项目概要 根据我们的家长奖（1R01CA257814-01），我们收集了一个连续多参数数据库 磁共振（MR）图像以及患者报告和客观毒性测量超过 400 名头颈 (HNC) 患者在治疗前、治疗中和治疗后。我们计划完整地利用这些数据 与家长奖的第一个具体目标保持一致，以有效量化与治疗相关的反应 肿瘤/淋巴结和正常组织，以便制定个性化治疗计划适应 个别 HNC 患者。然而，据我们所知，作为数据最丰富的图像毒性队列， 该数据库需要严格的管理才能用于人工智能/机器学习 (AI/ML) 预测肿瘤并发症概率 (TCP) 和正常组织并发症等的方法 概率（NTCP）。具体来说，肿瘤和正常组织感兴趣区域的多观察者分割是 必需的。此外，还需要进行传播工作，让 AI/ML 社区的专家参与进来 开发用于自动分割模型和 TCP/NTCP 预测的 AI/ML 方法。为此，我们计划 实现三个具体目标。通过我们的第一个具体目标，我们计划策划我们的系列多参数， 用于治疗反应和 TCP 的多时间点 MRI 数据集（附带提取的放射组学） 通过组建由三名医生组成的团队来获得真实的分割图像来进行预测。我们 进一步计划将精选的分割图像作为数据集存放到癌症影像档案馆 (TCIA)。作为 我们的第二个具体目标是，我们计划策划和公开沉积匹配的图像剂量多时间点 急性和晚期毒性指标将分发给 AI/ML 专家以进行 NTCP 建模。我们将 特别包括患者报告的 MD 安德森症状清单 - 头颈 (MDASI-HN) 毒性 结果、常见毒性标准 - 不良事件 (CTC-AE) 医生分级毒性和客观 吞咽功能障碍的测量，例如改良的钡剂吞咽和管饲评估。在 第三个具体目标，我们计划设计并执行针对连续图像剂量的公共众包挑战- TCP 和 NTCP 预测建模任务的响应预测。基于我们计划的测试数据集 为了在挑战执行后发布，我们将对提交的挑战进行后分析 模型（例如，假阳性和假阴性案例），并将最佳结果作为手稿传播 提交出版物和演示。如果成功，拟议的努力将直接响应 需要将人工智能/机器学习就绪的数据集用于癌症治疗。