Development of a prototype software for automated PET/CT interpretation and reporting in thoracic cancer

开发用于胸癌自动 PET/CT 判读和报告的原型软件

• 批准号: 10076938
• 负责人: JAYARAM K UDUPA
• 金额: $ 25.21万
• 依托单位: QUANTITATIVE RADIOLOGY SOLUTIONS, LLC
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10076938
• 关键词: Abbreviations; Abdomen; Algorithms; Anatomy; Artificial Intelligence; Body Burden; Body Regions; Cancer Patient; Chest; Clinical; Communication; Computer software; Conduct Clinical Trials; Data; Data Set; Development; Diagnosis; Discipline of Nuclear Medicine; Disease; Distant; Fostering; Geography; Glycolysis; Goals; Head and neck structure; Health; Image; Image Analysis; Insurance Carriers; Knowledge; Lead; Lesion; Machine Learning; Malignant Neoplasms; Malignant neoplasm of thorax; Manuals; Measurement; Measures; Medical Imaging; Medicare; Methodology; Methods; Mining; Modality; Modeling; Monitor; Names; Nodal; Normalcy; Oncologist; Organ; PET/CT scan; Pathologic; Patient-Focused Outcomes; Patients; Pelvis; Pharmacologic Substance; Phase; Physicians; Population; Positron-Emission Tomography; Privatization; Production; Property; Public Health; Radiology Specialty; Reporting; Research Personnel; Sampling Biases; Scanning; Small Business Technology Transfer Research; Staging; Standardization; Technology; Testing; Therapeutic; Training; X-Ray Computed Tomography; algorithm training; anticancer research; base; burden of illness; cancer diagnosis; cancer type; deep learning; deep learning algorithm; evidence based guidelines; fluorodeoxyglucose positron emission tomography; imaging modality; improved; innovation; interest; learning strategy; lymph nodes; lymphoid organ; model building; novel; object recognition; outcome forecast; payment; precision medicine; prototype; radiologist; response; software development; treatment response; uptake; whole body imaging

Abstract. In cancer, body-wide FDG-PET/CT is a prime modality for diagnosis, staging, and treatment assessment. Despite its paramount importance to enable precision medicine in cancer, no method is currently available for automated disease burden estimation and standardized reporting on PET/CT images regionally and globally in anatomic organs and lymph node zones within a body region or body-wide. Automated production- mode body-wide/ body-region-wide disease measurement with standardized reporting will foster cancer research discovery and will be of great interest to oncologists, radiologists/ nuclear medicine physicians, Medicare and private health insurers, and pharmaceutical companies that conduct clinical trials of new cancer therapeutics and currently rely on manual methods of response assessment. The overarching goal of this Phase I project is, therefore, to develop, validate, and demonstrate a prototype software for disease measurement and reporting via FDG-PET/CT in the above manner in one body region, namely thorax, based on innovative algorithms that are generalizable body-wide. The project has two aims: Aim 1: Develop, implement, and validate algorithms for disease burden estimation in thoracic cancer via FDG-PET/CT. Aim 2: Develop and demonstrate a prototype software implementing the above algorithms for disease measurement and reporting. Aim 1 will be accomplished in 3 stages: Tasks 1, 2: PET/CT image data sets which are radiologically near normal for the thoracic body region will be gathered from existing whole-body scans of 100 patients. In these data sets, 7 key anatomic organs and 5 key lymph node zones in the thorax will be delineated under expert guidance. These data will be used to build population fuzzy anatomy models following our established Automatic Anatomy Recognition (AAR) methodology. An additional 100 whole-body PET/CT scans of patients with different types of cancer will be gathered to test our methods. Using available commercial clinical software, the PET uptake properties of lesions in organs and diseased lymph nodes in lymph node zones will be measured manually and used as reference ground truth of disease burden. Task 3: Deep learning (DL) algorithms anatomically guided by AAR will be developed to very accurately localize (but not delineate) organs and lymph node zones in PET/CT images using the models. Task 4: Novel methods based on fuzzy principles will be developed to automatically tag and quantify pathological regions (without explicitly delineating them) within located organs and nodal zones, and the accuracy of disease measurement will be evaluated (Task 5). Aim 2 will be accomplished by incorporating the disease measurement methodology into a prototype software named AAR-DQ (Tasks 6, 7) based on our earlier software platform CAVASS. AAR-DQ will report disease burden in a hierarchical manner – (i) at the body-region level; (ii) at each organ/ lymph node zone level; (ii) at each lesion/ lymph node level. Expected milestones. Aim 1: AAR-DQ disease measurement not to deviate more than 10% from clinical ground truth measurement. Aim 2: Disease measurement/ reporting in under 5 minutes per patient PET/CT study.

抽象的。在癌症中，全身FDG-PET/CT是诊断，分期和治疗的主要方式 评估。尽管对于启用癌症的精确药物至关重要，但目前尚无方法 可用于自动疾病伯恩估计和有关PET/CT图像的标准化报告，并在区域和 全球在身体区域或身体范围内的解剖器官和淋巴结区域。自动生产 - 标准化报告的模式范围内/身体区域范围内的疾病测量将促进癌症 研究发现，肿瘤学家，放射科医生/核医学医师将引起极大的兴趣 医疗保险和私人健康的确保以及进行新癌症临床试验的制药公司 治疗剂，目前依靠手动响应评估方法。总体目标 因此，第一阶段项目是为了开发，验证和演示疾病的原型软件 通过上述方式通过FDG-PET/CT进行测量和报告，在一个身体区域，即胸部，基于胸腔 创新的算法是可概括的整个身体。该项目有两个目标：目标1：开发，实施， 并通过FDG-PET/CT验证胸腔癌中伯恩估计的算法。目标2：发展和 展示了实施上述疾病测量和报告算法的原型软件。 AIM 1将在3个阶段完成：任务1，2：PET/CT图像数据集在放射线上接近正常 对于胸部身体区域，将从现有的100例患者的全身扫描中收集。在这些数据集中 根据专家指导，将划定7个关键的解剖器官和5个胸腔中的5个关键淋巴结区。 这些数据将在我们建立的自动解剖结构之后用于建立种群模糊解剖模型 识别（AAR）方法论。另外100个全身PET/CT扫描患者具有不同类型的患者 将收集癌症来测试我们的方法。使用可用的商业临床软件，宠物吸收 将手动测量器官和淋巴结中淋巴结淋巴结的病变特性 用作伯恩疾病的参考基础真理。任务3：深度学习（DL）算法在解剖学上指导 由AAR开发以非常准确地定位（但不描绘）器官和PET/CT中的淋巴结区域 使用模型的图像。任务4：基于模糊原理的新方法将开发以自动 标记并量化病理区域（不明确描绘它们），内部的器官和节点区域内 并将评估疾病测量的准确性（任务5）。 AIM 2将通过 将疾病测量方法纳入名为AAR-DQ的原型软件（任务6，7） 基于我们较早的软件平台Cavass。 AAR-DQ将以分层的方式报告伯恩疾病 - （i）在身体区域； （ii）在每个器官/淋巴结区域水平； （ii）在每个病变/淋巴结水平上。 预期的里程碑。目标1：AAR-DQ疾病测量不偏离临床地面10％ 真相测量。 AIM 2：每位患者宠物/ CT研究中的5分钟以下的疾病测量/报告。