Development of an artificial intelligence-driven, imaging-based platform for pretreatment identification of extranodal extension in head and neck cancer

开发人工智能驱动、基于成像的平台，用于头颈癌结外扩散的治疗前识别

• 批准号: 10323383
• 负责人: Benjamin Harris Kann
• 金额: $ 16.82万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10323383
• 关键词: Adjuvant; Algorithms; Artificial Intelligence; Artificial Intelligence platform; Biopsy; Clinic; Clinical; Clinical Trials; Complex; Data; Data Set; Decision Making; Detection; Development; Diagnosis; Disease; Enrollment; Evaluation; Extranodal; Eye; Foundations; Future; Geography; Goals; Head; Head and Neck Cancer; Head and Neck Squamous Cell Carcinoma; Head and neck structure; Health Care Costs; Human; Image; Image Analysis; Institution; Lead; Machine Learning; Malignant Neoplasms; Manuals; Maps; Medical Imaging; Morbidity - disease rate; Neck Dissection; Newly Diagnosed; Operative Surgical Procedures; Output; Pathologic; Pathology; Pathway interactions; Patients; Pattern; Performance; Phase; Phase II Clinical Trials; Physicians; Positioning Attribute; Positron-Emission Tomography; Process; Prognostic Factor; Prospective cohort; Radiation; Radiation therapy; Research; Scanning; Scientist; Sensitivity and Specificity; Testing; Time; Tissues; Training; Translating; Treatment outcome; Work; X-Ray Computed Tomography; automated segmentation; base; cancer imaging; capsule; chemoradiation; chemotherapy; clinical implementation; cohort; computerized; cost; deep learning; design; disorder control; effective therapy; heuristics; imaging platform; improved; insight; interest; lymph nodes; neural network; neural network architecture; novel; optimal treatments; patient stratification; personalized cancer care; phase II trial; prediction algorithm; prospective; prospective test; radiological imaging; radiologist; radiomics; risk minimization; risk stratification; side effect; success; therapy development; tool; treatment planning; treatment strategy; tumor

Project Summary. The goal of this project is to develop, optimize, and evaluate an artificial intelligence (AI)- driven, medical imaging platform that utilizes computed tomography (CT) imaging to identify the presence of extranodal extension (ENE) in head and neck squamous cell carcinoma (HNSCC). HNSCC is a debilitating disease with significant patient-related morbidity related to the disease itself and its management, which is complex and consists of a combination of surgery, radiation, and chemotherapy. A key factor in determining proper HNSCC management is the presence of ENE, which occurs when tumor infiltrates through the capsule of an involved lymph node into the surrounding tissue. ENE is both an important prognostic factor and an indication for adjuvant treatment escalation with the addition of chemotherapy to radiation following surgery. This “trimodality therapy” is problematic, as it is associated with increased treatment-related morbidity and healthcare costs, but no improvement in disease control compared to upfront chemoradiation alone. The challenge is that ENE can only be definitively diagnosed pathologically after surgery, and pretreatment radiographic ENE identification has proven unreliable for even expert diagnosticians, leading to high rates of trimodality therapy and suboptimal treatment outcomes. In HNSCC management there is a critical need for improved pretreatment ENE identification to 1) select appropriate patients for surgery to avoid the excess morbidity and costs of trimodality therapy, 2) risk-stratify patients optimally, and 3) select appropriate patients for treatment de-escalation or intensification clinical trials. In recent years, Deep learning, a subtype of machine learning, under the umbrella of AI, has generated breakthroughs in computerized medical image analysis, at times outperforming human experts and discovering patterns hidden to the naked eye. While AI is poised to transform the fields of cancer imaging and personalized cancer care, there remain significant barriers to clinical implementation. The hypothesis of this project is that AI can be used to successfully identify HNSCC ENE on pretreatment imaging in retrospective and prospective patient cohorts and to develop a platform for lymph node auto-segmentation that will promote clinical utility of the platform. This hypothesis will be tested by rigorous optimization and evaluation of a deep learning ENE identification platform. Specifically, the platform will be validated for accuracy, sensitivity, specificity, and discriminatory performance on two heterogeneous retrospective datasets and two prospective cohorts derived from institutional and national Phase II clinical trials for HNSCC patients. The platform will then be directly compared with head and neck radiologists to determine if radiologist performance can be augmented with AI. In parallel, AI will be utilized to develop an auto-segmentation platform for tumor and lymph nodes, which will 1) improve the platform's clinical impact and 2) provide a valuable tool for treatment planning and future imaging-based research for HNSCC patients. 1

项目摘要。该项目的目标是开发、优化和评估人工智能（AI）- 驱动的医学成像平台，利用计算机断层扫描 (CT) 成像来识别是否存在 头颈鳞状细胞癌（HNSCC）的结外扩散（ENE）。 HNSCC 是一种使人衰弱的疾病 与疾病本身及其治疗相关的具有显着患者相关发病率的疾病，即 复杂，由手术、放疗和化疗相结合组成。决定的一个关键因素 正确的 HNSCC 治疗是 ENE 的存在，当肿瘤浸润到被膜时就会出现 ENE 受累淋巴结进入周围组织。 ENE 既是一个重要的预后因素，也是一个 手术后在放疗基础上添加化疗的辅助治疗升级的指征。 这种“三联疗法”是有问题的，因为它与治疗相关的发病率增加有关，并且 医疗费用，但与单独的前期放化疗相比，疾病控制没有改善。这 挑战在于ENE只能在手术后进行病理学明确诊断，并且进行预处理 事实证明，放射照相 ENE 识别即使对于专家诊断人员来说也是不可靠的，导致高发生率 三联治疗和次优治疗结果。在 HNSCC 管理中迫切需要 改进治疗前 ENE 识别，1) 选择合适的患者进行手术，以避免过量 三联治疗的发病率和费用，2) 对患者进行最佳风险分层，3) 选择合适的患者 用于治疗降级或强化临床试验。近年来，机器学习的一个子类型——深度学习 在人工智能的保护下，学习在计算机化医学图像分析方面取得了突破， 其表现超越了人类专家，并发现了肉眼隐藏的模式。虽然人工智能已做好准备 改变癌症成像和个性化癌症护理领域，但临床仍存在重大障碍 执行。该项目的假设是人工智能可用于成功识别 HNSCC ENE 在回顾性和前瞻性患者队列中进行治疗前成像，并开发淋巴平台 节点自动分割将促进该平台的临床实用性。 该假设将通过深度学习 ENE 识别的严格优化和评估来检验 平台。具体来说，该平台将验证准确性、敏感性、特异性和歧视性 两个异质回顾性数据集和两个前瞻性队列的表现 针对 HNSCC 患者的机构和国家 II 期临床试验。然后平台会直接进行比较 与头颈放射科医生一起确定放射科医生的表现是否可以通过人工智能来增强。并联， AI将用于开发肿瘤和淋巴结的自动分割平台，这将1）改善 该平台的临床影响以及 2) 为治疗计划和未来基于成像的提供有价值的工具 HNSCC 患者的研究。 1