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: 10540326
• 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/10540326
• 关键词: Adjuvant Therapy; 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; Infiltration; Institution; Lead; Lymph Node Involvement; Machine Learning; Malignant Neoplasms; Manuals; Maps; Medical Imaging; Modality; 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; Specificity; Testing; Time; Tissues; Training; Translating; Treatment outcome; Work; X-Ray Computed Tomography; automated segmentation; cancer imaging; capsule; chemoradiation; chemotherapy; clinical implementation; cohort; comparison control; computerized; cost; deep learning; design; disorder control; draining lymph node; 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; usability

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，其利用计算机断层扫描（CT）成像来识别 头颈部鳞状细胞癌（HNSCC）的淋巴结转移（ENE）。HNSCC是一种使人衰弱的 具有与疾病本身及其管理相关的显著患者相关发病率的疾病， 复杂，包括手术、放疗和化疗的组合。一个决定性的关键因素 适当的HNSCC处理是存在ENE，当肿瘤浸润穿过包膜时发生ENE 转移到周围组织中ENE既是一个重要的预后因素， 手术后在放疗基础上增加化疗的辅助治疗升级适应症。 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， 医疗费用，但与单独的前期放化疗相比，疾病控制没有改善。的 挑战是ENE只能在手术后和治疗前进行明确的病理诊断， 放射学ENE识别已被证明即使对于专家诊断医生也是不可靠的，导致高比率的 三联疗法和次优治疗结果。在HNSCC管理中，迫切需要 改善治疗前ENE识别，以1）选择合适的患者进行手术，以避免过量 三联疗法的发病率和成本，2）最佳地对患者进行风险分层，3）选择合适的患者 用于治疗降级或强化临床试验。近年来，深度学习，机器学习的一个子类型， 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， 超过人类专家的能力，发现肉眼看不到的模式。虽然AI准备 尽管癌症成像和个性化癌症护理领域的变革仍然存在重大的临床障碍， 实施.该项目的假设是，AI可以用于成功识别HNSCC ENE， 在回顾性和前瞻性患者队列中进行治疗前成像，并开发淋巴结转移的平台。 节点自动分割，这将促进平台的临床实用性。 这一假设将通过严格优化和评估深度学习ENE识别来进行测试 平台具体而言，将验证平台的准确性、灵敏度、特异性和区分性。 两个异质回顾性数据集和两个前瞻性队列的性能， HNSCC患者的机构和国家II期临床试验。然后平台会直接比较 与头颈部放射科医生合作，以确定放射科医生的表现是否可以通过人工智能来增强。同时， AI将用于开发肿瘤和淋巴结的自动分割平台，这将1）改善 该平台的临床影响和2）为治疗计划和未来基于成像的 HNSCC患者的研究。 1