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

项目摘要。该项目的目标是开发、优化和评估人工智能(AI)- 驱动的医疗成像平台，利用计算机断层扫描(CT)成像来识别是否存在 头颈部鳞状细胞癌的结外侵犯(ENE)HNSCC是一种令人衰弱的 与疾病本身及其管理有关的与患者相关的显著发病率的疾病，即 复杂，包括手术、放射和化疗的组合。决定的一个关键因素 适当的HNSCC治疗是ENE的存在，当肿瘤通过包膜渗透时就会发生 将受累的淋巴结转移到周围组织。烯既是一个重要的预后因素，也是一种 手术后化疗加放疗的辅助治疗升级指征。 这种“三联疗法”是有问题的，因为它与增加与治疗有关的发病率和 医疗费用，但与单独的前期放化疗相比，在疾病控制方面没有改善。这个 挑战是ENE只能在手术后和预处理后明确的病理诊断。 放射学识别ENE已被证明即使是专家诊断也不可靠，导致高发病率 三联疗法和次优治疗结果。在HNSCC管理中，迫切需要 改进了前期ENE识别，以1)选择合适的患者进行手术，以避免过度 三联疗法的发病率和费用，2)对患者进行最优风险分层，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，机器的一个子类型 在人工智能的保护伞下，学习已经在计算机化的医学图像分析方面产生了突破， 时间超过了人类专家，发现了肉眼看不到的模式。当人工智能准备好 改变癌症成像和个性化癌症护理领域，临床上仍然存在重大障碍 实施。本项目的假设是，人工智能可以成功地识别HNSCC ENE 回顾和预期患者队列中的前处理成像，并开发淋巴检测平台 节点自动分割，将促进该平台的临床实用性。 这一假设将通过对深度学习ENE识别的严格优化和评估来验证 站台。具体地说，将验证该平台的准确性、敏感度、特异性和区分性 在两个不同种类的回溯数据集和两个来自 针对HNSCC患者的机构和国家II期临床试验。然后将对平台进行直接比较 与头部和颈部放射科医生一起确定是否可以使用人工智能来增强放射科医生的表现。同时， 人工智能将被用来开发一个肿瘤和淋巴结的自动分割平台，这将1)改进 该平台的临床影响和2)为治疗计划和未来的基于影像的治疗提供了有价值的工具 针对HNSCC患者的研究。 1

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