Optimization and Validation of a Cost-effective Image-Guided Automated Extracapsular Extension Detection Framework through Interpretable Machine Learning in Head and Neck Cancer

通过可解释的机器学习在头颈癌中优化和验证具有成本效益的图像引导自动囊外扩展检测框架

• 批准号: 10648372
• 负责人: William Neil Duggar
• 金额: $ 15.97万
• 依托单位: MISSISSIPPI STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10648372
• 关键词: Adopted; Affect; Algorithms; Ally; American Joint Committee on Cancer; Anatomy; Area; Artificial Intelligence; Bioinformatics; Biological Markers; Cancer Biology; Cessation of life; Clinic; Clinical; Clinical Markers; Clinical Pathology; Communication; Computational Science; Consumption; Craniofacial Abnormalities; Data; Data Science; Data Set; Dental; Detection; Development; Diagnosis; Diagnostic Neoplasm Staging; Disease Progression; Evaluation; Extracapsular; Extramural Activities; Fibrosis; Funding; Future; Goals; HPV analysis; Head and Neck Cancer; Head and Neck Squamous Cell Carcinoma; Health Care Costs; Human Papillomavirus; Image; Image Analysis; Knowledge; Lymphedema; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Manuals; Medical Imaging; Mission; Modality; Modeling; National Institute of Dental and Craniofacial Research; Neck Dissection; Operative Surgical Procedures; Oral; Organ; Outcome; PET/CT scan; Pathologic; Pathological Staging; Pathology; Patients; Positron-Emission Tomography; Postoperative Period; Precision therapeutics; Process; Prognosis; Prognostic Marker; Publishing; Radiation therapy; Radiology Specialty; Research; Research Design; Research Project Grants; Role; Salivary; Security; Site; Staging; Staging System; Techniques; Testing; The Cancer Imaging Archive; Time; Toxic effect; Translational Research; Treatment-related toxicity; Trust; Validation; Work; X-Ray Computed Tomography; cancer biomarkers; chemoradiation; clinical diagnostics; clinical implementation; comparative; cost effective; craniofacial; deep learning; diagnostic tool; disease diagnosis; head and neck cancer patient; high risk; image guided; improved; improved outcome; interest; large datasets; lymph nodes; machine learning algorithm; machine learning method; multimodality; novel; precision medicine; prediction algorithm; programs; success; survival outcome; targeted treatment; tool; treatment planning; tumor; validation studies

Project Summary Squamous cell carcinoma of the head and neck (SCCHN) is the 6th commonest cancer in the world, leading to >300,000 deaths annually worldwide. The extracapsular extension (ECE) of the tumor in the lymph nodes is a significantly high-risk feature in head and neck cancers. Several studies demonstrate that ECE results in worse survival outcomes. Pathologic confirmation, such as neck dissection, is currently required as the gold standard for clinical identification of ECE. However, if it is ECE positive, postoperative chemoradiotherapy has to be considered. As a result, neck dissection followed by chemoradiation increases the toxicity, especially late toxicity such as fibrosis, and lymphedema can get worse due to the late toxicity. If we can detect ECE during preoperative evaluation, we can select those patients for chemoradiation before surgery. Thus, predicting ECE becomes a piece of critical information for clinicians planning treatment. The biggest obstacles to adopting AI/ML algorithms in the clinic are concerns about security, reliability, and transparency. If an algorithm could be developed to not only provide an accurate prediction of ECE (and/or clinical staging), but also to provide transparent and clinically understandable communication of how the predictive conclusion was reached, then clinicians would more readily adopt a tool utilizing that algorithm to be an ally. The purpose of this proposal is to optimize and pathologically validate AI/ML approaches for prognoses and diagnoses of ECE from medical images on head and neck cancer patients, which could be further implemented as a tool for diagnostic assistance and precision medicine. This translational research project focuses on filling the gap of AI/ML transparency and interpretability in the automated detection of ECE in head and neck cancers. In addition, our interpretable ML algorithm will not require the pre-annotated lymph node areas, which is a quite cost-effective technique by eliminating the time-consuming lymph node annotation process. We propose the following aims: 1) Validate and interpret the image-based ECE diagnosis model and its association with head and neck cancer anatomic organ sites and HPV status. 2) Optimize the cost-effective image-based ECE diagnosis model considering clinical markers within pathology, PETCT, and MRI results for clinical implementation. We will validate our model based on the dataset collected from both our team and existing data collected from The Cancer Image Archive. This proposal aligns with the mission of Oral & Salivary Cancer Biology Program and the NIDCR Special Interest in Supporting Dental, Oral, and Craniofacial Research Using Bioinformatic, Computational, and Data Science Approaches (NOT-DE-20-006). This study will provide the preliminary results of our future research on the precision treatment of high-risk head and neck cancer patients. We plan to submit an NIDCR R01 proposal in Year 2 for the precision treatment of high-risk head and neck cancer.

项目摘要 头颈部鳞状细胞癌（SCCHN）是世界上第六个最常见的癌症， 每年在全球范围内导致300,000人死亡。肿瘤的囊外延伸（ECE） 淋巴结是头颈癌的显着高危特征。几项研究表明 ECE导致生存结果较差。病理性的确认，例如颈部剖析，是 目前需要作为ECE临床识别的黄金标准。但是，如果它是肯定的， 必须考虑术后化学放疗。结果，颈部剖记随后是 化学疗法会增加毒性，尤其是晚期毒性，例如纤维化，淋巴水肿可以得到 由于毒性晚期，因此更糟。如果我们可以在术前评估期间检测ECE，我们可以选择这些 手术前进行化学放疗的患者。因此，预测ECE成为一件关键 临床医生计划治疗的信息。采用AI/ML算法的最大障碍 诊所是对安全性，可靠性和透明度的担忧。如果可以开发算法 不仅提供了ECE（和/或临床分期）的准确预测，而且还提供透明的 以及如何得出预测性结论的临床可理解的交流，然后 临床医生会更容易采用该算法成为盟友的工具。这个目的 建议是优化和病理验证AI/ML方法的预后和诊断 来自头颈癌患者的医学图像的ECE，可以进一步实施 诊断辅助和精确医学的工具。这个转化研究项目的重点 在头部自动检测中填补AI/ML透明度和可解释性的空白 和脖子癌。此外，我们可解释的ML算法将不需要预先注销的淋巴 节点区域，这是一种通过消除耗时的淋巴结，这是一种相当成本效益的技术 注释过程。我们提出以下目的：1）验证和解释基于图像的ECE 诊断模型及其与头颈癌解剖器官部位和HPV状态的关联。 2）优化基于图像的基于图像的ECE诊断模型，考虑临床标记 病理，PETCT和MRI临床实施结果。我们将根据 从我们的团队和从癌症图像档案中收集的现有数据收集的数据集。这 提案与口腔和唾液癌生物学计划和NIDCR特别的任务保持一致 有兴趣使用生物信息，计算和 数据科学方法（非DE-20-006）。这项研究将提供我们的初步结果 关于高危头颈癌患者精确治疗的未来研究。我们计划 在第2年提交NIDCR R01提案，以对高危头颈癌进行精确治疗。