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）是世界上第六常见的癌症， 导致全世界每年超过300，000人死亡。肿瘤的囊外延伸（ECE）在 淋巴结是头颈部癌症的显著高危特征。一些研究表明， ECE会导致更差的生存结果。病理证实，如颈淋巴结清扫术， 目前作为ECE临床鉴定的金标准。但是，如果是ECE阳性， 必须考虑术后放化疗。因此，颈淋巴结清扫术后， 放化疗增加了毒性，特别是晚期毒性，如纤维化，并可导致水肿。 由于晚期毒性而恶化。如果我们能在术前评估中检测到ECE，我们可以选择那些 患者在手术前接受放化疗。因此，预测ECE成为一个关键的 为临床医生规划治疗提供信息。采用AI/ML算法的最大障碍 诊所关注的是安全性、可靠性和透明度。如果能开发出一种算法 不仅提供ECE（和/或临床分期）的准确预测，而且提供透明的 以及如何得出预测结论的临床可理解的沟通，然后 临床医生将更容易采用利用该算法的工具作为盟友。这样做的目的 建议是优化和病理学验证AI/ML方法，用于疾病的诊断和治疗。 ECE从头部和颈部癌症患者的医学图像，这可以进一步实施，作为一个 用于诊断辅助和精准医疗的工具。该转化研究项目的重点是 填补了头部ECE自动检测中AI/ML透明度和可解释性的差距 和颈部癌症。此外，我们的可解释ML算法将不需要预先注释的淋巴 淋巴结区域，这是一个非常具有成本效益的技术，通过消除耗时的淋巴结 注释过程。我们提出以下目标：1）理解和解释基于图像的欧洲经委会 诊断模式及其与头颈癌解剖器官部位和HPV状态的相关性。 2)优化基于图像的ECE诊断模型，考虑临床标志物， 用于临床实施的病理学、PETCT和MRI结果。我们将根据 从我们的团队收集的数据集和从癌症图像档案收集的现有数据。这 建议符合口腔和唾液癌生物学计划和NIDCR特别的使命 兴趣支持牙科，口腔，颅面研究使用生物信息学，计算， 数据科学方法（NOT-DE-20-006）。这项研究将提供我们的初步结果， 未来对高危头颈癌患者的精准治疗研究。我们计划 在第二年提交NIDCR R 01提案，用于高风险头颈癌的精确治疗。