Combined Imaging and RNA Analyses to Predict Head and Neck Cancer Recurrence

结合成像和 RNA 分析来预测头颈癌复发

• 批准号: 10909477
• 负责人: Hua Li
• 金额: $ 67.31万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-04 至 2024-09-03
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10909477
• 关键词: Address; Behavior; Belief; Biological; Biological Markers; Cellular Structures; Characteristics; Clinical; Clinical Treatment; Computer Models; Data; Decision Making; Distant; Failure; Future; Goals; Head and Neck Cancer; Head and Neck Squamous Cell Carcinoma; Heterogeneity; Human; Human Papillomavirus; Image; Institution; Learning; Magnetic Resonance Imaging; Methods; Microscopic; Modality; Modeling; Monitor; Nature; Neoplasm Metastasis; Network-based; Operative Surgical Procedures; Organism; Oropharyngeal; Outcome; Patient Care; Patients; Performance; Play; Population; Positron-Emission Tomography; Prediction of Response to Therapy; Prognosis; Prognostic Marker; RNA; Radiation therapy; Recurrence; Recurrent Malignant Neoplasm; Recurrent tumor; Reporting; Retrospective Studies; Role; Solid; Survival Rate; Techniques; Testing; Therapeutic; Tissues; Training; Treatment Failure; Treatment outcome; Uncertainty; Work; X-Ray Computed Tomography; biomarker selection; biomarker signature; cancer recurrence; chemotherapy; clinical application; clinical decision-making; clinically relevant; cohort; deep learning; generative adversarial network; histological image; imaging biomarker; improved; individual patient; malignant mouth neoplasm; multimodal data; multimodality; neural network; neural network classifier; patient stratification; patient variability; personalized care; personalized medicine; predictive marker; prognostic; prognostic model; prognostic value; radiological imaging; response; serial imaging; theories; tool; treatment response; treatment strategy; tumor; tumor progression

Title: Combined Imaging and RNA Analyses to Predict Head and Neck Cancer Recurrence ABSTRACT Head and neck squamous cell carcinomas (HNSCCs) encompass a diverse group of tumors that generally are aggressive in their biological behavior. Recurrence is the most common form of treatment failure for HNSCC patients receiving standard therapy. Approximately 50% of HNSCC cases will develop recurrence, and the 5- year survival rate for recurrent patients is only 16~36%. Early prediction of HNSCC recurrence is one of the most challenging yet important tasks for stratifying HNSCC patients and supporting personalized treatment strategies to improve patient care. We and others have shown that human ribonucleic acids (RNAs) and human papillomavirus (HPV) RNAs are promising biomarkers and play critical regulatory roles in HNSCC. Radiologic imaging biomarkers derived from PET, CT, and MR imaging data have shown promise in stratifying patients with favorable and unfavorable prediction for treatment response. Their non-invasive characteristics also allow for convenient and longitudinal monitoring of tumor progression and heterogeneous response during the treatment course. Histologic images provide key information about microscopic structure of cells and tissues of organisms. Recent reports and our preliminary studies have shown that histologic imaging biomarkers, can aid in clinical decision-making by identifying metastases, subtyping and grading tumors, and predicting treatment failures. Clinicopathologic biomarkers show prognostic values through retrospective studies. Still, many HNSCC patients have recurred tumors despite favorable prediction by these biomarkers. The major goal of this study is to develop a comprehensive and robust computational model for early prediction of HNSCC treatment failures leading to tumor recurrence. We will integrate our recently developed advanced learning-based techniques to build prognostic models using about 1,200 patient cases collected from two institutions. The prognostic model will form a solid basis for individualized care of HNSCC patients based on predicted treatment outcomes. Moreover, our work is expected to discover the correlations among multimodal data, leading to dynamic patient stratification to support adaptive treatment strategies.

联合成像和RNA分析预测头颈癌复发 摘要 头颈部鳞状细胞癌（HNSCC）包括一组不同的肿瘤，其通常是恶性的。 在生物学行为上具有攻击性。复发是HNSCC最常见的治疗失败形式 接受标准治疗的患者。大约50%的HNSCC病例会复发，5- 复发患者的年生存率仅为16~ 36%。HNSCC复发的早期预测是最重要的 对HNSCC患者进行分层并支持个性化治疗策略的挑战性但重要的任务 来改善病人护理我们和其他人已经表明，人类核糖核酸（RNA）和人类 乳头瘤病毒（HPV）RNA是有前途的生物标志物，在HNSCC中起关键的调节作用。放射学 来自PET、CT和MR成像数据的成像生物标志物显示出对患者进行分层的前景 对治疗反应有有利和不利的预测。它们的非侵入性特征也允许 为了方便和纵向监测肿瘤进展和治疗期间的异质性反应， 治疗过程。组织学图像提供了有关细胞和组织的显微结构的关键信息， 有机体最近的报告和我们的初步研究表明，组织学成像生物标志物， 通过识别转移、肿瘤亚型和分级以及预测治疗，帮助临床决策 失败临床病理学生物标志物通过回顾性研究显示预后价值。不过许多 尽管这些生物标志物具有良好的预测，但HNSCC患者具有复发的肿瘤。 本研究的主要目标是开发一个全面和强大的计算模型， 预测HNSCC治疗失败导致肿瘤复发。我们将整合我们最近开发的 先进的基于学习的技术，使用从以下医院收集的约1，200例患者病例建立预后模型： 两个机构。该预后模型将为HNSCC患者的个体化护理奠定坚实的基础， 预测治疗结果。此外，我们的工作有望发现多模态之间的相关性， 数据，导致动态患者分层，以支持适应性治疗策略。