Precision imaging for risk stratification and personalized therapy of oropharyngeal cancer

口咽癌风险分层和个性化治疗的精准成像

• 批准号: 10659176
• 负责人: Quynh-Thu Xuan Le
• 金额: $ 60.15万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-05 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10659176
• 关键词: Address; Anatomy; Architecture; Attention; Biological; Biological Factors; Clinical; Clinical Data; Clinical Trials; Complement; Data; Data Set; Detection; Diagnosis; Disease; Disease Progression; Dissection; Distant; Distant Metastasis; Eligibility Determination; Epidemic; Evaluation; Exhibits; Future; Gene Expression; Goals; HPV oropharyngeal cancer; Habitats; Heterogeneity; Histologic; Histology; Human Papillomavirus; Human papilloma virus infection; Image; Image Analysis; Incidence; Individual; Institution; Invaded; Lymph Node Involvement; Malignant Neoplasms; Modality; Modeling; Molecular; Morphology; Neoplasm Metastasis; Oncology; Outcome; Pathology; Pathway interactions; Patient Selection; Patients; Pattern; Performance; Performance Status; Phenotype; Primary Neoplasm; Prognosis; Prognostic Factor; Quality of life; Radiation Therapy Oncology Group; Radiogenomics; Reduce health disparities; Regional Disease; Relapse; Resource-limited setting; Risk; Smoking History; Stream; Testing; Texture; Therapeutic; Tissues; Toxic effect; Training; Underserved Population; Validation; Variant; X-Ray Computed Tomography; chemoradiation; clinical prognostic; cohort; deep learning; deep learning model; design; disorder risk; draining lymph node; imaging biomarker; improved; improved outcome; innovation; knowledge base; malignant oropharynx neoplasm; multimodal data; multitask; neglect; novel; oral HPV infection; oral lesion; overtreatment; participant enrollment; personalized medicine; personalized predictions; precision medicine; preservation; prognostic; prognostic assays; prognostic model; prognostic signature; prognostic value; prospective; prospective test; quantitative imaging; radiological imaging; radiomics; randomized trial; randomized, clinical trials; response; risk prediction; risk stratification; standard care; success; therapy outcome; therapy resistant; treatment response; treatment strategy; trial enrollment; tumor; tumor heterogeneity; virtual

ABSTRACT During the past few decades, there has been a rapid increase in the incidence of oropharyngeal cancer (OPC), which is attributable to the epidemic of oral human papillomavirus (HPV) infection. Patients with HPV-positive OPC respond well to concurrent chemoradiotherapy and have a more favorable prognosis than HPV-negative patients. However, standard treatment is associated with significant toxicity and likely represents over-treatment for many patients with HPV-positive disease. Several randomized clinical trials have tested novel deintensification strategies with the goal to reduce toxicity and improve patients’ quality of life while preserving the high cure rate. These trials enroll patients based on cancer stage and smoking history. However, current clinical prognostic factors are rather crude and do not accurately predict disease progression on an individual level. Reliable prognostic models are critically needed for personalized risk-adaptive therapy of OPC. To address this unmet need, we propose quantitative CT features to characterize intratumoral spatial heterogeneity and disease invasion/spread, which are known drivers of treatment resistance and disease progression. In addition to knowledge-based image features, we will develop complementary data-driven deep learning models to predict disease progression by using a retrospective multi- institutional dataset of 1771 patients. Further, we will integrate imaging with clinical data to improve prediction and establish their validity by rigorous prospective validation in 1780 patients enrolled in 3 randomized clinical trials. Finally, we will employ a radiogenomic approach to elucidate biological basis of the imaging signatures. If successful, the proposed models will allow more accurate prediction of prognosis and improve risk stratification of OPC. This has significant therapeutic implications by optimizing the selection of patients for treatment deintensification, which will increase the likelihood of success of future clinical trials and pave the way for precision medicine in OPC. Because the information is derived from standard CT scans, this would be readily integrated into current clinical workflow, widely applicable to underserved populations in low-resource settings, and therefore would help reduce health disparity in the US.

抽象的 在过去的几十年中，口咽事件迅速增加 癌症（OPC），这归因于口腔乳头瘤病毒（HPV）感染的流行。 HPV阳性OPC患者对同时进行的化学放疗的反应良好，并且有更多 比HPV阴性患者有利的预后。但是，标准治疗与 对于许多HPV阳性疾病的患者而言，明显的毒性，可能代表过度治疗。 几项随机临床试验已经测试了新型的去敏化策略，其目标是 降低毒性并改善患者的生活质量，同时保留高疗法。这些试验 根据癌症阶段和吸烟史注册患者。但是，目前的临床预后 因素很粗糙，无法准确预测个体水平的疾病进展。 对于OPC的个性化风险自适应疗法，非常需要可靠的预后模型。到 满足这种未满足的需求，我们提出定量的CT特征来表征肿瘤内空间 异质性和疾病侵袭/蔓延，这是耐药性的已知驱动因素和 疾病进展。除了基于知识的图像功能，我们还将开发完整 数据驱动的深度学习模型，通过使用回顾性多种多样来预测疾病进展 1771名患者的机构数据集。此外，我们将将成像与临床数据集成以改进 在1780名招收的患者中，通过严格的前瞻性验证来预测并确定其有效性 随机临床试验。最后，我们将采用一种放射基因组方法来阐明生物学 成像签名的基础。如果成功，建议的模型将允许更准确 预测预测并改善OPC的风险分层。这具有重要的治疗性 通过优化患者的治疗去敏化的选择，这将增加，这将增加 未来的临床试验成功的可能性，并为OPC中的精密医学铺平了道路。 由于信息来自标准CT扫描，因此很容易将其集成到 当前的临床工作流程，广泛适用于低资源环境中服务不足的人群 因此，将有助于减少美国的健康差异。