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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

基本信息

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
项目类别：
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

项目摘要

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.

摘要