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
基本信息
- 批准号:10909477
- 负责人:
- 金额:$ 67.31万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AddressBehaviorBeliefBiologicalBiological MarkersCellular StructuresCharacteristicsClinicalClinical TreatmentComputer ModelsDataDecision MakingDistantFailureFutureGoalsHead and Neck CancerHead and Neck Squamous Cell CarcinomaHeterogeneityHumanHuman PapillomavirusImageInstitutionLearningMagnetic Resonance ImagingMethodsMicroscopicModalityModelingMonitorNatureNeoplasm MetastasisNetwork-basedOperative Surgical ProceduresOrganismOropharyngealOutcomePatient CarePatientsPerformancePlayPopulationPositron-Emission TomographyPrediction of Response to TherapyPrognosisPrognostic MarkerRNARadiation therapyRecurrenceRecurrent Malignant NeoplasmRecurrent tumorReportingRetrospective StudiesRoleSolidSurvival RateTechniquesTestingTherapeuticTissuesTrainingTreatment FailureTreatment outcomeUncertaintyWorkX-Ray Computed Tomographybiomarker selectionbiomarker signaturecancer recurrencechemotherapyclinical applicationclinical decision-makingclinically relevantcohortdeep learninggenerative adversarial networkhistological imageimaging biomarkerimprovedindividual patientmalignant mouth neoplasmmultimodal datamultimodalityneural networkneural network classifierpatient stratificationpatient variabilitypersonalized carepersonalized medicinepredictive markerprognosticprognostic modelprognostic valueradiological imagingresponseserial imagingtheoriestooltreatment responsetreatment strategytumortumor progression
项目摘要
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.
题目:联合成像和RNA分析预测头颈癌复发
项目成果
期刊论文数量(0)
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Hua Li其他文献
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