Deep learning in cervical cancer radiogenomics

宫颈癌放射基因组学中的深度学习

• 批准号: 10424854
• 负责人: Jin Zhang
• 金额: $ 22.09万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-13 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10424854
• 关键词: 3-Dimensional; Accounting; Address; Affect; Biological; Biological Markers; Biology; Biopsy Specimen; Cancer Patient; Cervix Neoplasms; Cessation of life; Characteristics; Clinical; Clinical Data; Complex; Data; Data Reporting; Data Set; Dimensions; Disease; Early Intervention; Equation; Future; Gene Expression; Genes; Genomics; Genotype; Goals; HPV oropharyngeal cancer; HPV-High Risk; Human Papillomavirus; Image; Investigational Therapies; Lead; Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Malignant neoplasm of cervix uteri; Methodology; Methods; Modality; Modeling; Oncogenic; Organoids; Outcome; Pathway interactions; Patient Outcomes Assessments; Patient-Focused Outcomes; Patients; Pattern; Phenotype; Positron-Emission Tomography; Prediction of Response to Therapy; Predictive Value; Radiation Dose Unit; Radiation therapy; Radiogenomics; Reaction Time; Recurrence; Regimen; Research; Risk; Sample Size; Sampling; Structural Genes; Structure; Survival Rate; The Cancer Genome Atlas; Therapy Clinical Trials; Time; Treatment Failure; Treatment outcome; Tumor Bank; Woman; X-Ray Computed Tomography; advanced disease; autoencoder; base; cancer diagnosis; cancer recurrence; cancer subtypes; cancer survival; cancer type; chemoradiation; clinical phenotype; clinical predictors; clinical trial enrollment; clinically relevant; cohort; complex data; deep learning; deep learning model; design; differential expression; epithelial to mesenchymal transition; experience; feature selection; follow-up; generative adversarial network; genomic data; high dimensionality; improved; insight; network models; neural network; novel; patient stratification; personalized medicine; predicting response; predictive marker; predictive modeling; prognostic; prospective; radiation response; radiomics; research clinical testing; risk prediction model; standard of care; stem; tool; treatment planning; treatment response; treatment risk; tumor

PROJECT SUMMARY/ABSTRACT The overall goal of this proposal is to optimize the use of radiomic and genomic data to develop biomarkers which make clinical predictions that change cancer patient management. While the need for such predictive biomarkers is evident across cancer types, we focus our proposal on the particularly prevalent and damaging condition of recurrent, locally-advanced cervical cancer (LACC). Cervical cancer remains the third most common cancer diagnosis of women, and treatment failure for locally-advanced disease is 30-50% following chemoradiation therapy. There is a pressing need to identify patients at risk for treatment failure to allow for personalized treatment including modified chemoradiation regimens, early escalation of therapy, and clinical trial enrollment. To develop radiogenomic biomarkers for LACC recurrence, this proposal addresses three outstanding methodological needs: limited availability of gene expression data for cancer subtypes, noisy and redundant imaging feature data, and lack of disease-informed, interpretable -omics integration, each addressed in its own specific aim. Aim 1 will use generative adversarial networks (GAN) to augment the small gene expression datasets for all high-risk HPV subtypes. Aim 2 will optimize imaging feature selection using a deep convolutional autoencoder (CAE). Aim 3 will integrate radiogenomic features through a structural equation modeling (SEM) approach incorporating HPV-specific oncogenic mechanisms as latent variables. Together, we expect fulfillment of these aims will create an optimized recurrence biomarker which will out- perform other prediction modalities as well as standard-of-care follow-up imaging. Beyond the specific application to HPV-driven malignancies, our proposal will generate novel tools and methods to integrate any high-dimensional radiogenomic data with hypothesis-driven research findings to improve cancer prediction.

项目总结/文摘