Deep learning in cervical cancer radiogenomics

宫颈癌放射基因组学中的深度学习

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要该提案的总体目标是优化放射组学和基因组数据的使用来开发生物标志物做出改变癌症患者管理的临床预测。虽然需要这样的预测生物标志物在各种癌症类型中都很明显，我们的建议重点关注特别普遍且具有破坏性的癌症复发性局部晚期宫颈癌（LACC）的情况。宫颈癌仍居第三位女性常见癌症诊断，局部晚期疾病治疗失败率为 30-50% 放化疗。迫切需要识别有治疗失败风险的患者，以便个性化治疗，包括修改放化疗方案、早期升级治疗和临床治疗试招生。为了开发 LACC 复发的放射基因组生物标志物，该提案解决了三个问题突出的方法学需求：癌症亚型的基因表达数据的可用性有限，噪声和冗余的成像特征数据，以及缺乏疾病信息、可解释的组学整合，每个并针对其自身的具体目标进行了阐述。目标 1 将使用生成对抗网络 (GAN) 来增强小型网络所有高危 HPV 亚型的基因表达数据集。目标 2 将使用深度卷积自动编码器（CAE）。目标 3 将通过结构整合放射基因组特征方程建模 (SEM) 方法将 HPV 特异性致癌机制作为潜在变量。我们共同期望这些目标的实现将创建一个优化的复发生物标志物，该标志物将超越 - 执行其他预测方式以及标准护理随访成像。超出具体 HPV 驱动的恶性肿瘤的应用，我们的建议将产生新的工具和方法来整合任何高维放射基因组数据与假设驱动的研究结果可改善癌症预测。