Radiogenomics framework for non-invasive personalized medicine

非侵入性个性化医疗的放射基因组学框架

基本信息

批准号：
10005534
负责人：
Olivier Gevaert
金额：
$ 44.4万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-02-15 至 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10005534
关键词：
3-Dimensional Algorithms Area Biological Assay Biological Markers Brain Neoplasms Cancer Patient Clinical Computer Simulation Computer Vision Systems Data Data Sources Development Diagnostic Diagnostic Imaging Diffusion Drug Targeting Drug usage Epidermal Growth Factor Receptor Eye Gene Expression Gene Expression Profile Genes Genome Genomics Glioblastoma Goals Head and Neck Cancer High-Throughput Nucleotide Sequencing Human Image Image Analysis Individual Informatics Investigation Lesion Letters Link Magnetic Resonance Imaging Malignant Neoplasms Malignant neoplasm of lung Manuals Maps Medical Imaging Medical center Methods Modeling Molecular Molecular Profiling Monitor Outcome Pathway interactions Patients Pattern Perfusion Pharmaceutical Preparations Positron-Emission Tomography Prediction of Response to Therapy Primary carcinoma of the liver cells Process Prognostic Marker Property Radiogenomics Rectal Cancer Research Resistance Retrospective cohort Shapes Site Somatic Mutation Squamous cell carcinoma Technology Texture Therapeutic Time Translating Translations Treatment outcome Tumor Tissue Work X-Ray Computed Tomography actionable mutation base cancer site clinical application clinical care cohort follow-up genome analysis genome sequencing human data imaging biomarker imaging modality imaging study improved outcome in vivo interest malignant breast neoplasm molecular imaging multimodality multiple omics mutational status novel therapeutics outcome forecast outcome prediction personalized medicine precision medicine precision oncology predict clinical outcome predictive signature quantitative imaging radiologist radiomics response supervised learning synergism tool transcriptome sequencing treatment choice treatment response tumor

项目摘要

Project summary Radiogenomics, is a burgeoning area of research that aims to link medical imaging with multi-omics molecular profiles of the same patients. Radiogenoimcs has shown its potential through its ability to predict clinical outcomes e.g. prognosis, and through predicting actionable molecular properties of tumors, e.g. the activity of EGFR, a major drug target in many cancers. The typical imaging genomics workflow consists of the following steps: (1) Identify the tumor through segmentation. This is often also defined as identifying Regions Of Interests or ROIs through a manual process with a radiologist or using computer vision algorithms. (2) Feature extraction, often also known as radiomics, whereby 100s of features are identified that capture the shape, the texture and the intensity distributions of lesions in 2D or 3D. (3) Supervised machine learning to predict clinical outcomes such as prognosis, overall survival or response to treatment, or predicting molecular profiles such as gene expression patterns or metagenes, or individual molecular properties such as the mutation status of a gene (e.g. EGFR). This workflow has been demonstrated in several cancers including lung cancer, brain tumors, hepatocellular carcinoma, breast cancer etc. Current radiogenomics applications are limited to study associations between imaging and molecular data, and predicting long term outcomes. However, no actionable information is gained from radiogenomics maps. In this renewal, we propose to develop a radiogenomics framework to support treatment response, treatment allocation and treatment monitoring: (1) we will develop informatics algorithms that integrate radiogenomic data for treatment response, (2) algorithms that allow combining radiogenomic data during treatment follow-up, and (3) algorithms that use the radiogenomic map to suggest novel drugs and predict drug target activities. Combining these complementary data sources in a radiogenomics framework for data fusion can have profound contributions toward predicting treatment outcomes by uncovering unknown synergies and relationships. More specifically, developing computational models integrating quantitative image features and molecular data to develop radiogenomics signatures, holds the potential to translate in benefit to tumor patients by investigating biomarkers that accurately predict therapy response of tumors. Readily, because medical imaging is part of the routine diagnostic work-up of cancer patients and molecular data of human tumors is increasingly being used in clinical workflows, therefore if reliable radiogenomic signatures can be found reflecting treatment response, translation to the clinical applications is feasible.

项目概要放射基因组学是一个新兴的研究领域，旨在将医学成像与多组学分子联系起来同一患者的个人资料。放射基因学通过其预测临床的能力显示了其潜力结果例如预后，并通过预测肿瘤的可操作分子特性，例如的活动 EGFR，许多癌症的主要药物靶点。典型的成像基因组学工作流程包括以下步骤：（1）通过以下方式识别肿瘤：分割。这通常也被定义为通过手动过程识别感兴趣区域或投资回报率与放射科医生或使用计算机视觉算法。 (2)特征提取，通常也称为放射组学，由此识别出数百个特征，这些特征捕获了形状、纹理和强度分布 2D 或 3D 病变。 (3) 有监督的机器学习来预测临床结果，例如预后、总体情况生存或对治疗的反应，或预测分子概况，例如基因表达模式或元基因，或个体分子特性，例如基因的突变状态（例如 EGFR）。这工作流程已在多种癌症中得到证实，包括肺癌、脑肿瘤、肝细胞癌癌、乳腺癌等当前的放射基因组学应用仅限于研究成像和分子数据之间的关联，并且预测长期结果。然而，从放射基因组学图谱中没有获得可操作的信息。在这个更新，我们建议开发一个放射基因组学框架来支持治疗反应、治疗分配和治疗监测：（1）我们将开发整合放射基因组数据的信息学算法对于治疗反应，(2) 允许在治疗随访期间结合放射基因组数据的算法，以及 (3)利用放射基因组图谱推荐新药并预测药物靶点活性的算法。将这些互补的数据源结合在放射基因组学框架中进行数据融合可以具有通过发现未知的协同作用对预测治疗结果做出了深远的贡献关系。更具体地说，开发集成定量图像特征和用于开发放射基因组学特征的分子数据有可能为肿瘤患者带来益处通过研究准确预测肿瘤治疗反应的生物标志物。很容易，因为医疗成像是癌症患者常规诊断检查的一部分，人类肿瘤的分子数据是越来越多地在临床工作流程中使用，因此如果可以找到可靠的放射基因组特征反映治疗反应，转化为临床应用是可行的。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Shallow Convolutional Neural Network Predicts Prognosis of Lung Cancer Patients in Multi-Institutional CT-Image Data.

DOI：
10.1038/s42256-020-0173-6
发表时间：
2020-05
期刊：
Nature machine intelligence
影响因子：
23.8
作者：
Mukherjee P;Zhou M;Lee E;Schicht A;Balagurunathan Y;Napel S;Gillies R;Wong S;Thieme A;Leung A;Gevaert O
通讯作者：
Gevaert O

Topological image modification for object detection and topological image processing of skin lesions.

DOI：
10.1038/s41598-020-77933-y
发表时间：
2020-12-03
期刊：
Scientific reports
影响因子：
4.6
作者：
Vandaele R;Nervo GA;Gevaert O
通讯作者：
Gevaert O

Structuring clinical text with AI: Old versus new natural language processing techniques evaluated on eight common cardiovascular diseases.

DOI：
10.1016/j.patter.2021.100289
发表时间：
2021-07-09
期刊：
Patterns (New York, N.Y.)
影响因子：
0
作者：
Zhan X;Humbert-Droz M;Mukherjee P;Gevaert O
通讯作者：
Gevaert O

A meta-learning approach for genomic survival analysis.

DOI：
10.1038/s41467-020-20167-3
发表时间：
2020-12-11
期刊：
Nature communications
影响因子：
16.6
作者：
Qiu YL;Zheng H;Devos A;Selby H;Gevaert O
通讯作者：
Gevaert O

AI-based analysis of CT images for rapid triage of COVID-19 patients.

DOI：
10.1038/s41746-021-00446-z
发表时间：
2021-04-22
期刊：
NPJ digital medicine
影响因子：
15.2
作者：
Xu Q;Zhan X;Zhou Z;Li Y;Xie P;Zhang S;Li X;Yu Y;Zhou C;Zhang L;Gevaert O;Lu G
通讯作者：
Lu G