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放射组学:医学影像与个性化医疗之间的桥梁。

Radiomics: the bridge between medical imaging and personalized medicine.

机构信息

The D-Lab: Decision Support for Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.

Department of Radiology and Nuclear Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Doctor Tanslaan 12, 6229 ET, Maastricht, The Netherlands.

出版信息

Nat Rev Clin Oncol. 2017 Dec;14(12):749-762. doi: 10.1038/nrclinonc.2017.141. Epub 2017 Oct 4.


DOI:10.1038/nrclinonc.2017.141
PMID:28975929
Abstract

Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.

摘要

放射组学是一种从标准医疗影像中挖掘高通量定量图像特征的方法,它能够在临床决策支持系统中提取和应用数据,以提高诊断、预后和预测的准确性,在癌症研究中越来越受到重视。放射组学分析利用复杂的图像分析工具和快速发展和验证的医疗影像数据,使用基于图像的特征进行精确诊断和治疗,为现代医学提供了强大的工具。本文描述了放射组学的过程、其陷阱、挑战、机遇以及其改善临床决策的能力,强调了其在癌症患者中的应用。目前,放射组学领域缺乏对该领域众多已发表的放射组学研究的科学完整性和临床相关性的标准化评估,这些研究是该领域快速发展的结果。为了使放射组学成为一门成熟的学科,需要建立严格的评估标准和报告指南。本文为满足这一领域的迫切需求提供了调查指导。

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本文引用的文献

[1]
Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer.

Sci Rep. 2017-4-3

[2]
Associations between Tumor Vascularity, Vascular Endothelial Growth Factor Expression and PET/MRI Radiomic Signatures in Primary Clear-Cell-Renal-Cell-Carcinoma: Proof-of-Concept Study.

Sci Rep. 2017-3-3

[3]
Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures.

Br J Radiol. 2017-2

[4]
The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review.

JAMA Oncol. 2016-12-1

[5]
Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study.

Phys Med Biol. 2016-9-7

[6]
Quantitative Analysis of (18)F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy.

Int J Radiat Oncol Biol Phys. 2016-9-1

[7]
Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma.

BMC Cancer. 2016-8-8

[8]
Predicting Malignant Nodules from Screening CT Scans.

J Thorac Oncol. 2016-12

[9]
Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non-Small Cell Lung Cancer.

Radiology. 2016-6-27

[10]
Overview of the American Society for Radiation Oncology-National Institutes of Health-American Association of Physicists in Medicine Workshop 2015: Exploring Opportunities for Radiation Oncology in the Era of Big Data.

Int J Radiat Oncol Biol Phys. 2016-7-1

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