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基于多中心MRI的影像组学中的标准化策略

Harmonization Strategies in Multicenter MRI-Based Radiomics.

作者信息

Stamoulou Elisavet, Spanakis Constantinos, Manikis Georgios C, Karanasiou Georgia, Grigoriadis Grigoris, Foukakis Theodoros, Tsiknakis Manolis, Fotiadis Dimitrios I, Marias Kostas

机构信息

Computational BioMedicine Laboratory (CBML), Foundation for Research and Technology-Hellas (FORTH), 700 13 Heraklion, Greece.

Department of Oncology-Pathology, Karolinska Institutet, 171 77 Stockholm, Sweden.

出版信息

J Imaging. 2022 Nov 7;8(11):303. doi: 10.3390/jimaging8110303.

Abstract

Radiomics analysis is a powerful tool aiming to provide diagnostic and prognostic patient information directly from images that are decoded into handcrafted features, comprising descriptors of shape, size and textural patterns. Although radiomics is gaining momentum since it holds great promise for accelerating digital diagnostics, it is susceptible to bias and variation due to numerous inter-patient factors (e.g., patient age and gender) as well as inter-scanner ones (different protocol acquisition depending on the scanner center). A variety of image and feature based harmonization methods has been developed to compensate for these effects; however, to the best of our knowledge, none of these techniques has been established as the most effective in the analysis pipeline so far. To this end, this review provides an overview of the challenges in optimizing radiomics analysis, and a concise summary of the most relevant harmonization techniques, aiming to provide a thorough guide to the radiomics harmonization process.

摘要

放射组学分析是一种强大的工具,旨在直接从解码为手工特征的图像中提供患者的诊断和预后信息,这些特征包括形状、大小和纹理模式的描述符。尽管放射组学因其在加速数字诊断方面具有巨大潜力而日益受到关注,但由于众多患者间因素(如患者年龄和性别)以及扫描仪间因素(取决于扫描仪中心的不同协议采集),它容易受到偏差和变异的影响。已经开发了多种基于图像和特征的归一化方法来补偿这些影响;然而,据我们所知,到目前为止,这些技术中没有一种被确立为在分析流程中最有效的方法。为此,本综述概述了优化放射组学分析中的挑战,并简要总结了最相关的归一化技术,旨在为放射组学归一化过程提供全面指导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3e98/9695920/5778574fd1ee/jimaging-08-00303-g001.jpg

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