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放射组学洞察:面向初学者的全面综述

Insights into radiomics: a comprehensive review for beginners.

作者信息

Mariotti Francesco, Agostini Andrea, Borgheresi Alessandra, Marchegiani Marzia, Zannotti Alice, Giacomelli Gloria, Pierpaoli Luca, Tola Elisabetta, Galiffa Elena, Giovagnoni Andrea

机构信息

Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Tronto, 10/A, 60126, Ancona, Italy.

Department of Radiological Sciences - Division of Clinical Radiology, University Hospital "Azienda Ospedaliero Universitaria delle Marche", Via Conca, 71, 60126, Ancona, Italy.

出版信息

Clin Transl Oncol. 2025 May 12. doi: 10.1007/s12094-025-03939-5.

Abstract

Radiomics and artificial intelligence (AI) are rapidly evolving, significantly transforming the field of medical imaging. Despite their growing adoption, these technologies remain challenging to approach due to their technical complexity. This review serves as a practical guide for early-career radiologists and researchers seeking to integrate radiomics into their studies. It provides practical insights for clinical and research applications, addressing common challenges, limitations, and future directions in the field. This work offers a structured overview of the essential steps in the radiomics workflow, focusing on concrete aspects of each step, including indicative and practical examples. It covers the main steps such as dataset definition, image acquisition and preprocessing, segmentation, feature extraction and selection, and AI model training and validation. Different methods to be considered are discussed, accompanied by summary diagrams. This review equips readers with the knowledge necessary to approach radiomics and AI in medical imaging from a hands-on research perspective.

摘要

放射组学和人工智能(AI)正在迅速发展,极大地改变了医学成像领域。尽管它们的应用越来越广泛,但由于技术复杂性,这些技术仍然难以掌握。本综述为寻求将放射组学纳入其研究的早期职业放射科医生和研究人员提供了实用指南。它为临床和研究应用提供了实用见解,探讨了该领域常见的挑战、局限性和未来方向。这项工作对放射组学工作流程中的基本步骤进行了结构化概述,重点关注每个步骤的具体方面,包括指示性和实际示例。它涵盖了主要步骤,如数据集定义、图像采集与预处理、分割、特征提取与选择以及AI模型训练与验证。讨论了需要考虑的不同方法,并配有示意图。本综述使读者从实际研究的角度获得在医学成像中应用放射组学和AI所需的知识。

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