Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.
Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.
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DOI：
10.3389/fonc.2022.773840
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发表时间：
2022
影响因子：
4.7
通讯作者：
Liao L
中科院分区：
文献类型：
--
作者：
Zhang X;Zhang Y;Zhang G;Qiu X;Tan W;Yin X;Liao L
从医学图像中高通量提取定量成像特征以用于放射组学分析，即广义的放射组学，是一个迅速发展和新兴的研究领域，特别是在多模式和多组学研究中。在这种背景下，多维数据的定量分析在评估不同组织和器官及其微环境的时空特征方面发挥着至关重要的作用。本文综述了该方法的最新进展，包括手动定义特征、数据采集和预处理、病变分割、特征提取、特征选择和降维、统计分析和模型构建。此外，正在分析基于深度学习的自动分割和放射学分析技术，以解决诸如严格的工作流程、手动/半自动病变注释、不充分的特征标准和多中心验证等限制。此外，从放射学图像、多模式图像、组织病理学图像和三维剂量分布数据的角度，特别是在肿瘤学中，总结了该技术在疾病诊断、治疗反应和预后预测方面的最新应用。还进一步分析了放射组学在诊断和治疗策略中的潜力和价值，并首次总结了与放射治疗相关的剂量组学的进展和挑战，突出了放射组学的最新进展。最后，提出了一个稳健的放射组学分析框架，并讨论了未来发展的挑战和建议，包括但不限于影响模型稳定性的因素(医学大数据和多类型数据以及医学方面的专家知识)，数据驱动过程的局限性(研究的重复性和可解释性，不同机构的不同治疗方案，以及前瞻性研究和临床试验)，以及对未来方向的思考(实现临床应用的能力和放射组学分析的开放平台)。
The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).
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影响因子：
19.7
作者：
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通讯作者：
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影响因子：
39.2
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作者：
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影响因子：
5.2
作者：
Alvarez-Jimenez C;Sandino AA;Prasanna P;Gupta A;Viswanath SE;Romero E
通讯作者：
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