Mali Shruti Atul, Ibrahim Abdalla, Woodruff Henry C, Andrearczyk Vincent, Müller Henning, Primakov Sergey, Salahuddin Zohaib, Chatterjee Avishek, Lambin Philippe
The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, Maastricht, Universiteitssingel 40, 6229 ER Maastricht, The Netherlands.
Department of Radiology and Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Center+, P.O. Box 5800, 6202 AZ Maastricht, The Netherlands.
J Pers Med. 2021 Aug 27;11(9):842. doi: 10.3390/jpm11090842.
Radiomics converts medical images into mineable data via a high-throughput extraction of quantitative features used for clinical decision support. However, these radiomic features are susceptible to variation across scanners, acquisition protocols, and reconstruction settings. Various investigations have assessed the reproducibility and validation of radiomic features across these discrepancies. In this narrative review, we combine systematic keyword searches with prior domain knowledge to discuss various harmonization solutions to make the radiomic features more reproducible across various scanners and protocol settings. Different harmonization solutions are discussed and divided into two main categories: image domain and feature domain. The image domain category comprises methods such as the standardization of image acquisition, post-processing of raw sensor-level image data, data augmentation techniques, and style transfer. The feature domain category consists of methods such as the identification of reproducible features and normalization techniques such as statistical normalization, intensity harmonization, ComBat and its derivatives, and normalization using deep learning. We also reflect upon the importance of deep learning solutions for addressing variability across multi-centric radiomic studies especially using generative adversarial networks (GANs), neural style transfer (NST) techniques, or a combination of both. We cover a broader range of methods especially GANs and NST methods in more detail than previous reviews.
放射组学通过高通量提取用于临床决策支持的定量特征，将医学图像转化为可挖掘的数据。然而，这些放射组学特征容易受到不同扫描仪、采集协议和重建设置的影响而产生变化。各种研究已经评估了这些差异下放射组学特征的可重复性和有效性。在这篇叙述性综述中，我们将系统的关键词搜索与先前的领域知识相结合，讨论各种标准化解决方案，以使放射组学特征在不同扫描仪和协议设置下更具可重复性。我们讨论了不同的标准化解决方案，并将其分为两大类：图像域和特征域。图像域类别包括图像采集标准化、原始传感器级图像数据的后处理、数据增强技术和风格迁移等方法。特征域类别包括可重复性特征识别和归一化技术，如统计归一化、强度标准化、ComBat及其衍生方法，以及使用深度学习进行的归一化。我们还反思了深度学习解决方案对于解决多中心放射组学研究中的变异性的重要性，特别是使用生成对抗网络（GAN）、神经风格迁移（NST）技术或两者结合的情况。与以往的综述相比，我们更详细地介绍了更广泛的方法，特别是GAN和NST方法。
