Refaee Turkey, Salahuddin Zohaib, Widaatalla Yousif, Primakov Sergey, Woodruff Henry C, Hustinx Roland, Mottaghy Felix M, Ibrahim Abdalla, Lambin Philippe
The D-Lab, Department of Precision Medicine, GROW-School for Oncology, Maastricht University, 6200 Maastricht, The Netherlands.
Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan 45142, Saudi Arabia.
J Pers Med. 2022 Mar 31;12(4):553. doi: 10.3390/jpm12040553.
Handcrafted radiomics features (HRFs) are quantitative features extracted from medical images to decode biological information to improve clinical decision making. Despite the potential of the field, limitations have been identified. The most important identified limitation, currently, is the sensitivity of HRF to variations in image acquisition and reconstruction parameters. In this study, we investigated the use of Reconstruction Kernel Normalization (RKN) and ComBat harmonization to improve the reproducibility of HRFs across scans acquired with different reconstruction kernels. A set of phantom scans ( = 28) acquired on five different scanner models was analyzed. HRFs were extracted from the original scans, and scans were harmonized using the RKN method. ComBat harmonization was applied on both sets of HRFs. The reproducibility of HRFs was assessed using the concordance correlation coefficient. The difference in the number of reproducible HRFs in each scenario was assessed using McNemar's test. The majority of HRFs were found to be sensitive to variations in the reconstruction kernels, and only six HRFs were found to be robust with respect to variations in reconstruction kernels. The use of RKN resulted in a significant increment in the number of reproducible HRFs in 19 out of the 67 investigated scenarios (28.4%), while the ComBat technique resulted in a significant increment in 36 (53.7%) scenarios. The combination of methods resulted in a significant increment in 53 (79.1%) scenarios compared to the HRFs extracted from original images. Since the benefit of applying the harmonization methods depended on the data being harmonized, reproducibility analysis is recommended before performing radiomics analysis. For future radiomics studies incorporating images acquired with similar image acquisition and reconstruction parameters, except for the reconstruction kernels, we recommend the systematic use of the pre- and post-processing approaches (respectively, RKN and ComBat).
手工制作的放射组学特征（HRFs）是从医学图像中提取的定量特征，用于解码生物信息以改善临床决策。尽管该领域具有潜力，但也存在一些局限性。目前已确定的最重要的局限性是HRF对图像采集和重建参数变化的敏感性。在本研究中，我们研究了使用重建内核归一化（RKN）和ComBat归一化来提高不同重建内核扫描中HRF的可重复性。分析了在五种不同扫描仪型号上采集的一组体模扫描（n = 28）。从原始扫描中提取HRF，并使用RKN方法对扫描进行归一化。对两组HRF均应用ComBat归一化。使用一致性相关系数评估HRF的可重复性。使用McNemar检验评估每种情况下可重复HRF数量的差异。发现大多数HRF对重建内核的变化敏感，只有六个HRF在重建内核变化方面具有稳健性。在67个研究场景中的19个（28.4%）中，使用RKN导致可重复HRF的数量显著增加，而ComBat技术在36个（53.7%）场景中导致显著增加。与从原始图像中提取的HRF相比，两种方法的组合在53个（79.1%）场景中导致显著增加。由于应用归一化方法的益处取决于要归一化的数据，因此建议在进行放射组学分析之前进行可重复性分析。对于未来纳入除重建内核外具有相似图像采集和重建参数的图像的放射组学研究，我们建议系统地使用预处理和后处理方法（分别为RKN和ComBat）。
