A general method for radiomic features selection - A SPECT simulation study

Introduction There are several radiological techniques, and, in this study, we focused the Single Photon Emission Computed Tomography (SPECT) imaging. It is possible to reconstruct the unknown tracer distribution inside the body by applying tomographic reconstruction algorithms such as Filtered Back Projection (FBP) and Ordered Subset Expectation Maximisation (OSEM) to the acquired data. Nowadays, thanks to technological innovations, a new branch of research has rapidly evolved: the radiomics. In practice, radiomics tries to assess whether the “textural features” of images in regions related to specific diseases could provide added value in a diagnostic process, in the evaluation of prognosis or could guide therapeutic choices. A general concern that has to be accounted for when performing a clinical study is whether changes in acquisition and reconstruction parameters could affect the value of the features. To the best of our knowledge, in literature, there is no unique method for identifying robust features; here, a generalised method to study the effects of the variation of reconstruction parameters on radiomic features is proposed and applied to asses stability and reliability in SPECT imaging. Materials and methods Only simulation studies could asses the link between features extracted from reconstructed images and their original values. From a preliminary statistical analysis, it emerged that at least 66 phantoms (representing different original textures) were needed to achieve a statistical power higher than 90%. These synthetic phantoms derived from abdominal CT scans and “Visible Human Project” image sets. Then, using a proper model, we simulated SPECT acquisitions of each phantom and reconstructed the corresponding images changing parameters in FBP and OSEM tomographic algorithms. Features extraction was conducted with PyRadiomics, an open-source software. Six feature classes were considered, based on Intensity, Grey-Level Co-occurrence Matrix (GLCM), Grey Level Dependence Matrix (GLDM), Grey Level Run Length Matrix (GLRLM), Grey Level Size Zone Matrix (GLSZM) and Neighbourhood Grey-Tone Difference Matrix (NGTDM). Ultimately, 93 different radiomics features for each phantom were calculated. In this way, data-set has a series of repeated measurements and the method of Generalised Estimating Equations (GEE) is suitable for analysing databases with a similar structure. In this study, two different GEE models were developed: one to analyse if the radiomic features calculated in the reconstructed images (Vr) reproduce the same feature in the original VOI (Vo); another to study if they are stable or not with reconstruction parameters variations. Results 32 different reconstructions for each available phantom were obtained, for a total of 2112 images stacks. The results of the two GEE models, features could be classified according to four possible groups: a) feature with a correlation between Vo and Vr, without reconstruction parameters variation effect; b) features without a correlation between Vo and Vr and without a significant impact of the reconstruction parameters variation; c) features with a statistically significant correlation between Vo and Vr and with the effect of the reconstruction parameters variation on the Vr value; d) reconstruction parameters variation affects Vr. Moreover, there is not a correlation between the values obtained from the reconstructed images and Vo. Discussion In literature, as far as we know, there are no trustworthy works of the reproducibility or repeatability applied to SPECT imaging. Here, with software simulations, we tried to answer the following two questions: 1) Are the features extracted from the reconstructed images (Vr) correlated to those of the original images (Vo)? 2) Are the features extracted from the reconstructed images robust when the reconstruction parameters vary? To answer these questions, two GEE models were developed. Most features showed a correlation between Vo and Vr, but with a relevant impact of reconstruction parameters variation. For clinical studies, in our opinion, features like a) would be the optimal choice. However, also features like c) could be used, but researchers have to handle with care these features for which the reconstruction parameters variations affect Vr. Using the remaining features is not recommended as the lack of correlation between Vo and Vr makes random any link with clinical end-points, so it could be difficult to reproduce any result on cohorts of patients other than the one used to develop the radiomic model. Conclusions From this study, it emerges how reconstruction parameters could affect radiomic features in SPECT imaging. In our opinion, researchers should take into account this dependency in both retrospective and prospective radiomic studies. Ultimately, the method described in this work, although complicated, represents a logical approach to carry out propaedeutic evaluations about the selection of imaging parameters or radiomic features to be used for clinical studies.