Magnetic resonance imaging-based radiomic features for extrapolating infiltration levels of immune cells in lower-grade
- PDF / 388,911 Bytes
- 9 Pages / 612.419 x 808.052 pts Page_size
- 84 Downloads / 176 Views
ORIGINAL ARTICLE
Magnetic resonance imaging-based radiomic features for extrapolating infiltration levels of immune cells in lower-grade gliomas Xuanwei Zhang1,2 · Shuo Liu3 · Xu Zhao1 · Xiaobo Shi1 · Jing Li1 · Jia Guo1 · Gabriele Niedermann4,5,6 · Ren Luo4,7 · Xiaozhi Zhang1 Received: 4 October 2019 / Accepted: 16 January 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Purpose To extrapolate the infiltration levels of immune cells in patients with lower-grade gliomas (LGGs) using magnetic resonance imaging (MRI)-based radiomic features. Methods A retrospective dataset of 516 patients with LGGs from The Cancer Genome Atlas (TCGA) database was analysed for the infiltration levels of six types of immune cells using Tumor IMmune Estimation Resource (TIMER) based on RNA sequencing data. Radiomic features were extracted from 107 patients whose pre-operative MRI data are available in The Cancer Imaging Archive; 85 and 22 of these patients were assigned to the training and testing cohort, respectively. The least absolute shrinkage and selection operator (LASSO) was applied to select optimal radiomic features to build the radiomic signatures for extrapolating the infiltration levels of immune cells in the training cohort. The developed radiomic signatures were examined in the testing cohort using Pearson’s correlation. Results The infiltration levels of B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils and dendritic cells negatively correlated with overall survival in the 516 patient cohort when using univariate Cox’s regression. Age, Karnofsky Performance Scale, WHO grade, isocitrate dehydrogenase mutant status and the infiltration of neutrophils correlated with survival using multivariate Cox’s regression analysis. The infiltration levels of the 6 cell types could be estimated by radiomic features in the training cohort, and their corresponding radiomic signatures were built. The infiltration levels of B cells, CD8+ T cells, neutrophils and macrophages estimated by radiomics correlated with those estimated by TIMER in the testing cohort. Combining clinical/genomic features with the radiomic signatures only slightly improved the prediction of immune cell infiltrations. Conclusion We developed MRI-based radiomic models for extrapolating the infiltration levels of immune cells in LGGs. Our results may have implications for treatment planning.
Keywords Lower-grade gliomas · Radiomics · Tumor infiltrating immune cells · Neutrophil · Isocitrate dehydrogenase
Xuanwei Zhang and Shuo Liu contributed equally to this manuscript. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00066-020-01584-1) contains supplementary material, which is available to authorized users.
2
Department of Thoracic Oncology, West China Hospital, Chengdu, China
3
Neurology Department, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
4
Department of Radiation Oncology, Faculty of Medicine, University of Freiburg, Frei
Data Loading...
