Differentiation Between Glioblastoma Multiforme and Metastasis From the Lungs and Other Sites Using Combined Clinical/Routine MRI Radiomics

Yuqi Han; Lingling Zhang; Shuzi Niu; Shuguang Chen; Bo Yang; Hongyan Chen; Fei Zheng; Yuying Zang; Hongbo Zhang; Yu Xin; Xuzhu Chen

doi:10.3389/fcell.2021.710461

Differentiation Between Glioblastoma Multiforme and Metastasis From the Lungs and Other Sites Using Combined Clinical/Routine MRI Radiomics

Front Cell Dev Biol. 2021 Aug 26:9:710461. doi: 10.3389/fcell.2021.710461. eCollection 2021.

Authors

Yuqi Han^{1

2}, Lingling Zhang³, Shuzi Niu⁴, Shuguang Chen⁵, Bo Yang⁶, Hongyan Chen³, Fei Zheng³, Yuying Zang³, Hongbo Zhang⁷, Yu Xin⁸, Xuzhu Chen³

Affiliations

¹ School of Life Sciences and Technology, Xidian University, Xi'an, China.
² Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
³ Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
⁴ Institute of Software, Chinese Academy of Sciences, Beijing, China.
⁵ School of Mathematical Sciences, Nankai University, Tianjin, China.
⁶ Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China.
⁷ Department of Neurosurgery, Huizhou Third People's Hospital, Guangzhou Medical University, Huizhou, China.
⁸ Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

Abstract

Background: Differentiation between cerebral glioblastoma multiforme (GBM) and solitary brain metastasis (MET) is important. The existing radiomic differentiation method ignores the clinical and routine magnetic resonance imaging (MRI) features.

Purpose: To differentiate between GBM and MET and between METs from the lungs (MET-lung) and other sites (MET-other) through clinical and routine MRI, and radiomics analyses.

Methods and materials: A total of 350 patients were collected from two institutions, including 182 patients with GBM and 168 patients with MET, which were all proven by pathology. The ROI of the tumor was obtained on axial postcontrast MRI which was performed before operation. Seven radiomic feature selection methods and four classification algorithms constituted 28 classifiers in two classification strategies, with the best classifier serving as the final radiomics model. The clinical and combination models were constructed using the nomograms developed. The performance of the nomograms was evaluated in terms of calibration, discrimination, and clinical usefulness. Student's t-test or the chi-square test was used to assess the differences in the clinical and radiological characteristics between the training and internal validation cohorts. Receiver operating characteristic curve analysis was performed to assess the performance of developed models with the area under the curve (AUC).

Results: The classifier fisher_decision tree (fisher_DT) showed the best performance (AUC: 0.696, 95% CI:0.608-0.783) for distinguishing between GBM and MET in internal validation cohorts; the classifier reliefF_random forest (reliefF_RF) showed the best performance (AUC: 0.759, 95% CI: 0.613-0.904) for distinguishing between MET-lung and MET-other in internal validation cohorts. The combination models incorporating the radiomics signature and clinical-radiological characteristics were superior to the clinical-radiological models in the two classification strategies (AUC: 0.764 for differentiation between GBM in internal validation cohorts and MET and 0.759 or differentiation between MET-lung and MET-other in internal validation cohorts). The nomograms showed satisfactory performance and calibration and were considered clinically useful, as revealed in the decision curve analysis.

Data conclusion: The combination of radiomic and non-radiomic features is helpful for the differentiation among GBM, MET-lung, and MET-other.

Keywords: glioblastoma multiforme; machine learning; magnetic resonance imaging; metastasis; radiomics.