An MRI-based joint model of radiomics and spatial distribution differentiates autoimmune encephalitis from low-grade diffuse astrocytoma

Sirong Piao; Xiao Luo; Yifang Bao; Bin Hu; Xueling Liu; Yuqi Zhu; Liqin Yang; Daoying Geng; Yuxin Li

doi:10.3389/fneur.2022.998279

An MRI-based joint model of radiomics and spatial distribution differentiates autoimmune encephalitis from low-grade diffuse astrocytoma

Front Neurol. 2022 Nov 3:13:998279. doi: 10.3389/fneur.2022.998279. eCollection 2022.

Authors

Sirong Piao^{1

2}, Xiao Luo³, Yifang Bao^{1

2}, Bin Hu^{1

2}, Xueling Liu^{1

2}, Yuqi Zhu^{1

2}, Liqin Yang^{1

2

3}, Daoying Geng^{1

2

3}, Yuxin Li^{1

2

3}

Affiliations

¹ Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
² Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.
³ Academy for Engineering and Technology, Fudan University, Shanghai, China.

Abstract

Background: The differential diagnosis between autoimmune encephalitis and low-grade diffuse astrocytoma remains challenging. We aim to develop a quantitative model integrating radiomics and spatial distribution features derived from MRI for discriminating these two conditions.

Methods: In our study, we included 188 patients with confirmed autoimmune encephalitis (n = 81) and WHO grade II diffuse astrocytoma (n = 107). Patients with autoimmune encephalitis (AE, n = 59) and WHO grade II diffuse astrocytoma (AS, n = 79) were divided into training and test sets, using stratified sampling according to MRI scanners. We further included an independent validation set (22 patients with AE and 28 patients with AS). Hyperintensity fluid-attenuated inversion recovery (FLAIR) lesions were segmented for each subject. Ten radiomics and eight spatial distribution features were selected via the least absolute shrinkage and selection operator (LASSO), and joint models were constructed by logistic regression for disease classification. Model performance was measured in the test set using the area under the receiver operating characteristic (ROC) curve (AUC). The discrimination performance of the joint model was compared with neuroradiologists.

Results: The joint model achieved better performance (AUC 0.957/0.908, accuracy 0.914/0.840 for test and independent validation sets, respectively) than the radiomics and spatial distribution models. The joint model achieved lower performance than a senior neuroradiologist (AUC 0.917/0.875) but higher performance than a junior neuroradiologist (AUC 0.692/0.745) in the test and independent validation sets.

Conclusion: The joint model of radiomics and spatial distribution from a single FLAIR could effectively classify AE and AS, providing clinical decision support for the differential diagnosis between the two conditions.

Keywords: MRI; autoimmune encephalitis; imaging analysis; low-grade diffuse astrocytoma; radiomics; spatial distribution.