Deep learning-based diagnosis of disease activity in patients with Graves' orbitopathy using orbital SPECT/CT

Ni Yao; Longxi Li; Zhengyuan Gao; Chen Zhao; Yanting Li; Chuang Han; Jiaofen Nan; Zelin Zhu; Yi Xiao; Fubao Zhu; Min Zhao; Weihua Zhou

doi:10.1007/s00259-023-06312-2

Deep learning-based diagnosis of disease activity in patients with Graves' orbitopathy using orbital SPECT/CT

Eur J Nucl Med Mol Imaging. 2023 Oct;50(12):3666-3674. doi: 10.1007/s00259-023-06312-2. Epub 2023 Jul 3.

Authors

Ni Yao^#¹, Longxi Li^#¹, Zhengyuan Gao², Chen Zhao³, Yanting Li¹, Chuang Han¹, Jiaofen Nan¹, Zelin Zhu¹, Yi Xiao⁴, Fubao Zhu¹, Min Zhao⁵, Weihua Zhou^{3

6}

Affiliations

¹ School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, Henan, China.
² School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, China.
³ Department of Applied Computing, Michigan Technological University, Houghton, MI, USA.
⁴ Department of Nuclear Medicine, Xiangya Hospital, Central South University, Changsha, China.
⁵ Department of Nuclear Medicine, The Third Xiangya Hospital, Central South University, No. 138, Tongzipo Road, Changsha, 410013, Hunan Province, China. mzhao1981@csu.edu.cn.
⁶ Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI, USA.

^# Contributed equally.

Abstract

Purpose: Orbital [^99mTc]TcDTPA orbital single-photon emission computed tomography (SPECT)/CT is an important method for assessing inflammatory activity in patients with Graves' orbitopathy (GO). However, interpreting the results requires substantial physician workload. We aim to propose an automated method called GO-Net to detect inflammatory activity in patients with GO.

Materials and methods: GO-Net had two stages: (1) a semantic V-Net segmentation network (SV-Net) that extracts extraocular muscles (EOMs) in orbital CT images and (2) a convolutional neural network (CNN) that uses SPECT/CT images and the segmentation results to classify inflammatory activity. A total of 956 eyes from 478 patients with GO (active: 475; inactive: 481) at Xiangya Hospital of Central South University were investigated. For the segmentation task, five-fold cross-validation with 194 eyes was used for training and internal validation. For the classification task, 80% of the eye data were used for training and internal fivefold cross-validation, and the remaining 20% of the eye data were used for testing. The EOM regions of interest (ROIs) were manually drawn by two readers and reviewed by an experienced physician as ground truth for segmentation GO activity was diagnosed according to clinical activity scores (CASs) and the SPECT/CT images. Furthermore, results are interpreted and visualized using gradient-weighted class activation mapping (Grad-CAM).

Results: The GO-Net model combining CT, SPECT, and EOM masks achieved a sensitivity of 84.63%, a specificity of 83.87%, and an area under the receiver operating curve (AUC) of 0.89 (p < 0.01) on the test set for distinguishing active and inactive GO. Compared with the CT-only model, the GO-Net model showed superior diagnostic performance. Moreover, Grad-CAM demonstrated that the GO-Net model placed focus on the GO-active regions. For EOM segmentation, our segmentation model achieved a mean intersection over union (IOU) of 0.82.

Conclusion: The proposed Go-Net model accurately detected GO activity and has great potential in the diagnosis of GO.

Keywords: Classification; Graves’ orbitopathy; SPECT/CT; Segmentation.

Abstract

Grants and funding