DOI: 10.1007/s00259-018-4195-9Pages: 1-8

Use of 18F-FDG PET/CT texture analysis to diagnose cardiac sarcoidosis

1. Hokkaido University Graduate School of Medicine, Department of Nuclear Medicine

2. Hokkaido University Hospital, First Department of Medicine

3. Hokkaido University Hospital, Department of Cardiovascular Medicine

4. Hokkaido University Hospital, Department of Diagnostic and Interventional Radiology

5. National Institute of Radiological Science, Diagnostic and Therapeutic Nuclear Medicine

6. Kyoto Prefectural University of Medicine, Department of Radiology

Correspondence to:
Kenji Hirata
Tel: +81-11-706-5152
Email: khirata@med.hokudai.ac.jp

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Abstract

Purpose

18F-fluorodeoxyglocose positron emission tomography (FDG PET) plays a significant role in the diagnosis of cardiac sarcoidosis (CS). Texture analysis is a group of computational methods for evaluating the inhomogeneity among adjacent pixels or voxels. We investigated whether texture analysis applied to myocardial FDG uptake has diagnostic value in patients with CS.

Methods

Thirty-seven CS patients (CS group), and 52 patients who underwent FDG PET/CT to detect malignant tumors with any FDG cardiac uptake (non-CS group) were studied. A total of 36 texture features from the histogram, gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), gray-level zone size matrix (GLZSM) and neighborhood gray-level difference matrix (NGLDM), were computed using polar map images. First, the inter-operator and inter-scan reproducibility of the texture features of the CS group were evaluated. Then, texture features of the patients with CS were compared to those without CS lesions.

Results

Twenty-eight of the 36 texture features showed high inter-operator reproducibility with intraclass correlation coefficients (ICCs) over 0.80. In addition, 17 of the 36 showed high inter-scan reproducibility with ICCs over 0.80. The SUVmax showed no difference between the CS and non-CS group [7.36 ± 2.77 vs. 8.78 ± 4.65, p = 0.45, area under the curve (AUC) = 0.60]. By contrast, 16 of the 36 texture features could distinguish CS from non-CS grsoup with AUC > 0.80. Multivariate logistic regression analysis after hierarchical clustering concluded that long-run emphasis (LRE; P = 0.0004) and short-run low gray-level emphasis (SRLGE; P = 0.016) were significant independent factors that could distinguish between the CS and non-CS groups. Specifically, LRE was significantly higher in CS than in non-CS (30.1 ± 25.4 vs. 11.4 ± 4.6, P < 0.0001), with high diagnostic ability (AUC = 0.91), and had high inter-operator reproducibility (ICC = 0.98).

Conclusions

The texture analysis had high inter-operator and high inter-scan reproducibility. Some of texture features showed higher diagnostic value than SUVmax for CS diagnosis. Therefore, texture analysis may have a role in semi-automated systems for diagnosing CS.

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  • Accepted: Oct 10, 2018
  • Online: Oct 16, 2018

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