Texture Analysis in the Diagnosis of Primary Breast Cancer: Comparison of High-Resolution Dedicated Breast Positron Emission Tomography (dbPET) and Whole-Body PET/CT

Yoko Satoh; Kenji Hirata; Daiki Tamada; Satoshi Funayama; Hiroshi Onishi

doi:10.3389/fmed.2020.603303

Texture Analysis in the Diagnosis of Primary Breast Cancer: Comparison of High-Resolution Dedicated Breast Positron Emission Tomography (dbPET) and Whole-Body PET/CT

Front Med (Lausanne). 2020 Dec 23:7:603303. doi: 10.3389/fmed.2020.603303. eCollection 2020.

Authors

Yoko Satoh^{1

2}, Kenji Hirata³, Daiki Tamada², Satoshi Funayama², Hiroshi Onishi²

Affiliations

¹ Yamanashi PET Imaging Clinic, Yamanashi, Japan.
² Department of Radiology, University of Yamanashi, Yamanashi, Japan.
³ Department of Diagnostic Imaging, School of Medicine, Hokkaido University, Sapporo, Japan.

Abstract

Objective: This retrospective study aimed to compare the ability to classify tumor characteristics of breast cancer (BC) of positron emission tomography (PET)-derived texture features between dedicated breast PET (dbPET) and whole-body PET/computed tomography (CT). Methods: Forty-four BCs scanned by both high-resolution ring-shaped dbPET and whole-body PET/CT were analyzed. The primary BC was extracted with a standardized uptake value (SUV) threshold segmentation method. On both dbPET and PET/CT images, 38 texture features were computed; their ability to classify tumor characteristics such as tumor (T)-category, lymph node (N)-category, molecular subtype, and Ki67 levels was compared. The texture features were evaluated using univariate and multivariate analyses following principal component analysis (PCA). AUC values were used to evaluate the diagnostic power of the computed texture features to classify BC characteristics. Results: Some texture features of dbPET and PET/CT were different between Tis-1 and T2-4 and between Luminal A and other groups, respectively. No association with texture features was found in the N-category or Ki67 level. In contrast, receiver-operating characteristic analysis using texture features' principal components showed that the AUC for classification of any BC characteristics were equally good for both dbPET and whole-body PET/CT. Conclusions: PET-based texture analysis of dbPET and whole-body PET/CT may have equally good classification power for BC.

Keywords: 18F-FDG; breast cancer; dedicated breast positron emission tomography (dbPET); positron emission tomography/computed tomography (PET/CT); texture analysis.