IMR Press / FBL / Volume 26 / Issue 9 / DOI: 10.52586/4960
Open Access Original Research
Molecular subtype classification of breast cancer using established radiomic signature models based on 18F-FDG PET/CT images
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1 Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, 300060 Tianjin, China
2 Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, 300060 Tianjin, China
3 Department of Nuclear Medicine, Southwest Hospital, The First Affiliated Hospital to Army Medical University, 400038 Chongqing, China
4 Department of General Surgery, Tianjin Medical University General Hospital, 300060 Tianjin, China
5 Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, 300060 Tianjin, China
*Correspondence: xli03@tmu.edu.cn (Xiaofeng Li); mawenjuan@tmu.edu.cn (Wenjuan Ma); wxu06@tmu.edu.cn (Wengui Xu)
These authors contributed equally.
Front. Biosci. (Landmark Ed) 2021, 26(9), 475–484; https://doi.org/10.52586/4960
Submitted: 5 July 2021 | Revised: 28 July 2021 | Accepted: 9 August 2021 | Published: 30 August 2021
(This article belongs to the Special Issue Cancer Metabolism and the Tumor Microenvironment)
Copyright: © 2021 The Author(s). Published by BRI.
This is an open access article under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).
Abstract

Backgrounds: To evaluate the predictive power of 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) derived radiomics in molecular subtype classification of breast cancer (BC). Methods: A total of 273 primary BC patients who underwent a 18F-FDG PET/CT imaging prior to any treatment were included in this retrospective study, and the values of five conventional PET parameters were calculated, including the maximum standardized uptake value (SUVmax), SUVmean, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The ImageJ 1.50i software and METLAB package were used to delineate the contour of BC lesions and extract PET/CT derived radiomic features reflecting heterogeneity. Then, the least absolute shrinkage and selection operator (LASSO) algorithm was used to select optimal subsets of radiomic features and establish several corresponding radiomic signature models. The predictive powers of individual PET parameters and developed PET/CT derived radiomic signature models in molecular subtype classification of BC were evaluated by using receiver operating curves (ROCs) analyses with areas under the curve (AUCs) as the main outcomes. Results: All of the three SUV parameters but not MTV nor TLG were found to be significantly underrepresented in luminal and non-triple (TN) subgroups in comparison with corresponding non-luminal and TN subgroups. Whereas, no significant differences existed in all the five conventional PET parameters between human epidermal growth factor receptor 2+ (HER2+) and HER2– subgroups. Furthermore, all of the developed radiomic signature models correspondingly exhibited much more better performances than all the individual PET parameters in molecular subtype classification of BC, including luminal vs. non-luminal, HER2+ vs. HER2–, and TN vs. non-TN classification, with a mean value of 0.856, 0.818, and 0.888 for AUC. Conclusions: PET/CT derived radiomic signature models outperformed individual significant PET parameters in molecular subtype classification of BC.

Keywords
Breast cancer
Radiomics
Molecular subtype classification
Positron emission tomography/computed tomography (18F-FDG PET/CT)
Funding
81801781/National Natural Science Foundation of China
82072004/National Natural Science Foundation of China
2018ZX09201015/National Natural Science Foundation of China
18PTZWHZ00100/Tianjin Science and Technology Committee Fund
H2018206600/Tianjin Science and Technology Committee Fund
2018KJ057/Science & Technology Development Fund of Tianjin Education Commission for Higher Education
2018KJ061/Science & Technology Development Fund of Tianjin Education Commission for Higher Education
Figures
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