Assessment of androgen receptor expression in breast cancer patients using 18 F-FDG PET/CT radiomics and clinicopathological characteristics

Objective In the present study, we mainly aimed to predict the expression of androgen receptor (AR) in breast cancer (BC) patients by combing radiomic features and clinicopathological factors in a non-invasive machine learning way. Materials and methods A total of 48 BC patients, who were initially diagnosed by 18F-FDG PET/CT, were retrospectively enrolled in this study. LIFEx software was used to extract radiomic features based on PET and CT data. The most useful predictive features were selected by the LASSO (least absolute shrinkage and selection operator) regression and t-test. Radiomic signatures and clinicopathologic characteristics were incorporated to develop a prediction model using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve, Hosmer-Lemeshow (H-L) test, and decision curve analysis (DCA) were conducted to assess the predictive efficiency of the model. Results In the univariate analysis, the metabolic tumor volume (MTV) was significantly correlated with the expression of AR in BC patients (p < 0.05). However, there only existed feeble correlations between estrogen receptor (ER), progesterone receptor (PR), and AR status (p = 0.127, p = 0.061, respectively). Based on the binary logistic regression method, MTV, $ SHAPE_Sphericity_{CT} $ (CT Sphericity from SHAPE), and $ GLCM_Contrast_{CT} $ (CT Contrast from grey-level co-occurrence matrix) were included in the prediction model for AR expression. Among them, $ GLCM_Contrast_{CT} $ was an independent predictor of AR status (OR = 9.00, p = 0.018). The area under the curve (AUC) of ROC in this model was 0.832. The p-value of the H-L test was beyond 0.05. Conclusions A prediction model combining radiomic features and clinicopathological characteristics could be a promising approach to predict the expression of AR and noninvasively screen the BC patients who could benefit from anti-AR regimens..

Medienart:	E-Artikel

Erscheinungsjahr:	2023
Erschienen:	2023

Enthalten in:	Zur Gesamtaufnahme - volume:23
Enthalten in:	BMC medical imaging - 23(2023), 1 vom: 17. Juli

Sprache:	Englisch

Beteiligte Personen:	Jia, Tongtong [VerfasserIn] Lv, Qingfu [VerfasserIn] Zhang, Bin [VerfasserIn] Yu, Chunjing [VerfasserIn] Sang, Shibiao [VerfasserIn] Deng, Shengming [VerfasserIn]

Links:	Volltext [kostenfrei]

Themen:	Androgen receptor Breast cancer Clinicopathological F-FDG PET/CT Machine learning Radiomics

Anmerkungen:	© The Author(s) 2023

doi:	10.1186/s12880-023-01052-z

funding:
Förderinstitution / Projekttitel:

PPN (Katalog-ID):	SPR052280292