关键词:

乳腺癌, 预测模型, 超声, MRI, EGFR

Abstract:

Objective　To establish a model for predicting the expression of epidermal growth factor receptor (EGFR) in breast cancer by ultrasound signs/parameters combined with non-morphological (functional and hemodynamic) magnetic resonance imaging (MRI) parameters and evaluate the diagnostic efficiency of the model. Methods　A retrospective study was conducted on 167 female patients with breast cancer aged 21-79 (45.23±11.72) years who underwent surgical resection after neoadjuvant chemotherapy (NAC) in Lianyungang First People's Hospital from January 2015 to December 2021. Before NAC treatment, all patients underwent breast mass biopsy, and their histopathological and EGFR analysis results were obtained. All patients underwent ultrasound and MRI examination before puncture (the interval between the two examinations was not more than 5 days). The parameters recorded were EGFR, apparent diffusion coefficient (ADC) value, Max Slope, time to peak (TTP), signal enhancement rate (SER), early enhancement rate (EER), time-signal strength curve (TIC), tumor maximum diameter, tumor margin and boundary, aspect ratio, microcalcification, color Doppler flow imaging (CDFI) grading, resistance index (RI), axillary lymph node metastasis (ALNM). Statistical analysis was conducted in R language 4.0.3 (https://www.r-project.org). All data were randomly divided into a modeling group and a verification group by the ratio of 7:3, and the random seeds were set. The comparison between the modeling group and the verification group was conducted according to the above variable types by independent sample t test, Mann-Whitney U test, and χ² test, respectively, to analyze the differences in the ultrasound parameters/signs, MRI non-morphological parameters, and EGFR expression. The random forest (RF) model was used to select variables in the EGFR modeling group. Generalized linear modeling (GLM) was used to obtain regression coefficient, odds ratio (OR), and 95% confidence interval for each modeling variable in the modeling group, and a colored nomogram was drawn. The model discrimination was evaluated by receiver operating characteristic curve (ROC), and the model effectiveness was evaluated by decision curve method (DCA). Results　ADC, maximum diameter, EER, and TTP were selected as the variables to construct the model for predicting EGFR. The model discrimination evaluation: ROC analysis showed that the area under the curve (AUC) of the modeling group was 0.815 (95%CI 0.726-0.905); in the verification group, the AUC was 0.805 (95%CI 0.660-0.949); AUC >0.80 indicated high discrimination. Model effectiveness evaluation: DCA analysis showed that the diagnosis probability in the patients of the modeling group was in the range of 0%-50%, and that of the verification group was in the range of 15%-55%. Conclusions　Based on machine learning, DCA, and other methods, the prediction model of EGFR expression by ultrasound signs/parameters combined with MRI non-morphological parameters before breast cancer treatment is proved to have good diagnostic efficacy. It is expected to produce higher clinical benefit and better clinical application in improving preoperative diagnosis and curative effect evaluation of immune factors for breast cancer.

Key words:

Breast cancer, Prediction model, Ultrasound, MRI, EGFR