Objective To evaluate the predictive value of ¹⁸F-FDG PET/CT derived multivariate radiomic model in human epidermal growth factor 2 (HER-2) status for primary breast cancer (BC).

Methods A total of 273 BC patients aged 26−78(51.8±10.8) years with complete clinical data and imaging data who underwent ¹⁸F-FDG PET/CT imaging before any treatment from January 1, 2010, to December 31, 2019, were included in the retrospective study. According to HER-2 status in primary BC lesion, the BC patients were classified into HER-2 positive group and HER-2 negative group. The differences in clinical characteristics and PET/CT metabolic parameters between the two groups were compared. For radiomic analysis, a multivariate radiomic model based on PET/CT was established after lesion segmentation and radiomic feature extraction. Furthermore, all the candidates were randomly divided into the training set and testing set at a ratio of 7∶3. Receiver operator characteristic curve analysis was used to determine the predictive power of PET metabolic parameters and develop a radiomic model in HER-2 status. Furthermore, the average performance of the radiomic model in the prediction of HER-2 status was determined after tenfold cross-validation. The Wilcoxon rank sum test was performed to compare the differences in PET metabolic parameters between the two groups. Chi-square test was used for qualitative data, whereas two independent sample t test was used for quantitative data with normal distribution. Mann-Whitney U rank sum test was employed for quantitative data that did not obey normal distribution.

Results A total of 106 patients were classified in HER-2 positive group, and 167 patients were in the negative group. The proportion of patients with axillary lymph node metastasis in the HER-2 negative group was higher than that in the HER-2 positive group (85.03%(80/106) vs. 75.47%(142/167)), and the difference was statistically significant (χ²=3.900, P<0.05). By contrast, no significant difference was found in age, pathological type, and tumor stage between the two groups (t=−0.028, χ²=5.429, 1.891; all P>0.05). For the five PET metabolic parameters between the two groups, namely, maximum standard uptake value, mean standard uptake value, peak of standard uptake value, metabolic tumor volume, and total lesion glycolysis, no statistically significant difference was found in the study (Z=−1.583 to −0.064, all P>0.05). In the training set, the area under the curve (AUC), accuracy, sensitivity, and specificity of the radiomic model were 0.913(95%CI: 0.871–0.954), 0.882(95%CI: 0.832–0.922), 0.849(95%CI: 0.759–0.910), and 0.910(95%CI: 0.841–0.952), respectively. In the testing set, the AUC, accuracy, sensitivity, and specificity of the radiomic model were 0.820(95%CI: 0.723–0.918), 0.830(95%CI: 0.738–0.900), 0.875(95%CI: 0.701–0.959), and 0.807(95%CI: 0.683–0.892), respectively. After tenfold cross-validation, the average AUC, accuracy, sensitivity, and specificity of the imaging omics model were 0.818, 0.847, 0.908, and 0.764, respectively.

Conclusions The established multivariate radiomic model based on ¹⁸F-FDG PET/CT images outperformed the traditional PET metabolic parameters in the prediction of HER-2 status for primary BC. This model can contribute to the clinical screening of a potential sensitive population for trastuzumab monoclonal antibody treatment and finally improve the prognosis for BC.