Abstract:

Objective

To establish a prediction model for HER-2 expression status in breast cancer using habitat analysis based on enhanced MRI radiomic features.

Methods

A retrospective analysis was conducted on the second-phase DCE-T₁ WI data of 168 breast cancer patients who underwent enhanced MRI examinations in the Affiliated Suzhou Hospital of Nanjing Medical University from January 2018 to May 2023.Among them， 100 cases were HER-2-negative， and 68 cases were HER-2-positive. After preprocessing the images， the whole tumor volume of interest （VOI） was manually segmented. Twenty-four regional radiomic features were extracted and clustered into habitat subregions using a Gaussian Mixture Model （GMM） combined with the Bayesian Information Criterion （BIC）. Radiomic features were separately extracted from the subregions and the entire tumor region. The data were randomly divided into a training set （117 cases） and a validation set（51 cases） at the ratio of 7 ∶3. Logistic regression （LR）， support vector machine （SVM）， and k-nearest neighbor （KNN） algorithms were used to construct habitat prediction models and whole-tumor prediction models. The optimal model was selected based on the area under the curve （AUC） value in the validation set，the receiver operating characteristic （ROC） curve was drawn. DeLong’s test was used to compare the AUC of the two prediction models， and decision curve analysis （DCA） was performed to evaluate the clinical utility of the models.

Results

Each tumor VOI was segmented into three habitat subregions. SVM was identified as the best modeling method. The habitat prediction model-SVM achieved an AUC of 0.949 （95%CI：0.915-0.984） in the training set and 0.844 （95%CI： 0.725-0.963） in the validation set. The best whole-tumor prediction model-SVM achieved an AUC of 0.870 （95%CI： 0.809-0.931） in the training set and 0.735 （95%CI： 0.588-0.882） in the validation set. The habitat prediction model-SVM demonstrated superior accuracy， sensitivity and specificity compared with the whole-tumor prediction model-SVM in both the training and validation sets.DeLong’s test indicated that the AUC differences between the models in the training set were statistically significant （Z=2.134， P=0.033）. DCA results showed that the habitat prediction model-SVM provided the highest net benefit.

Conclusions

This study established a prediction model for HER-2 expression status in breast cancer based on habitat analysis using enhanced MRI radiomic features， which may provide a reference for precision treatment of breast cancer patients.

Key words: Breast neoplasms, Magnetic resonance imaging, Human epidermal growth factor receptor 2, Habitat analysis