Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer.

Purpose: This study aimed to create a nomogram model (NM) that combines clinical-radiological factors with radiomics features of both intra- and peritumoral regions extracted from pretherapy dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images, in order to establish a reliable method for early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with breast cancer. Methods: A total of 214 patients were randomly divided into a training set (n=149) and a test set (n=65) in a ratio of 7:3. Radiomics features were extracted from intratumoral region and 2-mm, 4-mm, 6-mm, 8-mm peritumoral regions on DCE-MRI images, and selected the optimal peritumoral region. The intratumoral radiomics model (IRM), 2-mm, 4-mm, 6-mm, 8-mm peritumoral radiomics model (PRM), the combined intra- and the optimal peritumoral radiomics model (CIPRM) were constructed based on five machine learning algorithms, and then the radiomics scores (Rad-score) were obtained. Independent risk factors for clinical-radiological features were obtained by univariate and multivariate logistic regression analysis, and clinical model (CM) was constructed. Finally, the CIPRM Rad-score combined with clinical-radiological factors was used to construct a NM. The performance of different models were evaluated by receiver operating characteristic curve (ROC) analysis, calibration curve analysis, and decision curve analysis (DCA). Results: In our study, the 6-mm peritumoral size was considered to be the optimal peritumoral region. The CM is constructed based on three independent risk factors: estrogen receptor (ER), Ki-67, and breast edema score (BES). Incorporating ER, Ki-67, BES, and CIPRM Rad-score (combined intra- and 6-mm peritumoral) into the nomogram achieved a reliable predictive performance. And the area under the curve (AUC), sensitivity, specificity, and accuracy of the NM was 0.911, 0.848, 0.831, 0.826 for the training set and 0.897, 0.893, 0.784, 0.815 for the test set, respectively. Conclusion: The NM has a good value for early prediction of pCR after NAC in breast cancer patients.