Intra- and Peritumoral Radiomics Nomogram Based on DCE-MRI for the Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer

Yun Zhu¹Shuni Zhang¹Wei Wei²Li Yang¹Lingling Wang³Ying Wang¹Ye Fan⁴Haitao Sun^5*Zongyu Xie^1*

• ¹Department of Radiology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, China
• ²Department of Radiology, Anhui No.2 Provincial People's Hospital, Hefei, China
• ³Department of Medical Imaging Diagnostics, Bengbu Medical University, Bengbu, China
• ⁴Department of Clinical Medicine, Bengbu Medical College, Bengbu, Anhui Province, China
• ⁵Zhongshan Hospital, Fudan University, Shanghai, Shanghai Municipality, China

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), Ki67, 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.