Prediction of pathological complete response using radiomics on MRI in patients with breast cancer undergoing neoadjuvant pharmacotherapy

• DOI: 10.1007/s11548-022-02560-z
• Publication Date: 2022-01-12T17:02:52Z
• Authors: Yuka Kuramoto; Natsumi Wada; Yoshikazu Uchiyama
• Keywords: Breast Neoplasms; Mastectomy, Segmental; Magnetic Resonance Imaging; Neoadjuvant Therapy; 3. Good health; 03 medical and health sciences; 0302 clinical medicine; ROC Curve; Humans; Female; Breast; Retrospective Studies

Neoadjuvant pharmacotherapy is essential for patients with breast cancer who wish to preserve the breast by shrinking the malignant tumor, allowing breast-conserving surgery. It may eliminate cancer cells completely, which is known as pathologic complete response (pCR). Patients with pCR have a lower risk of recurrence. The purpose of this study was to develop a method for predicting patients who achieve pCR by neoadjuvant pharmacotherapy using radiomic features in MR images.Fat-suppressed T2-weighted MR images of 64 cases were identified from the ISPY1 dataset. There were 26 cases of pCR and 38 cases of non-pCR. The image slice with the largest tumor diameter was selected from MR images, and the tumor region was manually segmented. A total of 371 radiomic features were calculated from the tumor region. We selected nine radiomic features using Lasso in this study. A support vector machine (SVM) with nine radiomic features was used for predicting patients with pCR.The result of the ROC analysis showed that the area under the curve of SVM was 0.92 for distinguishing between pCR and non-pCR. Although the input data contain data that were misclassified by SVM, the survival curve classified into the pCR group was at a higher position than the non-pCR group. However, the log-rank test was [Formula: see text].We developed a method to predict patients with pCR by neoadjuvant pharmacotherapy using noninvasive MR images. The survival curve of patients classified as having pCR by the proposed method was higher than those classified as non-pCR. Since the proposed method predicts patients who achieve pCR by neoadjuvant pharmacotherapy, it enhances the value of preoperative image information.