Texture Analysis Using Machine Learning-Based 3-T Magnetic Resonance Imaging for Predicting Pathological Response in Breast Cancer Patients Treated with Neoadjuvant Chemotherapy

Abstract

Neoadjuvant chemotherapy (NAC) is the standard treatment for large operable or locally advanced breast cancer. Magnetic resonance imaging (MRI) is an important tool to evaluate tumor extent and response to treatment. In the future, surgery may not be required for patients showing pathologic complete response (pCR) after NAC. Therefore, early identification of such patients is crucial for improving and personalizing patient treatment. A total of 90 patients were identified who were diagnosed with invasive breast carcinoma and underwent breast MRI before and after NAC between August 2014 and May 2018. The patients were divided into training group (72 cases) and test group (18 cases). Immunohistochemical data were used to classify breast carcinomas into five molecular subtypes: Luminal A; Luminal B; Her-2 negative; Her-2 positive; or triple-negative and the corresponding surgical specimens were evaluated according to Residual Cancer Burden (RCB) criteria for pathological response. Statistical analyses were conducted using statistical software and statistical significance was defined as P < 0.05. A total of 90 patients with invasive breast carcinoma were enrolled in our study, including 72 patients in training group and 18 patients in test group. The accuracy of molecular subtypes is Luminal A (0.77); Luminal B (0.78); Her-2 negative (0.72); Her-2 positive (0.79); or triple-negative (0.87). Compared with other molecular subtypes, triple-negative show better prediction results (P= 0.028). The accuracy of pathological response is Grade 1 chemotherapy response (0.73); Grade 2 chemotherapy response (0.84); Grade 3 chemotherapy response (0.78); Grade 4 chemotherapy response (0.79); Grade 5 chemotherapy response (0.77). There was no significant difference in chemotherapy response between five groups. The results of this analysis suggested that three negative breast cancer has better prediction effect in magnetic resonance texture analysis for breast cancer patients receiving NAC. However, magnetic resonance texture analysis did not show statistical differences in chemotherapy response. A multicenter randomized controlled prospective trial may be needed.