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Abstract P1-10-29: Radiomics improved pre-therapeutic prediction of breast cancers insensitive to neoadjuvant chemotherapy

Background: Approximately 10–35% of breast cancers were found to be insensitive to neoadjuvant chemotherapy (NAC), and approximately 5% of patients had larger tumors after NAC; in these patients, NAC failed to exhibit a therapeutic effect and instead delayed surgical treatment. Thus, it is critical to identify predictive biomarkers to enhance patient selection for NAC. Here we report a radiomic model for pre-therapeutic prediction of breast cancers insensitive to NAC. Materials and Methods: We retrospectively enrolled 125 breast cancer patients (63 in the primary cohort and 62 in the validation cohort) who underwent magnetic resonance imaging (imaging sequence: diffusion-weighted, T2-weighted and contrast-enhanced T1-weighted) before receiving NAC. All patients received surgical resection, and Miller-Payne grading system were applied to assess the response to NAC. Grade 1-2 cases were classified as insensitive to NAC. We extracted 1941 radiomic features in the primary cohort. After feature selection, the optimal feature set was used to construct a radiomic signature using machine learning. We built a combined prediction model incorporating the radiomic signature and independent clinical risk factors using multivariable logistic regression. The performance of the combined model was assessed in the validation cohort. Results: The radiomic signature consisting 4 features showed good performance for identifying Grade 1-2 group, yielding an area under the curve (AUC) of 0.83 (95% confidence interval: 0.647–1) in the validation cohort. A clinical model based on human epidermal growth factor receptor-2 (HER2) status and Ki67 index yielded an AUC of 0.792 (95% confidence interval: 0.668–0.916) in the validation cohort. Incorporating radiomic signature, HER2 status and Ki67 index, the combined prediction model reached a better discrimination power than the radiomic signature and clinical model, with an AUC of 0.935 (95% confidence interval, 0.848–1) in the validation cohort. Conclusion: The combined model based on radiomics and clinical variables has a great potential in predicting drug insensitive breast cancers. Citation Format: Xuezhi Zhou, Zhenyu Liu, Yang Du, Qianqian Xiong, Kun Wang, Jie Tian. Radiomics improved pre-therapeutic prediction of breast cancers insensitive to neoadjuvant chemotherapy [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-10-29.