Qianqian Xiong1,2, Xuezhi Zhou3, Zhenyu Liu4, Chuqian Lei1,2, Ciqiu Yang1, Mei Yang1, Liulu Zhang1, Teng Zhu1, Xiaosheng Zhuang1,5, Changhong Liang6, Zaiyi Liu6, Jie Tian7,8,9, Kun Wang10,11. 1. Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital and Guangdong Academy of Medical Sciences, Guangzhou, 510080, China. 2. The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China. 3. Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, 710126, Shaanxi, China. 4. Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, 100190, No. 95 Zhongguancun East Road, Beijing, China. 5. Shantou University Medical College, Shantou, 515041, Guangdong, China. 6. Department of Radiology, Guangdong Provincial People's Hospital & Guangdong Academy of Medical Sciences, Guangzhou, 510080, China. 7. Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, 710126, Shaanxi, China. jie.tian@ia.ac.cn. 8. Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, 100190, No. 95 Zhongguancun East Road, Beijing, China. jie.tian@ia.ac.cn. 9. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, 100191, Beijing, China. jie.tian@ia.ac.cn. 10. Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital and Guangdong Academy of Medical Sciences, Guangzhou, 510080, China. gzwangkun@126.com. 11. The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China. gzwangkun@126.com.
Abstract
PURPOSE: To evaluate the value of multiparametric magnetic resonance imaging (MRI) in pretreatment prediction of breast cancers insensitive to neoadjuvant chemotherapy (NAC). METHODS: A total of 125 breast cancer patients (63 in the primary cohort and 62 in the validation cohort) who underwent MRI before receiving NAC were enrolled. All patients received surgical resection, and Miller-Payne grading system was applied to assess the response to NAC. Grade 1-2 cases were classified as insensitive to NAC. We extracted 1941 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 selected by multivariable logistic regression. The performance of the combined model was assessed with the results of independent validation. RESULTS: Four features were selected for the construction of the radiomic signature based on the primary cohort. Combining with independent clinical factors, the combined prediction model for identifying the Grade 1-2 group reached a better discrimination power than the radiomic signature, with an area under the receiver operating characteristic curve of 0.935 (95% confidence interval 0.848-1) in the validation cohort, and its clinical utility was confirmed by the decision curve analysis. CONCLUSION: The combined model based on radiomics and clinical variables has potential in predicting drug-insensitive breast cancers.
PURPOSE: To evaluate the value of multiparametric magnetic resonance imaging (MRI) in pretreatment prediction of breast cancers insensitive to neoadjuvant chemotherapy (NAC). METHODS: A total of 125 breast cancerpatients (63 in the primary cohort and 62 in the validation cohort) who underwent MRI before receiving NAC were enrolled. All patients received surgical resection, and Miller-Payne grading system was applied to assess the response to NAC. Grade 1-2 cases were classified as insensitive to NAC. We extracted 1941 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 selected by multivariable logistic regression. The performance of the combined model was assessed with the results of independent validation. RESULTS: Four features were selected for the construction of the radiomic signature based on the primary cohort. Combining with independent clinical factors, the combined prediction model for identifying the Grade 1-2 group reached a better discrimination power than the radiomic signature, with an area under the receiver operating characteristic curve of 0.935 (95% confidence interval 0.848-1) in the validation cohort, and its clinical utility was confirmed by the decision curve analysis. CONCLUSION: The combined model based on radiomics and clinical variables has potential in predicting drug-insensitive breast cancers.
Entities:
Keywords:
Breast cancer; Insensitive; MRI; Neoadjuvant chemotherapy; Radiomics
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