Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy
Objectives: Breast cancers show different regression patterns after neoadjuvant chemotherapy. Certain regression patterns are associated with more reliable margins in breast-conserving surgery. Our study aims to establish a nomogram based on radiomic features and clinicopathological factors to predi...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-11-01
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| Series: | Translational Oncology |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1936523320303235 |
| _version_ | 1818435774376312832 |
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| author | Xiaosheng Zhuang Chi Chen Zhenyu Liu Liulu Zhang Xuezhi Zhou Minyi Cheng Fei Ji Teng Zhu Chuqian Lei Junsheng Zhang Jingying Jiang Jie Tian Kun Wang |
| author_facet | Xiaosheng Zhuang Chi Chen Zhenyu Liu Liulu Zhang Xuezhi Zhou Minyi Cheng Fei Ji Teng Zhu Chuqian Lei Junsheng Zhang Jingying Jiang Jie Tian Kun Wang |
| author_sort | Xiaosheng Zhuang |
| collection | DOAJ |
| description | Objectives: Breast cancers show different regression patterns after neoadjuvant chemotherapy. Certain regression patterns are associated with more reliable margins in breast-conserving surgery. Our study aims to establish a nomogram based on radiomic features and clinicopathological factors to predict regression patterns in breast cancer patients. Methods: We retrospectively reviewed 144 breast cancer patients who received neoadjuvant chemotherapy and underwent definitive surgery in our center from January 2016 to December 2019. Tumor regression patterns were categorized as type 1 (concentric regression + pCR) and type 2 (multifocal residues + SD + PD) based on pathological results. We extracted 1158 multidimensional features from 2 sequences of MRI images. After feature selection, machine learning was applied to construct a radiomic signature. Clinical characteristics were selected by backward stepwise selection. The combined prediction model was built based on both the radiomic signature and clinical factors. The predictive performance of the combined prediction model was evaluated. Results: Two radiomic features were selected for constructing the radiomic signature. Combined with two significant clinical characteristics, the combined prediction model showed excellent prediction performance, with an area under the receiver operating characteristic curve of 0.902 (95% confidence interval 0.8343–0.9701) in the primary cohort and 0.826 (95% confidence interval 0.6774–0.9753) in the validation cohort. Conclusions: Our study established a unique model combining a radiomic signature and clinicopathological factors to predict tumor regression patterns prior to the initiation of NAC. The early prediction of type 2 regression offers the opportunity to modify preoperative treatments or aids in determining surgical options. |
| first_indexed | 2024-12-14T16:58:14Z |
| format | Article |
| id | doaj.art-63851d0cbbb74601b9a8cc0a6a2d1a20 |
| institution | Directory Open Access Journal |
| issn | 1936-5233 |
| language | English |
| last_indexed | 2024-12-14T16:58:14Z |
| publishDate | 2020-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Translational Oncology |
| spelling | doaj.art-63851d0cbbb74601b9a8cc0a6a2d1a202022-12-21T22:53:54ZengElsevierTranslational Oncology1936-52332020-11-011311100831Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapyXiaosheng Zhuang0Chi Chen1Zhenyu Liu2Liulu Zhang3Xuezhi Zhou4Minyi Cheng5Fei Ji6Teng Zhu7Chuqian Lei8Junsheng Zhang9Jingying Jiang10Jie Tian11Kun Wang12Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Shantou University Medical College, Shantou 515041, Guangdong, ChinaSchool of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China; Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, ChinaKey Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Science, Beijing 100080, ChinaDepartment of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, ChinaKey Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, ChinaDepartment of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, ChinaDepartment of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, ChinaDepartment of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, ChinaDepartment of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; The Second School of Clinical Medicine, Southern Medical University, Guangzhou 510515, ChinaDepartment of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Shantou University Medical College, Shantou 515041, Guangdong, ChinaBeijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing 100191, China; Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing 100191, China; Correspondence to: J. Jiang, Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China.Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Science, Beijing 100080, China; Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing 100191, China; Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology, Beijing 100191, China; Correspondence to: J. Tian, Institute of Automation, Chinese Academy of Sciences, No. 95 Zhongguancun East Road, Haidian District, Beijing 100190, China.Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China; Correspondence to: K. Wang, Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106, Zhongshan ER Lu, Guangzhou 510080, China.Objectives: Breast cancers show different regression patterns after neoadjuvant chemotherapy. Certain regression patterns are associated with more reliable margins in breast-conserving surgery. Our study aims to establish a nomogram based on radiomic features and clinicopathological factors to predict regression patterns in breast cancer patients. Methods: We retrospectively reviewed 144 breast cancer patients who received neoadjuvant chemotherapy and underwent definitive surgery in our center from January 2016 to December 2019. Tumor regression patterns were categorized as type 1 (concentric regression + pCR) and type 2 (multifocal residues + SD + PD) based on pathological results. We extracted 1158 multidimensional features from 2 sequences of MRI images. After feature selection, machine learning was applied to construct a radiomic signature. Clinical characteristics were selected by backward stepwise selection. The combined prediction model was built based on both the radiomic signature and clinical factors. The predictive performance of the combined prediction model was evaluated. Results: Two radiomic features were selected for constructing the radiomic signature. Combined with two significant clinical characteristics, the combined prediction model showed excellent prediction performance, with an area under the receiver operating characteristic curve of 0.902 (95% confidence interval 0.8343–0.9701) in the primary cohort and 0.826 (95% confidence interval 0.6774–0.9753) in the validation cohort. Conclusions: Our study established a unique model combining a radiomic signature and clinicopathological factors to predict tumor regression patterns prior to the initiation of NAC. The early prediction of type 2 regression offers the opportunity to modify preoperative treatments or aids in determining surgical options.http://www.sciencedirect.com/science/article/pii/S1936523320303235 |
| spellingShingle | Xiaosheng Zhuang Chi Chen Zhenyu Liu Liulu Zhang Xuezhi Zhou Minyi Cheng Fei Ji Teng Zhu Chuqian Lei Junsheng Zhang Jingying Jiang Jie Tian Kun Wang Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy Translational Oncology |
| title | Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy |
| title_full | Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy |
| title_fullStr | Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy |
| title_full_unstemmed | Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy |
| title_short | Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy |
| title_sort | multiparametric mri based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy |
| url | http://www.sciencedirect.com/science/article/pii/S1936523320303235 |
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