IEEE Access (Jan 2019)
Accurate Prediction of Neoadjuvant Chemotherapy Pathological Complete Remission (pCR) for the Four Sub-Types of Breast Cancer
- Xin Feng,
- Lelian Song,
- Shaofei Wang,
- Haoqiu Song,
- Hang Chen,
- Yuxuan Liu,
- Chenwei Lou,
- Jian Zhao,
- Quewang Liu,
- Yang Liu,
- Ruixue Zhao,
- Kai Xing,
- Sijie Li,
- Yunhe Yu,
- Zhenyu Liu,
- Chengyang Yin,
- Bing Han,
- Ye Du,
- Ruihao Xin,
- Lan Huang,
- Zhimin Fan,
- Fengfeng Zhou
Affiliations
- Xin Feng
- ORCiD
- Cancer Systems Biology Center, China–Japan Union Hospital, Jilin University, Changchun, China
- Lelian Song
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, China
- Shaofei Wang
- Key Laboratory of Symbolic Computation and Knowledge Engineering, BioKnow Health Informatics Laboratory, College of Computer Science and Technology, Ministry of Education, Jilin University, Changchun, China
- Haoqiu Song
- Cancer Systems Biology Center, China–Japan Union Hospital, Jilin University, Changchun, China
- Hang Chen
- Key Laboratory of Symbolic Computation and Knowledge Engineering, BioKnow Health Informatics Laboratory, College of Computer Science and Technology, Ministry of Education, Jilin University, Changchun, China
- Yuxuan Liu
- Cancer Systems Biology Center, China–Japan Union Hospital, Jilin University, Changchun, China
- Chenwei Lou
- Key Laboratory of Symbolic Computation and Knowledge Engineering, BioKnow Health Informatics Laboratory, College of Computer Science and Technology, Ministry of Education, Jilin University, Changchun, China
- Jian Zhao
- Key Laboratory of Symbolic Computation and Knowledge Engineering, BioKnow Health Informatics Laboratory, College of Computer Science and Technology, Ministry of Education, Jilin University, Changchun, China
- Quewang Liu
- Key Laboratory of Symbolic Computation and Knowledge Engineering, BioKnow Health Informatics Laboratory, College of Computer Science and Technology, Ministry of Education, Jilin University, Changchun, China
- Yang Liu
- Key Laboratory of Symbolic Computation and Knowledge Engineering, BioKnow Health Informatics Laboratory, College of Computer Science and Technology, Ministry of Education, Jilin University, Changchun, China
- Ruixue Zhao
- Key Laboratory of Symbolic Computation and Knowledge Engineering, BioKnow Health Informatics Laboratory, College of Computer Science and Technology, Ministry of Education, Jilin University, Changchun, China
- Kai Xing
- Key Laboratory of Symbolic Computation and Knowledge Engineering, BioKnow Health Informatics Laboratory, College of Computer Science and Technology, Ministry of Education, Jilin University, Changchun, China
- Sijie Li
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, China
- Yunhe Yu
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, China
- Zhenyu Liu
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, China
- Chengyang Yin
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, China
- Bing Han
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, China
- Ye Du
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, China
- Ruihao Xin
- College of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, China
- Lan Huang
- Key Laboratory of Symbolic Computation and Knowledge Engineering, BioKnow Health Informatics Laboratory, College of Computer Science and Technology, Ministry of Education, Jilin University, Changchun, China
- Zhimin Fan
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, China
- Fengfeng Zhou
- ORCiD
- Key Laboratory of Symbolic Computation and Knowledge Engineering, BioKnow Health Informatics Laboratory, College of Computer Science and Technology, Ministry of Education, Jilin University, Changchun, China
- DOI
- https://doi.org/10.1109/ACCESS.2019.2941543
- Journal volume & issue
-
Vol. 7
pp. 134697 – 134706
Abstract
Neoadjuvant chemotherapy (NAC) has become the main treatment option for breast cancer. Its adverse drug reactions (ADRs) make NAC painful both physiologically and psychologically. The factor pathological complete remission (pCR) describes how well a series of six or more chemotherapeutic treatments works on a patient. This study investigated the possibility of predicting pCR using only the nodal sizes of the first three treatments. A best feature combination for each breast cancer subtype was screened from the real nodal sizes of the first three treatments and the nodal sizes` of the next three treatments predicted from those of the first three ones. The prediction was evaluated by the metrics Avc = (sensitivity + specificity)/2. A triple-negative breast cancer (TN) patient may have an estimation of pCR Avc = 0.8696 after taking just three treatments. At least Avc = 0.7594 was achieved for all the four breast cancer subtypes investigated in this study.
Keywords
- Pathological complete response (pCR)
- breast cancer
- neoadjuvant chemotherapy
- biomarker detection
- feature selection
